Ginette Lafit (ginette.lafit@kuleuven.be) 26 april, 2022
- 1 Preliminaries
- 2 Data preprocessing for single intensive longitudinal analyses
- 3 Visulations and descriptive statistics
- 4 Data preprocessing for dyadic intensive longitudinal analyses
- 5 Create person-mean centered time-varying predictors
- 6 Estimate linear models for average of person-level data
(cross-sectional
data)
- 6.1 Estimate linear model for male partners
- 6.2 Estimate linear model for male partners with moderation effects
- 6.3 Estimate APIM for distinguishable partners
- 6.4 Estimate APIM for indistinguishable partners
- 6.5 Estimate APIM for distinguishable partners with moderation effects
- 6.6 Estimate APIM for indistinguishable partners with moderation effects
- 7 Estimate linear models for a single person (time series data)
- 8 Estimate linear mixed-effect models for single persons
design
- 8.1 Linear mixed-effects model to estimate the effect of enacted response on happy assuming Level 1 errors are independent
- 8.2 Linear mixed-effects model to estimate the effect of enacted response on happy assuming Level 1 errors follow an Autorregressive (AR(1)) model
- 8.3 Linear mixed-effects model to estimate the effect of enacted response on happy assuming Level 1 errors are independent including moderation effects
- 8.4 Linear mixed-effects model to estimate the autoregressive effect of happiness
- 9 Estimate longitudinal actor and partner interdependence model (L-APIM)
- 10 Estimate L-APIM with moderation effects
- 11 Estimate vector autoregressive model (VAR(1)) for dyadic partners
- 12 References
# This code chunk simply makes sure that all the
# libraries used here are installed.
packages <- c("nlme", "tidyverse", "foreign", "data.table", "ggeffects", "psych", "sjPlot", "sjmisc", "sjlabelled")
if ( length(missing_pkgs <- setdiff(packages,
rownames(installed.packages()))) > 0) {
message("Installing missing package(s): ",
paste(missing_pkgs, collapse = ", "))
install.packages(missing_pkgs)
}library(nlme) # to estimate linear-mixed effect models
library(tidyverse) # reshaped data and variable transformation## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::collapse() masks nlme::collapse()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(foreign) # load .sav data set
library(data.table) # to create lagged outcome##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following object is masked from 'package:purrr':
##
## transpose
library(ggeffects) # to create plots for the estimated effects
library(psych) # to compute descriptive statistics & some psychometrics##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(sjPlot) # to create html tables## Registered S3 method overwritten by 'parameters':
## method from
## format.parameters_distribution datawizard
library(sjmisc) # to create html tables##
## Attaching package: 'sjmisc'
## The following object is masked from 'package:purrr':
##
## is_empty
## The following object is masked from 'package:tidyr':
##
## replace_na
## The following object is masked from 'package:tibble':
##
## add_case
library(sjlabelled) # to create html tables##
## Attaching package: 'sjlabelled'
## The following object is masked from 'package:forcats':
##
## as_factor
## The following object is masked from 'package:dplyr':
##
## as_label
## The following object is masked from 'package:ggplot2':
##
## as_label
set.seed(1235) # Set a seed to reproduce analyses
# Get the session info (also for reproducibility)
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Dutch_Belgium.1252 LC_CTYPE=Dutch_Belgium.1252
## [3] LC_MONETARY=Dutch_Belgium.1252 LC_NUMERIC=C
## [5] LC_TIME=Dutch_Belgium.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] sjlabelled_1.1.8 sjmisc_2.8.9 sjPlot_2.8.10 psych_2.1.9
## [5] ggeffects_1.1.1 data.table_1.14.2 foreign_0.8-81 forcats_0.5.1
## [9] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4 readr_2.1.2
## [13] tidyr_1.2.0 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
## [17] nlme_3.1-155
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.2 lubridate_1.8.0 insight_0.17.0 httr_1.4.2
## [5] tools_4.1.2 backports_1.4.1 utf8_1.2.2 R6_2.5.1
## [9] DBI_1.1.2 colorspace_2.0-3 withr_2.5.0 tidyselect_1.1.2
## [13] mnormt_2.0.2 emmeans_1.7.3 compiler_4.1.2 performance_0.9.0
## [17] cli_3.2.0 rvest_1.0.2 xml2_1.3.3 sandwich_3.0-1
## [21] bayestestR_0.11.5 scales_1.1.1 mvtnorm_1.1-3 digest_0.6.29
## [25] minqa_1.2.4 rmarkdown_2.12 pkgconfig_2.0.3 htmltools_0.5.2
## [29] lme4_1.1-28 dbplyr_2.1.1 fastmap_1.1.0 rlang_1.0.1
## [33] readxl_1.3.1 rstudioapi_0.13 generics_0.1.2 zoo_1.8-9
## [37] jsonlite_1.8.0 magrittr_2.0.2 parameters_0.17.0 Matrix_1.3-4
## [41] Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2 lifecycle_1.0.1
## [45] stringi_1.7.6 multcomp_1.4-18 yaml_2.3.5 MASS_7.3-54
## [49] grid_4.1.2 parallel_4.1.2 crayon_1.5.0 lattice_0.20-45
## [53] haven_2.4.3 splines_4.1.2 sjstats_0.18.1 hms_1.1.1
## [57] tmvnsim_1.0-2 knitr_1.37 pillar_1.7.0 boot_1.3-28
## [61] estimability_1.3 effectsize_0.6.0.1 codetools_0.2-18 reprex_2.0.1
## [65] glue_1.6.2 evaluate_0.15 modelr_0.1.8 nloptr_2.0.0
## [69] vctrs_0.3.8 tzdb_0.2.0 cellranger_1.1.0 gtable_0.3.0
## [73] assertthat_0.2.1 datawizard_0.4.0 xfun_0.30 xtable_1.8-4
## [77] broom_0.7.12 coda_0.19-4 survival_3.2-13 TH.data_1.1-0
## [81] ellipsis_0.3.2
We use the data from The Dyadic Interaction Study used in Sels, Ceulemans, and Kuppens (2019). The Dyadic Interaction Study includes 101 couples that self-identified as heterosexual which were in a relationship for at least 2 months and of which both partners were over the age of 18. Participants were recruited in the context of a larger study on emotion dynamics in intimate relationships, from which only the ESM part is relevant to this study. Participants were on average 26 years old (SD = 5 years, Min = 18, Max = 53), and had been in a relationship for 4.5 years (SD = 2.8, min = 7 months, max = 21 years). The majority of these couples were living together (n = 96) and did not have children yet (n = 5). The nationality of most participants was Belgian (n = 187). The other participants had a Dutch (n = 9), German (n = 3), Armenian (n = 1), Chinese (n = 1), or Ukrainian nationality (n = 1). Among half of the participants (n = 102) had a University degree, one-fourth had completed higher education (n = 43), and the remainder had a primary school (n = 1) or secondary school education level (n = 56). Participants were recruited through social media platforms, and flyers and posters that were distributed in public places in Leuven, Belgium. The study was approved by the ethics committee of the Faculty of Psychology and Educational Sciences of the KU Leuven.
During the ESM period of 7 days, each partner reported on their feelings and other experiences several times a day. Specifically, partners reported whether they were together at a given moment (resulting in a dyad level Presence of the Partner variable when one of the partners said yes), how happy they were, and how much they had tried to make their partner feel understood and appreciated (i.e., enacted responsiveness). While the first item was dichotomous (0 = no, 1 = yes), the other items were answered by a sliding scale ranging from ‘not at all’ (0) to ‘very much’ (100). Both partners were considered together when one of the partners said so. Partners were prompted simultaneously, but the items were ordered randomly to avoid cooperation. During weekdays, partners were prompted 6 times a day, from 5 PM until 10 PM. On weekend days, partners were assessed 14 times a day, from 10 AM until 10 PM. These time spans were selected because partners were more likely to be together during these hours. Each time span was divided into equal intervals, and each signal was programmed randomly in each interval. Participants received a minimum of 47 and a maximum of 72 beeps. Participants for which data were missing due to practical and technical issues were excluded. The final sample includes 94 heterosexual couples.
We first load the data set including the baseline questionnaire.
# Load in background (or baseline) questionnaires
data.baseline = read.csv("background.csv", header = TRUE, sep = ';', dec = ".", stringsAsFactors = FALSE)
# Re-name dyad identification number
names(data.baseline)[names(data.baseline) == "ï..couple"] = "couple"
# Create variable Couple Satisfaction
data.baseline$couple_satisf = rowMeans(data.baseline[,c("PRQCI_satisfaction1","PRQCI_satisfaction2","PRQCI_satisfaction3" )], na.rm = TRUE)
# Change the level of the variable sex (F = vrouw, M = man)
data.baseline$sex = ifelse(data.baseline$sex == "vrouw", "F", "M")
# Find the dimensions
dim(data.baseline)## [1] 202 180
# Find the structure
str(data.baseline)## 'data.frame': 202 obs. of 180 variables:
## $ couple : int 1 1 2 2 3 3 4 4 5 5 ...
## $ starttijd : chr "3/1/2016" "2/29/2016" "3/2/2016" "3/3/2016" ...
## $ informed_consent : chr "akkoord" "akkoord" "akkoord" "akkoord" ...
## $ sex : chr "M" "F" "M" "F" ...
## $ age : int 24 24 20 20 20 19 20 20 22 22 ...
## $ nationality : chr "Belg" "belg" "belg" "Belg" ...
## $ education : chr "universitair onderwijs" "middelbaar" "middelbaar" "universitair onderwijs" ...
## $ profession : chr "Werkzoekend" "student" "student" "Student" ...
## $ breaks : chr "nee" "nee" "nee" "nee" ...
## $ children : chr "nee" "nee" "nee" "nee" ...
## $ total_children : int NA NA NA NA NA NA NA NA NA NA ...
## $ status_rel : chr "niet-samenwonend" "niet-samenwonend" "niet-samenwonend" "niet-samenwonend" ...
## $ hours_together : int 16 24 20 10 20 35 56 35 40 28 ...
## $ sit : chr "ja" "nee" "nee" "ja" ...
## $ cont_sit : chr "relatieproblemen" " " " " "ziekte/overlijden van iemand in naaste omgeving" ...
## $ sev_sit : int 4 NA NA 7 9 7 NA NA 8 NA ...
## $ PRQCI_satisfaction1 : int 5 5 6 6 5 4 5 6 5 6 ...
## $ PRQCI_satisfaction2 : int 5 5 6 6 6 5 5 7 6 6 ...
## $ PRQCI_satisfaction3 : int 5 5 6 6 6 6 5 6 6 7 ...
## $ PRQCI_commitment1 : int 5 6 7 7 6 7 4 7 6 7 ...
## $ PRQCI_commitment2 : int 4 6 5 6 5 6 5 7 6 7 ...
## $ PRQCI_commitment3 : int 4 4 5 7 6 7 5 6 6 7 ...
## $ PRQCI_intimacy1 : int 5 5 6 6 6 6 4 7 6 7 ...
## $ PRQCI_intimacy2 : int 5 6 6 6 6 6 6 7 6 7 ...
## $ PRQCI_intimacy3 : int 5 6 7 6 6 4 5 7 5 7 ...
## $ PRQCI_trust1 : int 7 6 7 7 6 6 7 7 7 6 ...
## $ PRQCI_trust2 : int 5 7 7 6 4 5 7 7 6 7 ...
## $ PRQCI_trust3 : int 7 6 7 7 7 6 7 7 7 6 ...
## $ PRQCI_passion1 : int 5 5 5 5 3 3 4 5 5 5 ...
## $ PRQCI_passion2 : int 4 5 6 6 2 3 4 5 5 6 ...
## $ PRQCI_passion3 : int 4 6 6 6 2 4 5 5 5 5 ...
## $ PRQCI_love1 : int 5 5 7 7 6 7 6 7 7 7 ...
## $ PRQCI_love2 : int 5 6 6 7 6 5 5 7 7 7 ...
## $ PRQCI_love3 : int 5 5 6 6 5 7 6 7 6 7 ...
## $ KMS_1 : int 6 5 6 6 6 5 5 6 6 6 ...
## $ KMS_2 : int 6 6 6 7 5 6 5 7 6 6 ...
## $ KMS_3 : int 6 6 6 6 6 5 5 6 6 7 ...
## $ rel_interdependence : int 4 6 6 5 5 6 6 7 4 5 ...
## $ com1 : int 7 6 7 8 8 7 8 9 8 9 ...
## $ com2 : int 7 6 9 6 8 9 7 9 8 9 ...
## $ com3_R : int 4 4 1 8 2 1 4 1 2 2 ...
## $ com4_R : int 2 3 2 2 6 9 1 1 2 2 ...
## $ com5 : int 6 6 8 8 7 8 6 9 7 8 ...
## $ com6 : int 7 5 5 7 7 5 7 9 7 8 ...
## $ com7 : int 6 7 7 7 7 7 7 9 5 8 ...
## $ ICQ_NA1 : int 2 4 4 4 3 3 3 3 3 4 ...
## $ ICQ_NA2 : int 3 4 3 4 2 4 2 3 3 3 ...
## $ ICQ_NA3 : int 3 4 3 4 3 4 2 2 3 4 ...
## $ ICQ_ES1 : int 2 3 4 4 3 3 2 3 3 3 ...
## $ ICQ_ES2 : int 2 3 3 4 3 2 3 2 4 3 ...
## $ ICQ_ES3 : int 3 3 4 3 3 3 2 3 3 3 ...
## $ ICQ_D1 : int 3 4 4 4 4 4 2 3 3 4 ...
## $ ICQ_D2 : int 3 4 4 4 4 3 1 3 3 4 ...
## $ ICQ_D3 : int 4 4 4 3 2 3 2 3 3 4 ...
## $ ICQ_CM1 : int 2 2 3 2 2 3 4 1 2 2 ...
## $ ICQ_CM2 : int 2 3 3 3 4 4 3 2 2 3 ...
## $ ICQ_CM3 : int 2 3 1 1 4 1 4 1 3 2 ...
## $ attachavoid1_R : int 5 6 7 7 5 5 5 6 5 7 ...
## $ attachanx1 : int 2 5 6 5 3 7 2 4 2 5 ...
## $ attachavoid2 : int 1 5 1 2 4 6 1 4 2 2 ...
## $ attachanx2 : int 3 2 1 4 1 7 6 5 1 2 ...
## $ attachavoid3_R : int 5 7 7 6 6 7 4 6 5 6 ...
## $ attachanx3 : int 3 3 1 3 4 7 1 4 1 3 ...
## $ attachavoid4 : int 2 5 2 5 5 6 5 5 4 1 ...
## $ attachanx4_R : int 2 4 6 5 6 1 5 3 7 2 ...
## $ attachavoid5_R : int 5 7 6 7 5 7 4 6 5 7 ...
## $ attachanx5 : int 2 7 3 5 3 6 1 7 2 6 ...
## $ attachavoid6 : int 5 6 3 2 2 7 3 4 4 2 ...
## $ attachanx6 : int 2 3 1 5 1 7 2 2 2 2 ...
## $ CESD1 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD2 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD3 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD4_R : chr "meestal of altijd (5 tot 7 dagen)" "meestal of altijd (5 tot 7 dagen)" "meestal of altijd (5 tot 7 dagen)" "regelmatig (3 tot 4 dagen)" ...
## $ CESD5 : chr "zelden of nooit (minder dan 1 dag)" "soms of weinig (1 tot 2 dagen)" "zelden of nooit (minder dan 1 dag)" "regelmatig (3 tot 4 dagen)" ...
## $ CESD6 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD7 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD8_R : chr "regelmatig (3 tot 4 dagen)" "regelmatig (3 tot 4 dagen)" "meestal of altijd (5 tot 7 dagen)" "meestal of altijd (5 tot 7 dagen)" ...
## $ CESD9 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD10 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD11 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD12_R : chr "regelmatig (3 tot 4 dagen)" "meestal of altijd (5 tot 7 dagen)" "meestal of altijd (5 tot 7 dagen)" "meestal of altijd (5 tot 7 dagen)" ...
## $ CESD13 : chr "soms of weinig (1 tot 2 dagen)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "soms of weinig (1 tot 2 dagen)" ...
## $ CESD14 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD15 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD16_R : chr "regelmatig (3 tot 4 dagen)" "regelmatig (3 tot 4 dagen)" "meestal of altijd (5 tot 7 dagen)" "meestal of altijd (5 tot 7 dagen)" ...
## $ CESD17 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD18 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "soms of weinig (1 tot 2 dagen)" ...
## $ CESD19 : chr "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" "zelden of nooit (minder dan 1 dag)" ...
## $ CESD20 : chr "zelden of nooit (minder dan 1 dag)" "soms of weinig (1 tot 2 dagen)" "zelden of nooit (minder dan 1 dag)" "soms of weinig (1 tot 2 dagen)" ...
## $ EC1 : int 2 3 3 4 3 3 2 3 2 3 ...
## $ EC2_R : int 1 0 0 0 3 1 1 0 1 1 ...
## $ EC3 : int 2 4 4 4 3 4 3 4 3 4 ...
## $ EC4_R : int 2 1 1 1 1 1 1 1 1 1 ...
## $ EC5_R : int 1 1 0 0 3 0 1 1 1 0 ...
## $ EC6 : int 1 2 2 1 3 4 1 2 2 3 ...
## $ EC7 : int 2 2 2 3 2 2 3 3 3 3 ...
## $ PT1 : int 2 3 3 2 2 3 3 2 1 3 ...
## $ PT2 : int 2 2 3 3 3 4 3 3 2 3 ...
## $ PT3_R : int 3 1 1 3 1 1 1 1 4 1 ...
## [list output truncated]
# See the first 6 rows
head(data.baseline)## couple starttijd informed_consent sex age nationality education
## 1 1 3/1/2016 akkoord M 24 Belg universitair onderwijs
## 2 1 2/29/2016 akkoord F 24 belg middelbaar
## 3 2 3/2/2016 akkoord M 20 belg middelbaar
## 4 2 3/3/2016 akkoord F 20 Belg universitair onderwijs
## 5 3 3/5/2016 akkoord M 20 Belg middelbaar
## 6 3 3/5/2016 akkoord F 19 Belg middelbaar
## profession breaks children total_children status_rel hours_together
## 1 Werkzoekend nee nee NA niet-samenwonend 16
## 2 student nee nee NA niet-samenwonend 24
## 3 student nee nee NA niet-samenwonend 20
## 4 Student nee nee NA niet-samenwonend 10
## 5 Student nee nee NA niet-samenwonend 20
## 6 Student nee nee NA niet-samenwonend 35
## sit
## 1 ja
## 2 nee
## 3 nee
## 4 ja
## 5 ja
## 6 ja
## cont_sit
## 1 relatieproblemen
## 2
## 3
## 4 ziekte/overlijden van iemand in naaste omgeving
## 5 niet nagekomen belofte:ziekte/ongeval van 1 van de partners:relatieproblemen:andere
## 6 samenwonen:andere
## sev_sit PRQCI_satisfaction1 PRQCI_satisfaction2 PRQCI_satisfaction3
## 1 4 5 5 5
## 2 NA 5 5 5
## 3 NA 6 6 6
## 4 7 6 6 6
## 5 9 5 6 6
## 6 7 4 5 6
## PRQCI_commitment1 PRQCI_commitment2 PRQCI_commitment3 PRQCI_intimacy1
## 1 5 4 4 5
## 2 6 6 4 5
## 3 7 5 5 6
## 4 7 6 7 6
## 5 6 5 6 6
## 6 7 6 7 6
## PRQCI_intimacy2 PRQCI_intimacy3 PRQCI_trust1 PRQCI_trust2 PRQCI_trust3
## 1 5 5 7 5 7
## 2 6 6 6 7 6
## 3 6 7 7 7 7
## 4 6 6 7 6 7
## 5 6 6 6 4 7
## 6 6 4 6 5 6
## PRQCI_passion1 PRQCI_passion2 PRQCI_passion3 PRQCI_love1 PRQCI_love2
## 1 5 4 4 5 5
## 2 5 5 6 5 6
## 3 5 6 6 7 6
## 4 5 6 6 7 7
## 5 3 2 2 6 6
## 6 3 3 4 7 5
## PRQCI_love3 KMS_1 KMS_2 KMS_3 rel_interdependence com1 com2 com3_R com4_R
## 1 5 6 6 6 4 7 7 4 2
## 2 5 5 6 6 6 6 6 4 3
## 3 6 6 6 6 6 7 9 1 2
## 4 6 6 7 6 5 8 6 8 2
## 5 5 6 5 6 5 8 8 2 6
## 6 7 5 6 5 6 7 9 1 9
## com5 com6 com7 ICQ_NA1 ICQ_NA2 ICQ_NA3 ICQ_ES1 ICQ_ES2 ICQ_ES3 ICQ_D1 ICQ_D2
## 1 6 7 6 2 3 3 2 2 3 3 3
## 2 6 5 7 4 4 4 3 3 3 4 4
## 3 8 5 7 4 3 3 4 3 4 4 4
## 4 8 7 7 4 4 4 4 4 3 4 4
## 5 7 7 7 3 2 3 3 3 3 4 4
## 6 8 5 7 3 4 4 3 2 3 4 3
## ICQ_D3 ICQ_CM1 ICQ_CM2 ICQ_CM3 attachavoid1_R attachanx1 attachavoid2
## 1 4 2 2 2 5 2 1
## 2 4 2 3 3 6 5 5
## 3 4 3 3 1 7 6 1
## 4 3 2 3 1 7 5 2
## 5 2 2 4 4 5 3 4
## 6 3 3 4 1 5 7 6
## attachanx2 attachavoid3_R attachanx3 attachavoid4 attachanx4_R attachavoid5_R
## 1 3 5 3 2 2 5
## 2 2 7 3 5 4 7
## 3 1 7 1 2 6 6
## 4 4 6 3 5 5 7
## 5 1 6 4 5 6 5
## 6 7 7 7 6 1 7
## attachanx5 attachavoid6 attachanx6 CESD1
## 1 2 5 2 zelden of nooit (minder dan 1 dag)
## 2 7 6 3 zelden of nooit (minder dan 1 dag)
## 3 3 3 1 zelden of nooit (minder dan 1 dag)
## 4 5 2 5 zelden of nooit (minder dan 1 dag)
## 5 3 2 1 soms of weinig (1 tot 2 dagen)
## 6 6 7 7 soms of weinig (1 tot 2 dagen)
## CESD2 CESD3
## 1 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 2 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 3 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 4 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 5 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 6 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## CESD4_R CESD5
## 1 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 2 meestal of altijd (5 tot 7 dagen) soms of weinig (1 tot 2 dagen)
## 3 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 4 regelmatig (3 tot 4 dagen) regelmatig (3 tot 4 dagen)
## 5 meestal of altijd (5 tot 7 dagen) soms of weinig (1 tot 2 dagen)
## 6 regelmatig (3 tot 4 dagen) soms of weinig (1 tot 2 dagen)
## CESD6 CESD7
## 1 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 2 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 3 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 4 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 5 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 6 regelmatig (3 tot 4 dagen) soms of weinig (1 tot 2 dagen)
## CESD8_R CESD9
## 1 regelmatig (3 tot 4 dagen) zelden of nooit (minder dan 1 dag)
## 2 regelmatig (3 tot 4 dagen) zelden of nooit (minder dan 1 dag)
## 3 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 4 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 5 regelmatig (3 tot 4 dagen) zelden of nooit (minder dan 1 dag)
## 6 regelmatig (3 tot 4 dagen) zelden of nooit (minder dan 1 dag)
## CESD10 CESD11
## 1 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 2 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 3 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 4 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 5 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 6 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## CESD12_R CESD13
## 1 regelmatig (3 tot 4 dagen) soms of weinig (1 tot 2 dagen)
## 2 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 3 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 4 meestal of altijd (5 tot 7 dagen) soms of weinig (1 tot 2 dagen)
## 5 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 6 soms of weinig (1 tot 2 dagen) soms of weinig (1 tot 2 dagen)
## CESD14 CESD15
## 1 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 2 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 3 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 4 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 5 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 6 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## CESD16_R CESD17
## 1 regelmatig (3 tot 4 dagen) zelden of nooit (minder dan 1 dag)
## 2 regelmatig (3 tot 4 dagen) zelden of nooit (minder dan 1 dag)
## 3 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 4 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 5 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 6 soms of weinig (1 tot 2 dagen) soms of weinig (1 tot 2 dagen)
## CESD18 CESD19
## 1 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 2 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 3 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 4 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 5 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 6 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## CESD20 EC1 EC2_R EC3 EC4_R EC5_R EC6 EC7 PT1 PT2
## 1 zelden of nooit (minder dan 1 dag) 2 1 2 2 1 1 2 2 2
## 2 soms of weinig (1 tot 2 dagen) 3 0 4 1 1 2 2 3 2
## 3 zelden of nooit (minder dan 1 dag) 3 0 4 1 0 2 2 3 3
## 4 soms of weinig (1 tot 2 dagen) 4 0 4 1 0 1 3 2 3
## 5 zelden of nooit (minder dan 1 dag) 3 3 3 1 3 3 2 2 3
## 6 regelmatig (3 tot 4 dagen) 3 1 4 1 0 4 2 3 4
## PT3_R PT4 PT5 PT6 SWL1 SWL2 SWL3 SWL4 SWL5 dci7_NC dci10_NC dci11_NC dci15_NC
## 1 3 2 1 2 7 6 6 7 6 2 2 3 3
## 2 1 2 1 1 5 5 6 5 5 2 3 3 2
## 3 1 2 1 3 6 7 7 5 6 1 1 1 1
## 4 3 2 1 2 4 6 6 5 5 3 2 2 2
## 5 1 4 3 1 5 7 6 6 4 1 1 1 4
## 6 1 3 2 0 3 2 4 2 2 3 4 5 4
## dci22_NC dci25_NC dci26_NC dci27_NC RS1 RS1_w RS1_e RS2 RS2_w RS2_e RS3 RS3_w
## 1 2 2 3 2 1 3 5 1 2 5 1 2
## 2 1 1 2 2 1 3 5 1 4 4 1 2
## 3 1 2 1 1 1 4 3 1 4 5 1 2
## 4 1 2 1 2 1 2 5 1 4 4 1 1
## 5 1 1 3 3 1 3 4 1 2 5 1 1
## 6 1 1 3 2 1 2 6 1 5 5 1 1
## RS3_e RJS4 RS4_w RS4_e RS5 RS5_w RS5_e RS6 RS6_w RS6_e RS7 RS7_w RS7_e RS8
## 1 6 1 2 4 1 3 4 1 4 4 1 2 5 1
## 2 5 1 3 5 1 3 4 1 1 6 1 2 5 1
## 3 6 1 2 6 1 2 6 1 1 6 1 2 4 1
## 4 6 1 2 5 1 1 5 1 1 6 1 2 5 1
## 5 5 1 3 4 1 2 5 1 1 5 1 2 4 1
## 6 6 1 6 4 1 6 2 1 4 5 1 6 2 1
## RS8_w RS8_e RS9 RS9_w RS9_e clarity_5_R clarity_6 clarity_11_R clarity_13_R
## 1 2 4 1 2 5 3 3 3 1
## 2 4 4 1 1 5 4 2 3 4
## 3 4 4 1 1 6 1 4 1 3
## 4 5 5 1 1 6 4 3 2 3
## 5 2 4 1 1 5 4 4 2 3
## 6 6 3 1 5 2 5 1 4 5
## clarity_14 clarity_15 clarity_19 clarity_21_R clarity_25 clarity_28
## 1 3 3 4 3 3 3
## 2 5 4 2 3 2 4
## 3 3 2 5 1 3 4
## 4 4 2 3 2 4 4
## 5 4 2 4 3 4 4
## 6 2 5 1 5 1 2
## clarity_30 expressive1 expressive2_R expressive3 maudsley_1 maudsley_2
## 1 3 3 4 3 NA NA
## 2 3 5 2 4 NA NA
## 3 4 4 2 5 NA NA
## 4 4 4 2 4 NA NA
## 5 3 4 3 4 NA NA
## 6 1 4 2 3 NA NA
## maudsley_3 maudsley_4 maudsley_5 aspectenonderzoek begeleidingvan
## 1 NA NA NA volledig Laura
## 2 NA NA NA volledig Laura
## 3 NA NA NA labsessie Laura
## 4 NA NA NA labsessie Laura
## 5 NA NA NA labsessie Laura
## 6 NA NA NA labsessie Laura
## Opmerkingenoververloop
## 1 Pilotering- BIJ koppel klopt vraag "geef aan hoe je jezelf voelt mbt je partner" na negatieve conversatie & positieve conversatie niet , hebben nog lange vragenlijst gekregen
## 2 Pilotering- BIJ koppel klopt vraag "geef aan hoe je jezelf voelt mbt je partner" na negatieve conversatie & positieve conversatie niet , hebben nog lange vragenlijst gekregen
## 3 Pilotering-BIJ koppel klopt vraag "geef aan hoe je jezelf voelt mbt je partner" na negatieve conversatie & positieve conversatie niet , hebben nog lange vragenlijst gekregen
## 4 Pilotering-BIJ koppel klopt vraag "geef aan hoe je jezelf voelt mbt je partner" na negatieve conversatie & positieve conversatie niet , hebben nog lange vragenlijst gekregen
## 5 Pilotering-BIJ koppel klopt vraag "geef aan hoe je jezelf voelt mbt je partner" na negatieve conversatie & positieve conversatie niet , hebben nog lange vragenlijst gekregen, wonen eigenlijk gedurende de week samen, meisje gaat naar psycholoog
## 6 Pilotering-BIJ koppel klopt vraag "geef aan hoe je jezelf voelt mbt je partner" na negatieve conversatie & positieve conversatie niet , hebben nog lange vragenlijst gekregen, wonen eigenlijk gedurende de week samen, meisje gaat naar psycholoog
## male female ppnr relationship_duration total_months_together
## 1 1 0 701 2:02:00 26
## 2 0 1 1 2:02:00 26
## 3 1 0 702 1:09:00 21
## 4 0 1 2 1:09:00 21
## 5 1 0 703 1:07:00 19
## 6 0 1 3 1:07:00 19
## total_years_together partnumber call_status_rel Extra
## 1 2,16666666666667 1
## 2 2,16666666666667 2
## 3 1,75 1
## 4 1,75 2
## 5 1,58333333333333 1
## 6 1,58333333333333 2
## aangeduidinachtergrondvragenlijst new_status_rel PRQCI_commitment com3 com4
## 1 niet-samenwo 4,33333333333333 6 8
## 2 niet-samenwo 5,33333333333333 6 7
## 3 niet-samenwo 5,66666666666667 9 8
## 4 niet-samenwo 6,66666666666667 2 8
## 5 niet-samenwo 5,66666666666667 8 4
## 6 niet-samenwo 6,66666666666667 9 1
## RUScomm_total couple_satisf
## 1 6,71428571428571 5.000000
## 2 6,14285714285714 5.000000
## 3 7,57142857142857 6.000000
## 4 6,57142857142857 6.000000
## 5 7 5.666667
## 6 6,57142857142857 5.000000
# See the last 6 rows
tail(data.baseline)## couple starttijd informed_consent sex age nationality
## 197 115 8/10/2016 akkoord M 25 Belg
## 198 115 8/31/2016 akkoord F 26 Belg
## 199 116 8/31/2016 akkoord M 36 Belg
## 200 116 8/8/2016 akkoord F 33 Belg
## 201 117 8/9/2016 akkoord M 30 belg
## 202 117 8/11/2016 akkoord F 27 Belg
## education profession breaks children
## 197 universitair onderwijs Consultant nee nee
## 198 universitair onderwijs Leraar Nederlands-Frans-NT2 nee nee
## 199 universitair onderwijs Compliance Engineer nee nee
## 200 hoger onderwijs Geen nee nee
## 201 universitair onderwijs assistent geneeskunde nee nee
## 202 universitair onderwijs Huisarts nee nee
## total_children status_rel hours_together sit
## 197 NA samenwonend 40 nee
## 198 NA samenwonend 49 nee
## 199 NA getrouwd 70 nee
## 200 NA getrouwd 70 ja
## 201 NA niet-samenwonend 35 nee
## 202 NA samenwonend 30 nee
## cont_sit
## 197
## 198
## 199
## 200 verandering van werk van 1 van de partners:problemen met schoonouders:financiële problemen
## 201
## 202
## sev_sit PRQCI_satisfaction1 PRQCI_satisfaction2 PRQCI_satisfaction3
## 197 NA 6 6 7
## 198 NA 5 5 6
## 199 NA 7 6 7
## 200 10 6 6 6
## 201 NA 7 6 7
## 202 NA 6 6 6
## PRQCI_commitment1 PRQCI_commitment2 PRQCI_commitment3 PRQCI_intimacy1
## 197 6 6 6 5
## 198 6 5 4 5
## 199 7 7 7 7
## 200 7 7 7 7
## 201 6 6 6 7
## 202 6 6 6 6
## PRQCI_intimacy2 PRQCI_intimacy3 PRQCI_trust1 PRQCI_trust2 PRQCI_trust3
## 197 6 6 7 7 7
## 198 6 6 7 7 7
## 199 7 7 7 7 7
## 200 7 7 7 6 7
## 201 6 6 7 7 7
## 202 6 6 7 6 7
## PRQCI_passion1 PRQCI_passion2 PRQCI_passion3 PRQCI_love1 PRQCI_love2
## 197 4 4 4 7 6
## 198 4 5 4 5 5
## 199 4 5 2 7 7
## 200 4 5 5 7 7
## 201 6 6 6 7 6
## 202 6 6 6 6 6
## PRQCI_love3 KMS_1 KMS_2 KMS_3 rel_interdependence com1 com2 com3_R com4_R
## 197 6 6 6 6 7 8 8 2 1
## 198 7 6 6 6 6 9 8 2 1
## 199 7 6 7 6 7 9 9 1 1
## 200 7 6 6 6 6 9 9 1 1
## 201 7 6 7 7 6 9 9 1 2
## 202 6 6 6 6 6 8 8 1 2
## com5 com6 com7 ICQ_NA1 ICQ_NA2 ICQ_NA3 ICQ_ES1 ICQ_ES2 ICQ_ES3 ICQ_D1
## 197 8 8 8 2 2 3 3 3 4 4
## 198 7 8 8 3 3 3 2 3 2 4
## 199 9 9 9 1 1 1 4 4 4 4
## 200 9 9 9 3 3 3 4 4 4 4
## 201 8 7 8 1 3 2 3 3 3 4
## 202 8 8 8 3 3 3 3 3 3 4
## ICQ_D2 ICQ_D3 ICQ_CM1 ICQ_CM2 ICQ_CM3 attachavoid1_R attachanx1
## 197 4 3 3 4 4 5 4
## 198 2 3 3 3 2 7 3
## 199 3 4 2 4 3 7 7
## 200 3 3 2 4 1 7 7
## 201 3 3 2 2 3 6 6
## 202 4 4 3 3 2 6 5
## attachavoid2 attachanx2 attachavoid3_R attachanx3 attachavoid4 attachanx4_R
## 197 1 2 4 2 1 5
## 198 2 5 7 5 2 2
## 199 1 1 7 1 1 2
## 200 2 6 7 3 3 7
## 201 2 1 7 2 2 2
## 202 2 2 6 2 2 3
## attachavoid5_R attachanx5 attachavoid6 attachanx6
## 197 3 5 1 4
## 198 7 5 5 3
## 199 7 5 1 5
## 200 7 2 6 6
## 201 6 4 3 3
## 202 6 5 2 2
## CESD1 CESD2
## 197 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 198 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 199 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 200 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 201 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 202 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## CESD3 CESD4_R
## 197 zelden of nooit (minder dan 1 dag) meestal of altijd (5 tot 7 dagen)
## 198 zelden of nooit (minder dan 1 dag) meestal of altijd (5 tot 7 dagen)
## 199 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen)
## 200 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 201 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 202 zelden of nooit (minder dan 1 dag) meestal of altijd (5 tot 7 dagen)
## CESD5 CESD6
## 197 meestal of altijd (5 tot 7 dagen) soms of weinig (1 tot 2 dagen)
## 198 soms of weinig (1 tot 2 dagen) soms of weinig (1 tot 2 dagen)
## 199 regelmatig (3 tot 4 dagen) regelmatig (3 tot 4 dagen)
## 200 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 201 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 202 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## CESD7 CESD8_R
## 197 regelmatig (3 tot 4 dagen) regelmatig (3 tot 4 dagen)
## 198 soms of weinig (1 tot 2 dagen) regelmatig (3 tot 4 dagen)
## 199 meestal of altijd (5 tot 7 dagen) meestal of altijd (5 tot 7 dagen)
## 200 soms of weinig (1 tot 2 dagen) meestal of altijd (5 tot 7 dagen)
## 201 soms of weinig (1 tot 2 dagen) meestal of altijd (5 tot 7 dagen)
## 202 soms of weinig (1 tot 2 dagen) regelmatig (3 tot 4 dagen)
## CESD9 CESD10
## 197 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 198 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 199 soms of weinig (1 tot 2 dagen) regelmatig (3 tot 4 dagen)
## 200 meestal of altijd (5 tot 7 dagen) zelden of nooit (minder dan 1 dag)
## 201 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 202 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## CESD11 CESD12_R
## 197 regelmatig (3 tot 4 dagen) regelmatig (3 tot 4 dagen)
## 198 regelmatig (3 tot 4 dagen) meestal of altijd (5 tot 7 dagen)
## 199 soms of weinig (1 tot 2 dagen) regelmatig (3 tot 4 dagen)
## 200 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen)
## 201 zelden of nooit (minder dan 1 dag) meestal of altijd (5 tot 7 dagen)
## 202 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen)
## CESD13 CESD14
## 197 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 198 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 199 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 200 soms of weinig (1 tot 2 dagen) soms of weinig (1 tot 2 dagen)
## 201 soms of weinig (1 tot 2 dagen) zelden of nooit (minder dan 1 dag)
## 202 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## CESD15 CESD16_R
## 197 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen)
## 198 zelden of nooit (minder dan 1 dag) meestal of altijd (5 tot 7 dagen)
## 199 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 200 regelmatig (3 tot 4 dagen) regelmatig (3 tot 4 dagen)
## 201 zelden of nooit (minder dan 1 dag) meestal of altijd (5 tot 7 dagen)
## 202 soms of weinig (1 tot 2 dagen) regelmatig (3 tot 4 dagen)
## CESD17 CESD18
## 197 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 198 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 199 soms of weinig (1 tot 2 dagen) regelmatig (3 tot 4 dagen)
## 200 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## 201 zelden of nooit (minder dan 1 dag) zelden of nooit (minder dan 1 dag)
## 202 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen)
## CESD19 CESD20 EC1 EC2_R
## 197 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen) 2 0
## 198 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen) 3 1
## 199 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen) 4 0
## 200 soms of weinig (1 tot 2 dagen) soms of weinig (1 tot 2 dagen) 4 1
## 201 zelden of nooit (minder dan 1 dag) soms of weinig (1 tot 2 dagen) 3 1
## 202 zelden of nooit (minder dan 1 dag) regelmatig (3 tot 4 dagen) 4 1
## EC3 EC4_R EC5_R EC6 EC7 PT1 PT2 PT3_R PT4 PT5 PT6 SWL1 SWL2 SWL3 SWL4 SWL5
## 197 2 1 0 2 4 3 3 2 3 1 1 5 7 5 6 6
## 198 3 0 1 3 3 3 2 1 3 1 3 6 6 6 4 1
## 199 4 0 0 4 4 4 4 2 3 4 2 1 2 2 4 5
## 200 4 0 0 3 4 3 3 1 3 4 4 2 3 3 2 1
## 201 3 1 1 3 3 3 3 2 3 3 3 4 5 6 3 4
## 202 4 0 0 3 2 3 3 2 3 3 3 5 5 5 6 5
## dci7_NC dci10_NC dci11_NC dci15_NC dci22_NC dci25_NC dci26_NC dci27_NC RS1
## 197 1 1 2 1 1 1 2 1 1
## 198 1 2 3 3 1 1 3 3 1
## 199 5 4 1 4 2 4 3 1 1
## 200 1 2 2 2 3 1 1 1 1
## 201 2 1 1 1 3 3 2 3 1
## 202 2 2 1 2 2 1 1 2 1
## RS1_w RS1_e RS2 RS2_w RS2_e RS3 RS3_w RS3_e RJS4 RS4_w RS4_e RS5 RS5_w
## 197 2 4 1 2 5 1 1 6 1 3 4 1 1
## 198 4 3 1 6 5 1 4 6 1 4 5 1 5
## 199 6 1 1 3 3 1 5 2 1 1 6 1 5
## 200 6 4 1 6 5 1 3 5 1 6 2 1 1
## 201 4 4 1 4 4 1 1 6 1 4 4 1 2
## 202 2 5 1 2 5 1 3 5 1 2 4 1 3
## RS5_e RS6 RS6_w RS6_e RS7 RS7_w RS7_e RS8 RS8_w RS8_e RS9 RS9_w RS9_e
## 197 5 1 1 6 1 4 4 1 4 3 1 3 4
## 198 5 1 2 6 1 5 5 1 6 4 1 3 5
## 199 2 1 1 6 1 5 4 1 5 6 1 5 4
## 200 6 1 2 5 1 6 1 1 5 5 1 4 5
## 201 6 1 2 5 1 4 3 1 5 3 1 5 4
## 202 5 1 1 5 1 4 4 1 2 5 1 1 5
## clarity_5_R clarity_6 clarity_11_R clarity_13_R clarity_14 clarity_15
## 197 5 3 3 2 3 4
## 198 4 2 2 2 4 2
## 199 5 1 1 1 3 4
## 200 5 4 2 2 5 2
## 201 4 4 2 2 4 2
## 202 1 4 1 3 4 2
## clarity_19 clarity_21_R clarity_25 clarity_28 clarity_30 expressive1
## 197 3 3 3 5 3 4
## 198 4 2 3 4 2 3
## 199 1 2 3 4 1 2
## 200 5 1 3 5 4 4
## 201 4 3 3 4 4 3
## 202 4 1 5 4 4 4
## expressive2_R expressive3 maudsley_1 maudsley_2 maudsley_3 maudsley_4
## 197 3 4 1 4 2 0
## 198 4 3 3 3 4 0
## 199 5 2 0 8 6 0
## 200 3 5 0 4 2 0
## 201 4 3 1 0 1 0
## 202 2 4 1 1 1 1
## maudsley_5 aspectenonderzoek begeleidingvan
## 197 0 volledig Joachim
## 198 1 volledig Joachim
## 199 0 volledig Joachim
## 200 3 volledig Joachim
## 201 0 volledig Kay
## 202 1 volledig Kay
## Opmerkingenoververloop
## 197
## 198
## 199 Ander mailadres: Oomsgeert@gmail.com
## 200 Ander mailadres: Oomsgeert@gmail.com
## 201 Hebben hun betalingsinfo op papier gegeven, het meisje heeft op zondag 4 september om 18 u 30 foutief geantwoord op het blij item, en diezelfde dag om 20 u 45 bij "inleven in partner" foutief op geantwoord
## 202 Hebben hun betalingsinfo op papier gegeven, het meisje heeft op zondag 4 september om 18 u 30 foutief geantwoord op het blij item, en diezelfde dag om 20 u 45 bij "inleven in partner" foutief op geantwoord
## male female ppnr relationship_duration total_months_together
## 197 1 0 815 6:03:00 75
## 198 0 1 115 6:04:00 76
## 199 1 0 816 9:04:00 112
## 200 0 1 116 9:04:00 112
## 201 1 0 817 3:07:00 43
## 202 0 1 117 3:07:00 43
## total_years_together partnumber call_status_rel Extra
## 197 6,25 1
## 198 6,33333333333333 2
## 199 9,33333333333333 1
## 200 9,33333333333333 2
## 201 3,58333333333333 1 Wel_Samenwonend
## 202 3,58333333333333 2 Wel_Samenwonend
## aangeduidinachtergrondvragenlijst new_status_rel PRQCI_commitment com3 com4
## 197 samenwonend 6 8 9
## 198 samenwonend 5 8 9
## 199 getrouwd 7 9 9
## 200 getrouwd 7 9 9
## 201 inconsistent samenwonend 6 9 8
## 202 inconsistent samenwonend 6 9 8
## RUScomm_total couple_satisf
## 197 8,14285714285714 6.333333
## 198 8,14285714285714 5.333333
## 199 9 6.666667
## 200 9 6.000000
## 201 8,28571428571429 6.666667
## 202 8,14285714285714 6.000000
# Find the column names
names(data.baseline)## [1] "couple" "starttijd"
## [3] "informed_consent" "sex"
## [5] "age" "nationality"
## [7] "education" "profession"
## [9] "breaks" "children"
## [11] "total_children" "status_rel"
## [13] "hours_together" "sit"
## [15] "cont_sit" "sev_sit"
## [17] "PRQCI_satisfaction1" "PRQCI_satisfaction2"
## [19] "PRQCI_satisfaction3" "PRQCI_commitment1"
## [21] "PRQCI_commitment2" "PRQCI_commitment3"
## [23] "PRQCI_intimacy1" "PRQCI_intimacy2"
## [25] "PRQCI_intimacy3" "PRQCI_trust1"
## [27] "PRQCI_trust2" "PRQCI_trust3"
## [29] "PRQCI_passion1" "PRQCI_passion2"
## [31] "PRQCI_passion3" "PRQCI_love1"
## [33] "PRQCI_love2" "PRQCI_love3"
## [35] "KMS_1" "KMS_2"
## [37] "KMS_3" "rel_interdependence"
## [39] "com1" "com2"
## [41] "com3_R" "com4_R"
## [43] "com5" "com6"
## [45] "com7" "ICQ_NA1"
## [47] "ICQ_NA2" "ICQ_NA3"
## [49] "ICQ_ES1" "ICQ_ES2"
## [51] "ICQ_ES3" "ICQ_D1"
## [53] "ICQ_D2" "ICQ_D3"
## [55] "ICQ_CM1" "ICQ_CM2"
## [57] "ICQ_CM3" "attachavoid1_R"
## [59] "attachanx1" "attachavoid2"
## [61] "attachanx2" "attachavoid3_R"
## [63] "attachanx3" "attachavoid4"
## [65] "attachanx4_R" "attachavoid5_R"
## [67] "attachanx5" "attachavoid6"
## [69] "attachanx6" "CESD1"
## [71] "CESD2" "CESD3"
## [73] "CESD4_R" "CESD5"
## [75] "CESD6" "CESD7"
## [77] "CESD8_R" "CESD9"
## [79] "CESD10" "CESD11"
## [81] "CESD12_R" "CESD13"
## [83] "CESD14" "CESD15"
## [85] "CESD16_R" "CESD17"
## [87] "CESD18" "CESD19"
## [89] "CESD20" "EC1"
## [91] "EC2_R" "EC3"
## [93] "EC4_R" "EC5_R"
## [95] "EC6" "EC7"
## [97] "PT1" "PT2"
## [99] "PT3_R" "PT4"
## [101] "PT5" "PT6"
## [103] "SWL1" "SWL2"
## [105] "SWL3" "SWL4"
## [107] "SWL5" "dci7_NC"
## [109] "dci10_NC" "dci11_NC"
## [111] "dci15_NC" "dci22_NC"
## [113] "dci25_NC" "dci26_NC"
## [115] "dci27_NC" "RS1"
## [117] "RS1_w" "RS1_e"
## [119] "RS2" "RS2_w"
## [121] "RS2_e" "RS3"
## [123] "RS3_w" "RS3_e"
## [125] "RJS4" "RS4_w"
## [127] "RS4_e" "RS5"
## [129] "RS5_w" "RS5_e"
## [131] "RS6" "RS6_w"
## [133] "RS6_e" "RS7"
## [135] "RS7_w" "RS7_e"
## [137] "RS8" "RS8_w"
## [139] "RS8_e" "RS9"
## [141] "RS9_w" "RS9_e"
## [143] "clarity_5_R" "clarity_6"
## [145] "clarity_11_R" "clarity_13_R"
## [147] "clarity_14" "clarity_15"
## [149] "clarity_19" "clarity_21_R"
## [151] "clarity_25" "clarity_28"
## [153] "clarity_30" "expressive1"
## [155] "expressive2_R" "expressive3"
## [157] "maudsley_1" "maudsley_2"
## [159] "maudsley_3" "maudsley_4"
## [161] "maudsley_5" "aspectenonderzoek"
## [163] "begeleidingvan" "Opmerkingenoververloop"
## [165] "male" "female"
## [167] "ppnr" "relationship_duration"
## [169] "total_months_together" "total_years_together"
## [171] "partnumber" "call_status_rel"
## [173] "Extra" "aangeduidinachtergrondvragenlijst"
## [175] "new_status_rel" "PRQCI_commitment"
## [177] "com3" "com4"
## [179] "RUScomm_total" "couple_satisf"
# View the data set
View(data.baseline)Subsequently, we load the ESM data set.
# Load ESM person period data set
data.person = read.spss("ESM_8.sav", to.data.frame=TRUE)## re-encoding from UTF-8
data.person = data.frame(data.person)
# Find the dimensions
dim(data.person)## [1] 11638 89
# Find the structure
str(data.person)## 'data.frame': 11638 obs. of 89 variables:
## $ couple : num 1 1 1 1 1 1 1 1 1 1 ...
## $ ppnr : num 1 1 1 1 1 1 1 1 1 1 ...
## $ MonthESM : num 3 3 3 3 3 3 3 3 3 3 ...
## $ DayESM : num 3 3 3 3 3 4 4 4 4 4 ...
## $ launchTime : num 1.37e+10 1.37e+10 1.37e+10 1.37e+10 1.37e+10 ...
## $ triggerId : num 0 2 3 4 5 6 7 8 9 10 ...
## $ angry : num 100 42 9 8 4 3 2 2 1 4 ...
## $ sad : num 70 26 6 5 4 5 6 3 2 2 ...
## $ anxiety : num 76 6 5 3 2 2 4 2 2 1 ...
## $ relaxed : num 64 62 76 100 76 74 62 51 59 55 ...
## $ happy : num 74 47 68 66 48 57 68 47 51 50 ...
## $ lonely : num 70 8 1 3 4 4 2 6 1 1 ...
## $ aff_grid : chr "[7,3]" "[5,7]" "[7,4]" "[6,6]" ...
## $ contact_0 : num NA NA NA NA NA 1 1 1 NA NA ...
## $ contact_1 : num NA NA NA NA NA NA NA NA 1 1 ...
## $ contact_2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ contact_3 : num 1 1 1 1 1 NA NA NA NA NA ...
## $ aff_grid_partner : chr "[6,3]" "[4,7]" "[6,2]" "[4,6]" ...
## $ together_0 : num NA NA 1 NA NA 1 1 1 1 1 ...
## $ together_1 : num 1 1 NA 1 1 NA NA NA NA NA ...
## $ perc_respons : num 69 22 54 43 60 59 55 50 46 57 ...
## $ enact_respons : num 76 38 54 44 73 53 41 51 55 46 ...
## $ expres_emot_0 : num NA NA NA NA NA 1 1 1 1 1 ...
## $ expres_emot_1 : num 1 1 1 1 1 NA NA NA NA NA ...
## $ expres_emot_1_A : num NA NA NA NA NA NA NA NA NA NA ...
## $ partner_expr_0 : num NA NA NA NA NA 1 1 1 1 1 ...
## $ partner_expr_1 : num NA 1 1 NA 1 NA NA NA NA NA ...
## $ partner_expr_2 : num 1 NA NA NA NA NA NA NA NA NA ...
## $ partner_expr_3 : num NA NA NA 1 NA NA NA NA NA NA ...
## $ rumination_relation : num 76 24 41 50 6 14 7 17 22 7 ...
## $ pos_thought_rel : num 73 2 52 50 52 54 45 45 62 52 ...
## $ conflict_0 : num NA NA 1 1 1 1 1 1 1 1 ...
## $ conflict_1 : num 1 1 NA NA NA NA NA NA NA NA ...
## $ nice_sit_0 : num NA 1 1 NA NA 1 1 1 1 1 ...
## $ nice_sit_1 : num 1 NA NA 1 1 NA NA NA NA NA ...
## $ enacted_f : num 67 8 42 21 11 28 52 41 26 28 ...
## $ perceived_neglect : num 68 29 28 31 16 22 14 50 24 20 ...
## $ perceived_threat : num 73 49 71 55 9 16 16 42 24 2 ...
## $ sex_arousal : num 66 9 5 5 47 15 12 22 11 28 ...
## $ enacted_h : num 71 54 8 38 12 13 13 35 18 4 ...
## $ expect : num 72 60 10 23 1 16 12 11 5 1 ...
## $ YearESM : num 2016 2016 2016 2016 2016 ...
## $ beeptime : num 64005 68594 72098 73301 76281 ...
## $ hourESM : num 17 19 20 20 21 17 18 19 20 21 ...
## $ minuteESM : num 46 3 1 21 11 24 14 10 2 2 ...
## $ secESM : num 45 14 38 41 21 ...
## $ angry_mean : num 32.6 32.6 32.6 32.6 32.6 2.5 2.5 2.5 2.5 2.5 ...
## $ sad_mean : num 22.2 22.2 22.2 22.2 22.2 ...
## $ anxiety_mean : num 18.4 18.4 18.4 18.4 18.4 ...
## $ relaxed_mean : num 75.6 75.6 75.6 75.6 75.6 ...
## $ happy_mean : num 60.6 60.6 60.6 60.6 60.6 ...
## $ lonely_mean : num 17.2 17.2 17.2 17.2 17.2 ...
## $ aff_grid_first_1 : chr "[7,3]" "[7,3]" "[7,3]" "[7,3]" ...
## $ contact_0_sum : num NA NA NA NA NA 4 4 4 4 4 ...
## $ contact_1_sum : num NA NA NA NA NA 2 2 2 2 2 ...
## $ contact_2_sum : num NA NA NA NA NA NA NA NA NA NA ...
## $ contact_3_sum : num 5 5 5 5 5 NA NA NA NA NA ...
## $ aff_grid_partner_first : chr "[6,3]" "[6,3]" "[6,3]" "[6,3]" ...
## $ together_0_sum : num 1 1 1 1 1 6 6 6 6 6 ...
## $ together_1_sum : num 4 4 4 4 4 NA NA NA NA NA ...
## $ perc_respons_mean : num 49.6 49.6 49.6 49.6 49.6 ...
## $ enact_respons_mean : num 57 57 57 57 57 ...
## $ expres_emot_0_sum : num NA NA NA NA NA 6 6 6 6 6 ...
## $ expres_emot_1_sum : num 5 5 5 5 5 NA NA NA NA NA ...
## $ expres_emot_1_A_sum : num NA NA NA NA NA NA NA NA NA NA ...
## $ partner_expr_0_sum : num NA NA NA NA NA 6 6 6 6 6 ...
## $ partner_expr_1_sum : num 3 3 3 3 3 NA NA NA NA NA ...
## $ partner_expr_2_sum : num 1 1 1 1 1 NA NA NA NA NA ...
## $ partner_expr_3_sum : num 1 1 1 1 1 NA NA NA NA NA ...
## $ rumination_relation_mean: num 39.4 39.4 39.4 39.4 39.4 ...
## $ pos_thought_rel_mean : num 45.8 45.8 45.8 45.8 45.8 ...
## $ conflict_0_sum : num 3 3 3 3 3 6 6 6 6 6 ...
## $ conflict_1_sum : num 2 2 2 2 2 NA NA NA NA NA ...
## $ nice_sit_0_sum : num 2 2 2 2 2 6 6 6 6 6 ...
## $ nice_sit_1_sum : num 3 3 3 3 3 NA NA NA NA NA ...
## $ enacted_f_mean : num 29.8 29.8 29.8 29.8 29.8 32 32 32 32 32 ...
## $ perceived_neglect_mean : num 34.4 34.4 34.4 34.4 34.4 ...
## $ perceived_threat_mean : num 51.4 51.4 51.4 51.4 51.4 19.5 19.5 19.5 19.5 19.5 ...
## $ sex_arousal_mean : num 26.4 26.4 26.4 26.4 26.4 ...
## $ enacted_h_mean : num 36.6 36.6 36.6 36.6 36.6 ...
## $ expect_mean : num 33.2 33.2 33.2 33.2 33.2 ...
## $ Daynr : num 1 1 1 1 1 2 2 2 2 2 ...
## $ startdate_lab : chr "3/03/2016 " "3/03/2016 " "3/03/2016 " "3/03/2016 " ...
## $ starthour_lab : num 59400 59400 59400 59400 59400 59400 59400 59400 59400 59400 ...
## $ enddate : chr "11/03/2016" "11/03/2016" "11/03/2016" "11/03/2016" ...
## $ ESM_starthour : num 64800 64800 64800 64800 64800 64800 64800 64800 64800 64800 ...
## $ partnumber : num 2 2 2 2 2 2 2 2 2 2 ...
## $ beepnr : num 1 2 3 4 5 6 7 8 9 10 ...
## $ valid : num 1 2 3 4 5 6 7 8 9 10 ...
# See the first 6 rows
head(data.person)## couple ppnr MonthESM DayESM launchTime triggerId angry sad anxiety relaxed
## 1 1 1 3 3 13676406405 0 100 70 76 64
## 2 1 1 3 3 13676410994 2 42 26 6 62
## 3 1 1 3 3 13676414498 3 9 6 5 76
## 4 1 1 3 3 13676415701 4 8 5 3 100
## 5 1 1 3 3 13676418681 5 4 4 2 76
## 6 1 1 3 4 13676491493 6 3 5 2 74
## happy lonely aff_grid contact_0 contact_1 contact_2 contact_3
## 1 74 70 [7,3] NA NA NA 1
## 2 47 8 [5,7] NA NA NA 1
## 3 68 1 [7,4] NA NA NA 1
## 4 66 3 [6,6] NA NA NA 1
## 5 48 4 [7,7] NA NA NA 1
## 6 57 4 [5,3] 1 NA NA NA
## aff_grid_partner together_0 together_1 perc_respons enact_respons
## 1 [6,3] NA 1 69 76
## 2 [4,7] NA 1 22 38
## 3 [6,2] 1 NA 54 54
## 4 [4,6] NA 1 43 44
## 5 [6,7] NA 1 60 73
## 6 [4,6] 1 NA 59 53
## expres_emot_0 expres_emot_1 expres_emot_1_A partner_expr_0 partner_expr_1
## 1 NA 1 NA NA NA
## 2 NA 1 NA NA 1
## 3 NA 1 NA NA 1
## 4 NA 1 NA NA NA
## 5 NA 1 NA NA 1
## 6 1 NA NA 1 NA
## partner_expr_2 partner_expr_3 rumination_relation pos_thought_rel conflict_0
## 1 1 NA 76 73 NA
## 2 NA NA 24 2 NA
## 3 NA NA 41 52 1
## 4 NA 1 50 50 1
## 5 NA NA 6 52 1
## 6 NA NA 14 54 1
## conflict_1 nice_sit_0 nice_sit_1 enacted_f perceived_neglect perceived_threat
## 1 1 NA 1 67 68 73
## 2 1 1 NA 8 29 49
## 3 NA 1 NA 42 28 71
## 4 NA NA 1 21 31 55
## 5 NA NA 1 11 16 9
## 6 NA 1 NA 28 22 16
## sex_arousal enacted_h expect YearESM beeptime hourESM minuteESM secESM
## 1 66 71 72 2016 64005 17 46 45
## 2 9 54 60 2016 68594 19 3 14
## 3 5 8 10 2016 72098 20 1 38
## 4 5 38 23 2016 73301 20 21 41
## 5 47 12 1 2016 76281 21 11 21
## 6 15 13 16 2016 62693 17 24 53
## angry_mean sad_mean anxiety_mean relaxed_mean happy_mean lonely_mean
## 1 32.6 22.200000 18.400000 75.60000 60.60000 17.200000
## 2 32.6 22.200000 18.400000 75.60000 60.60000 17.200000
## 3 32.6 22.200000 18.400000 75.60000 60.60000 17.200000
## 4 32.6 22.200000 18.400000 75.60000 60.60000 17.200000
## 5 32.6 22.200000 18.400000 75.60000 60.60000 17.200000
## 6 2.5 3.166667 2.333333 59.33333 54.83333 2.666667
## aff_grid_first_1 contact_0_sum contact_1_sum contact_2_sum contact_3_sum
## 1 [7,3] NA NA NA 5
## 2 [7,3] NA NA NA 5
## 3 [7,3] NA NA NA 5
## 4 [7,3] NA NA NA 5
## 5 [7,3] NA NA NA 5
## 6 [5,3] 4 2 NA NA
## aff_grid_partner_first together_0_sum together_1_sum perc_respons_mean
## 1 [6,3] 1 4 49.60000
## 2 [6,3] 1 4 49.60000
## 3 [6,3] 1 4 49.60000
## 4 [6,3] 1 4 49.60000
## 5 [6,3] 1 4 49.60000
## 6 [4,6] 6 NA 53.33333
## enact_respons_mean expres_emot_0_sum expres_emot_1_sum expres_emot_1_A_sum
## 1 57.00000 NA 5 NA
## 2 57.00000 NA 5 NA
## 3 57.00000 NA 5 NA
## 4 57.00000 NA 5 NA
## 5 57.00000 NA 5 NA
## 6 49.66667 6 NA NA
## partner_expr_0_sum partner_expr_1_sum partner_expr_2_sum partner_expr_3_sum
## 1 NA 3 1 1
## 2 NA 3 1 1
## 3 NA 3 1 1
## 4 NA 3 1 1
## 5 NA 3 1 1
## 6 6 NA NA NA
## rumination_relation_mean pos_thought_rel_mean conflict_0_sum conflict_1_sum
## 1 39.40000 45.80000 3 2
## 2 39.40000 45.80000 3 2
## 3 39.40000 45.80000 3 2
## 4 39.40000 45.80000 3 2
## 5 39.40000 45.80000 3 2
## 6 12.33333 51.16667 6 NA
## nice_sit_0_sum nice_sit_1_sum enacted_f_mean perceived_neglect_mean
## 1 2 3 29.8 34.40000
## 2 2 3 29.8 34.40000
## 3 2 3 29.8 34.40000
## 4 2 3 29.8 34.40000
## 5 2 3 29.8 34.40000
## 6 6 NA 32.0 24.33333
## perceived_threat_mean sex_arousal_mean enacted_h_mean expect_mean Daynr
## 1 51.4 26.40000 36.60000 33.200000 1
## 2 51.4 26.40000 36.60000 33.200000 1
## 3 51.4 26.40000 36.60000 33.200000 1
## 4 51.4 26.40000 36.60000 33.200000 1
## 5 51.4 26.40000 36.60000 33.200000 1
## 6 19.5 16.66667 15.83333 8.666667 2
## startdate_lab starthour_lab enddate ESM_starthour partnumber beepnr valid
## 1 3/03/2016 59400 11/03/2016 64800 2 1 1
## 2 3/03/2016 59400 11/03/2016 64800 2 2 2
## 3 3/03/2016 59400 11/03/2016 64800 2 3 3
## 4 3/03/2016 59400 11/03/2016 64800 2 4 4
## 5 3/03/2016 59400 11/03/2016 64800 2 5 5
## 6 3/03/2016 59400 11/03/2016 64800 2 6 6
# See the last 6 rows
tail(data.person)## couple ppnr MonthESM DayESM launchTime triggerId angry sad anxiety
## 11633 117 817 9 10 13692907077 66 4 2 2
## 11634 117 817 9 10 13692909172 67 3 8 11
## 11635 117 817 9 10 13692913346 68 8 14 8
## 11636 117 817 9 10 13692915397 69 8 9 12
## 11637 117 817 9 10 13692920833 70 4 9 10
## 11638 117 817 9 10 13692921643 71 7 6 7
## relaxed happy lonely aff_grid contact_0 contact_1 contact_2 contact_3
## 11633 44 39 5 [5,5] 1 NA NA NA
## 11634 47 47 5 [5,5] 1 NA NA NA
## 11635 38 33 29 [2,3] NA NA NA 1
## 11636 29 15 38 [3,4] NA NA NA 1
## 11637 63 62 3 [6,6] NA NA NA 1
## 11638 59 62 6 [7,6] NA NA NA 1
## aff_grid_partner together_0 together_1 perc_respons enact_respons
## 11633 [6,6] 1 NA 68 67
## 11634 [7,6] 1 NA 76 80
## 11635 [1,2] NA 1 65 18
## 11636 [1,2] NA 1 76 50
## 11637 [5,6] NA 1 70 62
## 11638 [6,6] NA 1 68 70
## expres_emot_0 expres_emot_1 expres_emot_1_A partner_expr_0 partner_expr_1
## 11633 1 NA NA 1 NA
## 11634 1 NA NA 1 NA
## 11635 1 NA NA NA NA
## 11636 NA NA 1 NA NA
## 11637 NA 1 NA NA 1
## 11638 NA 1 NA NA 1
## partner_expr_2 partner_expr_3 rumination_relation pos_thought_rel
## 11633 NA NA 2 42
## 11634 NA NA 10 52
## 11635 NA 1 12 20
## 11636 NA 1 41 27
## 11637 NA NA 9 35
## 11638 NA NA 7 43
## conflict_0 conflict_1 nice_sit_0 nice_sit_1 enacted_f perceived_neglect
## 11633 1 NA 1 NA 1 6
## 11634 1 NA 1 NA 7 9
## 11635 NA 1 1 NA 14 10
## 11636 NA 1 1 NA 8 15
## 11637 1 NA NA 1 8 9
## 11638 1 NA NA 1 6 12
## perceived_threat sex_arousal enacted_h expect YearESM beeptime hourESM
## 11633 13 25 3 4 2016 62277 17
## 11634 12 32 11 2 2016 64372 17
## 11635 53 17 8 10 2016 68546 19
## 11636 46 14 16 39 2016 70597 19
## 11637 11 50 12 13 2016 76033 21
## 11638 9 58 6 8 2016 76843 21
## minuteESM secESM angry_mean sad_mean anxiety_mean relaxed_mean happy_mean
## 11633 17 57 5.071429 5.428571 6.285714 45.92857 44.85714
## 11634 52 52 5.071429 5.428571 6.285714 45.92857 44.85714
## 11635 2 26 5.071429 5.428571 6.285714 45.92857 44.85714
## 11636 36 37 5.071429 5.428571 6.285714 45.92857 44.85714
## 11637 7 13 5.071429 5.428571 6.285714 45.92857 44.85714
## 11638 20 43 5.071429 5.428571 6.285714 45.92857 44.85714
## lonely_mean aff_grid_first_1 contact_0_sum contact_1_sum contact_2_sum
## 11633 9.357143 [5,2] 7 2 NA
## 11634 9.357143 [5,2] 7 2 NA
## 11635 9.357143 [5,2] 7 2 NA
## 11636 9.357143 [5,2] 7 2 NA
## 11637 9.357143 [5,2] 7 2 NA
## 11638 9.357143 [5,2] 7 2 NA
## contact_3_sum aff_grid_partner_first together_0_sum together_1_sum
## 11633 5 [7,7] 8 6
## 11634 5 [7,7] 8 6
## 11635 5 [7,7] 8 6
## 11636 5 [7,7] 8 6
## 11637 5 [7,7] 8 6
## 11638 5 [7,7] 8 6
## perc_respons_mean enact_respons_mean expres_emot_0_sum expres_emot_1_sum
## 11633 70.5 69.92857 10 3
## 11634 70.5 69.92857 10 3
## 11635 70.5 69.92857 10 3
## 11636 70.5 69.92857 10 3
## 11637 70.5 69.92857 10 3
## 11638 70.5 69.92857 10 3
## expres_emot_1_A_sum partner_expr_0_sum partner_expr_1_sum
## 11633 1 9 3
## 11634 1 9 3
## 11635 1 9 3
## 11636 1 9 3
## 11637 1 9 3
## 11638 1 9 3
## partner_expr_2_sum partner_expr_3_sum rumination_relation_mean
## 11633 NA 2 9.142857
## 11634 NA 2 9.142857
## 11635 NA 2 9.142857
## 11636 NA 2 9.142857
## 11637 NA 2 9.142857
## 11638 NA 2 9.142857
## pos_thought_rel_mean conflict_0_sum conflict_1_sum nice_sit_0_sum
## 11633 38.14286 12 2 11
## 11634 38.14286 12 2 11
## 11635 38.14286 12 2 11
## 11636 38.14286 12 2 11
## 11637 38.14286 12 2 11
## 11638 38.14286 12 2 11
## nice_sit_1_sum enacted_f_mean perceived_neglect_mean
## 11633 3 6.857143 8.785714
## 11634 3 6.857143 8.785714
## 11635 3 6.857143 8.785714
## 11636 3 6.857143 8.785714
## 11637 3 6.857143 8.785714
## 11638 3 6.857143 8.785714
## perceived_threat_mean sex_arousal_mean enacted_h_mean expect_mean Daynr
## 11633 15.14286 32.35714 7.571429 10.21429 8
## 11634 15.14286 32.35714 7.571429 10.21429 8
## 11635 15.14286 32.35714 7.571429 10.21429 8
## 11636 15.14286 32.35714 7.571429 10.21429 8
## 11637 15.14286 32.35714 7.571429 10.21429 8
## 11638 15.14286 32.35714 7.571429 10.21429 8
## startdate_lab starthour_lab enddate ESM_starthour partnumber beepnr
## 11633 3/09/2016 63000 11/09/2016 68400 1 56
## 11634 3/09/2016 63000 11/09/2016 68400 1 57
## 11635 3/09/2016 63000 11/09/2016 68400 1 58
## 11636 3/09/2016 63000 11/09/2016 68400 1 59
## 11637 3/09/2016 63000 11/09/2016 68400 1 60
## 11638 3/09/2016 63000 11/09/2016 68400 1 61
## valid
## 11633 49
## 11634 50
## 11635 51
## 11636 52
## 11637 53
## 11638 54
# Find the column names
names(data.person)## [1] "couple" "ppnr"
## [3] "MonthESM" "DayESM"
## [5] "launchTime" "triggerId"
## [7] "angry" "sad"
## [9] "anxiety" "relaxed"
## [11] "happy" "lonely"
## [13] "aff_grid" "contact_0"
## [15] "contact_1" "contact_2"
## [17] "contact_3" "aff_grid_partner"
## [19] "together_0" "together_1"
## [21] "perc_respons" "enact_respons"
## [23] "expres_emot_0" "expres_emot_1"
## [25] "expres_emot_1_A" "partner_expr_0"
## [27] "partner_expr_1" "partner_expr_2"
## [29] "partner_expr_3" "rumination_relation"
## [31] "pos_thought_rel" "conflict_0"
## [33] "conflict_1" "nice_sit_0"
## [35] "nice_sit_1" "enacted_f"
## [37] "perceived_neglect" "perceived_threat"
## [39] "sex_arousal" "enacted_h"
## [41] "expect" "YearESM"
## [43] "beeptime" "hourESM"
## [45] "minuteESM" "secESM"
## [47] "angry_mean" "sad_mean"
## [49] "anxiety_mean" "relaxed_mean"
## [51] "happy_mean" "lonely_mean"
## [53] "aff_grid_first_1" "contact_0_sum"
## [55] "contact_1_sum" "contact_2_sum"
## [57] "contact_3_sum" "aff_grid_partner_first"
## [59] "together_0_sum" "together_1_sum"
## [61] "perc_respons_mean" "enact_respons_mean"
## [63] "expres_emot_0_sum" "expres_emot_1_sum"
## [65] "expres_emot_1_A_sum" "partner_expr_0_sum"
## [67] "partner_expr_1_sum" "partner_expr_2_sum"
## [69] "partner_expr_3_sum" "rumination_relation_mean"
## [71] "pos_thought_rel_mean" "conflict_0_sum"
## [73] "conflict_1_sum" "nice_sit_0_sum"
## [75] "nice_sit_1_sum" "enacted_f_mean"
## [77] "perceived_neglect_mean" "perceived_threat_mean"
## [79] "sex_arousal_mean" "enacted_h_mean"
## [81] "expect_mean" "Daynr"
## [83] "startdate_lab" "starthour_lab"
## [85] "enddate" "ESM_starthour"
## [87] "partnumber" "beepnr"
## [89] "valid"
# View the data set
View(data.person)
# Select variable to use
data.person = data.person[,c("couple","ppnr","beepnr","DayESM","launchTime","happy","relaxed","enact_respons","together_0","together_1")]
# View a few rows of the data
head(data.person)## couple ppnr beepnr DayESM launchTime happy relaxed enact_respons together_0
## 1 1 1 1 3 13676406405 74 64 76 NA
## 2 1 1 2 3 13676410994 47 62 38 NA
## 3 1 1 3 3 13676414498 68 76 54 1
## 4 1 1 4 3 13676415701 66 100 44 NA
## 5 1 1 5 3 13676418681 48 76 73 NA
## 6 1 1 6 4 13676491493 57 74 53 1
## together_1
## 1 1
## 2 1
## 3 NA
## 4 1
## 5 1
## 6 NA
# Add variable number of observation per person
data.person$obs = rep(0,nrow(data.person))
for (i in unique(data.person$ppnr)){
data.person$obs[which(data.person$ppnr==i)] = 1:length(which(data.person$ppnr==i))
}
# Create presence of partner variable
data.person$together = rowSums(cbind(1-data.person$together_0,data.person$together_1), na.rm=TRUE)
data.person$together = ifelse(is.na(data.person$together_0) & is.na(data.person$together_1),NA,data.person$together)
data.person = data.person[,c("couple","ppnr","beepnr","DayESM","launchTime","obs","happy","relaxed","enact_respons","together")]
# create variable Gender
data.person$gender = ifelse(data.person$ppnr == data.person$couple,"F","M")The baseline data set includes more participants than the ESM data set. Thus, for the participants included in the ESM data set, we append the person-level or time-invariant variables Age and couple satisfaction.
# Get the identification number of each participant
ID.dyad = unique(data.person$ppnr)
# Add variables Age and items related to couple satisfaction to the ESM data set
data.person$age = rep(0,nrow(data.person))
data.person$couple_satisf = rep(0,nrow(data.person))
for (i in ID.dyad){
ID.couple.i = unique(data.person$couple[which(data.person$ppnr==i)])
Gender.i = unique(data.person$gender[which(data.person$ppnr==i)])
data.person$age[which(data.person$ppnr==i)] = data.baseline$age[which(c(data.baseline$couple==ID.couple.i &
data.baseline$sex==Gender.i) == TRUE)]
data.person$couple_satisf[which(data.person$ppnr==i)] = data.baseline$couple_satisf[which(c(data.baseline$couple==ID.couple.i &
data.baseline$sex==Gender.i) == TRUE)]
}
# Find the structure
str(data.person)## 'data.frame': 11638 obs. of 13 variables:
## $ couple : num 1 1 1 1 1 1 1 1 1 1 ...
## $ ppnr : num 1 1 1 1 1 1 1 1 1 1 ...
## $ beepnr : num 1 2 3 4 5 6 7 8 9 10 ...
## $ DayESM : num 3 3 3 3 3 4 4 4 4 4 ...
## $ launchTime : num 1.37e+10 1.37e+10 1.37e+10 1.37e+10 1.37e+10 ...
## $ obs : num 1 2 3 4 5 6 7 8 9 10 ...
## $ happy : num 74 47 68 66 48 57 68 47 51 50 ...
## $ relaxed : num 64 62 76 100 76 74 62 51 59 55 ...
## $ enact_respons: num 76 38 54 44 73 53 41 51 55 46 ...
## $ together : num 1 1 0 1 1 0 0 0 0 0 ...
## $ gender : chr "F" "F" "F" "F" ...
## $ age : num 24 24 24 24 24 24 24 24 24 24 ...
## $ couple_satisf: num 5 5 5 5 5 5 5 5 5 5 ...
# summary of the data
summary(data.person)## couple ppnr beepnr DayESM
## Min. : 1.00 Min. : 1.0 Min. : 1.0 Min. : 1.00
## 1st Qu.: 29.00 1st Qu.: 58.0 1st Qu.:16.0 1st Qu.: 6.00
## Median : 58.00 Median :117.0 Median :31.0 Median :14.00
## Mean : 59.13 Mean :408.8 Mean :31.6 Mean :14.57
## 3rd Qu.: 86.00 3rd Qu.:758.0 3rd Qu.:47.0 3rd Qu.:23.00
## Max. :117.00 Max. :817.0 Max. :72.0 Max. :31.00
##
## launchTime obs happy relaxed
## Min. :1.368e+10 Min. : 1.0 Min. : 0.00 Min. : 0.00
## 1st Qu.:1.368e+10 1st Qu.:16.0 1st Qu.: 50.00 1st Qu.: 50.00
## Median :1.368e+10 Median :31.0 Median : 64.00 Median : 66.00
## Mean :1.369e+10 Mean :31.6 Mean : 62.64 Mean : 63.63
## 3rd Qu.:1.369e+10 3rd Qu.:47.0 3rd Qu.: 78.00 3rd Qu.: 80.00
## Max. :1.369e+10 Max. :72.0 Max. :100.00 Max. :100.00
## NA's :816 NA's :816
## enact_respons together gender age
## Min. : 0.00 Min. :0.0000 Length:11638 Min. :18.00
## 1st Qu.: 63.00 1st Qu.:0.0000 Class :character 1st Qu.:23.00
## Median : 77.00 Median :1.0000 Mode :character Median :25.00
## Mean : 74.57 Mean :0.5937 Mean :26.33
## 3rd Qu.: 89.00 3rd Qu.:1.0000 3rd Qu.:28.00
## Max. :100.00 Max. :1.0000 Max. :53.00
## NA's :816 NA's :816
## couple_satisf
## Min. :3.000
## 1st Qu.:5.667
## Median :6.000
## Mean :5.971
## 3rd Qu.:6.333
## Max. :7.000
## NA's :69
# See the first 6 rows
head(data.person)## couple ppnr beepnr DayESM launchTime obs happy relaxed enact_respons
## 1 1 1 1 3 13676406405 1 74 64 76
## 2 1 1 2 3 13676410994 2 47 62 38
## 3 1 1 3 3 13676414498 3 68 76 54
## 4 1 1 4 3 13676415701 4 66 100 44
## 5 1 1 5 3 13676418681 5 48 76 73
## 6 1 1 6 4 13676491493 6 57 74 53
## together gender age couple_satisf
## 1 1 F 24 5
## 2 1 F 24 5
## 3 0 F 24 5
## 4 1 F 24 5
## 5 1 F 24 5
## 6 0 F 24 5
# See the last 6 rows
tail(data.person)## couple ppnr beepnr DayESM launchTime obs happy relaxed enact_respons
## 11633 117 817 56 10 13692907077 56 39 44 67
## 11634 117 817 57 10 13692909172 57 47 47 80
## 11635 117 817 58 10 13692913346 58 33 38 18
## 11636 117 817 59 10 13692915397 59 15 29 50
## 11637 117 817 60 10 13692920833 60 62 63 62
## 11638 117 817 61 10 13692921643 61 62 59 70
## together gender age couple_satisf
## 11633 0 M 30 6.666667
## 11634 0 M 30 6.666667
## 11635 1 M 30 6.666667
## 11636 1 M 30 6.666667
## 11637 1 M 30 6.666667
## 11638 1 M 30 6.666667
# Order the variables
data.person = data.person[,c("couple","ppnr","beepnr","DayESM","launchTime","obs","gender","age","couple_satisf","happy","relaxed","enact_respons","together")]We first obtain some descriptive statistics including number of dyads, number of participant, number of observations per day, and compliance.
# Get number of dyads
length(unique(data.person$couple)) ## [1] 94
# Get number of participants
length(unique(data.person$ppnr))## [1] 188
# Get the number of assessment per day
table(data.person$DayESM)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 518 694 728 532 410 406 356 386 492 532 238 220 224 304 228 392 294 284 288 342
## 21 22 23 24 25 26 27 28 29 30 31
## 356 292 324 268 228 320 404 448 416 422 292
# Get the number of assessment per day for each participant
beeps.person = lapply(data.person$ppnr, function(i) table(data.person$DayESM[which(data.person$ppnr==i)]))
# Show results for some of the participants
beeps.person[1:6]## [[1]]
##
## 3 4 5 6 7 8 9 10
## 5 6 14 14 6 6 6 6
##
## [[2]]
##
## 3 4 5 6 7 8 9 10
## 5 6 14 14 6 6 6 6
##
## [[3]]
##
## 3 4 5 6 7 8 9 10
## 5 6 14 14 6 6 6 6
##
## [[4]]
##
## 3 4 5 6 7 8 9 10
## 5 6 14 14 6 6 6 6
##
## [[5]]
##
## 3 4 5 6 7 8 9 10
## 5 6 14 14 6 6 6 6
##
## [[6]]
##
## 3 4 5 6 7 8 9 10
## 5 6 14 14 6 6 6 6
# Distribution of the launch time per participant
data.table.dt = setDT(data.person)
data.table.dt[, as.list(summary(launchTime)), by = ppnr]## ppnr Min. 1st Qu. Median Mean 3rd Qu.
## 1: 1 13676406405 13676566935 13676656215 13676692409 13676842366
## 2: 6 13678377036 13678473304 13678660596 13678684000 13678918188
## 3: 7 13678383054 13678476192 13678662141 13678693123 13678918723
## 4: 8 13678392519 13678484439 13678701730 13678707464 13678921309
## 5: 9 13678741947 13678923940 13679058602 13679047034 13679174527
## ---
## 184: 813 13692140520 13692299272 13692386874 13692431255 13692569267
## 185: 814 13692278708 13692376817 13692565339 13692581148 13692825274
## 186: 815 13692283668 13692381288 13692568804 13692589924 13692826042
## 187: 816 13692290058 13692384836 13692571955 13692598896 13692827314
## 188: 817 13692310342 13692401141 13692652563 13692633398 13692834504
## Max.
## 1: 13677025725
## 2: 13679010539
## 3: 13679011501
## 4: 13679011453
## 5: 13679356460
## ---
## 184: 13692750135
## 185: 13692922061
## 186: 13692923745
## 187: 13692922052
## 188: 13692921643
# Plot the distribution of launch time per participant (we sample 10 participants)
n.ID.sample = sample(unique(data.person$ppnr),10)
data.person.sample = data.person[which(data.person$ppnr %in% n.ID.sample),]
ggplot(data.person.sample, aes(x=obs, launchTime)) + geom_point() + facet_wrap(~ppnr)
# Compute a binary variable indicating if a participant answered a beep. We take the ESM item Happy as reference
data.person$Compliance = ifelse(is.na(data.person$happy)==FALSE, 1, 0)
# Mean, median of the compliance across all participants
describe(data.person$Compliance)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11638 0.93 0.26 1 1 0 0 1 1 -3.37 9.34 0
# Compliance per participant
data.compliance.person = aggregate(data.person$Compliance, by = list(data.person$ppnr), mean, na.rm = TRUE)
# See the first 6 rows
head(data.compliance.person)## Group.1 x
## 1 1 0.6984127
## 2 6 0.9565217
## 3 7 0.9552239
## 4 8 0.9687500
## 5 9 0.9523810
## 6 10 0.8852459
# See the last 6 rows
tail(data.compliance.person)## Group.1 x
## 183 812 0.9354839
## 184 813 0.9500000
## 185 814 0.9027778
## 186 815 0.8000000
## 187 816 0.9558824
## 188 817 0.8852459
# Obtain descriptive statistics of person's average compliance
describe(data.compliance.person$x)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 188 0.93 0.06 0.95 0.94 0.05 0.7 1 0.3 -1.28 1.58 0
Next, we obtain visualizations and statistics of the distribution of the person-level or time-invariant variables variables
# We create a variable including the Gender (1 = F, 2 = M), Age and Couple Satisfaction of each participant
dt.person = aggregate(cbind(as.numeric(as.factor(data.person$gender)),data.person$happy), by = list(data.person$ppnr), mean, na.rm = TRUE)
colnames(dt.person) = c("Group.1","gender","happy")
# See the first 6 rows
head(dt.person)## Group.1 gender happy
## 1 1 1 59.20455
## 2 6 1 61.86364
## 3 7 1 53.29688
## 4 8 1 55.14516
## 5 9 1 82.78333
## 6 10 1 78.16667
# See the last 6 rows
tail(dt.person)## Group.1 gender happy
## 183 812 2 84.63793
## 184 813 2 52.38596
## 185 814 2 65.83077
## 186 815 2 58.37500
## 187 816 2 49.29231
## 188 817 2 62.96296
# Descriptive statistics for person's means of time-varying variable happy
describe(dt.person$happy)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 188 62.6 12.07 62.26 62.38 12.23 26.65 97.47 70.82 0.1 -0.01
## se
## X1 0.88
# Descriptive statistics for person's means of time-varying variable happy for women
describe(dt.person$happy[dt.person$gender==1])## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 94 62.23 12.16 62.05 62.34 11.73 26.65 89.09 62.45 -0.19 -0.06
## se
## X1 1.25
# Descriptive statistics for person's means of time-varying variable happy for men
describe(dt.person$happy[dt.person$gender==2])## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 94 62.97 12.04 62.39 62.39 12.94 36.98 97.47 60.48 0.4 -0.09
## se
## X1 1.24
We now focus on time-varying variables, we obtain visualization and descriptive statistics
# Histogram for the time-varying variable happy
ggplot(data.person, aes(happy)) + geom_histogram(color="black", fill="white",bins=30) 
# Histogram for the time-varying variable happy by gender
ggplot(data.person, aes(happy)) + geom_histogram(color="black", fill="white",bins=30) +
facet_wrap(~gender)
# Descriptive statistics for happy
describe(data.person$happy)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 10822 62.64 21.78 64 63.8 20.76 0 100 100 -0.5 0.06 0.21
# Descriptive statistics for happy for women
describe(data.person$happy[data.person$gender=='F'])## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 5394 62.24 22.25 64 63.53 20.76 0 100 100 -0.52 0.02 0.3
# Descriptive statistics for happy for men
describe(data.person$happy[data.person$gender=='M'])## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 5428 63.05 21.3 64 64.06 20.76 0 100 100 -0.46 0.08 0.29
# Distribution of happy per participant
data.table.dt = setDT(na.omit(data.person))
data.table.dt[, as.list(summary(happy, na.omit = TRUE)), by = ppnr]## ppnr Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1: 1 2 55.00 60.5 59.20455 66.50 84
## 2: 6 32 50.25 59.5 61.86364 74.75 91
## 3: 7 1 28.75 60.5 53.29688 75.00 100
## 4: 8 25 42.25 59.0 55.14516 65.75 82
## 5: 9 46 74.00 82.5 82.78333 96.75 100
## ---
## 183: 813 13 41.00 57.0 52.38596 64.00 79
## 184: 814 4 57.00 66.0 65.83077 77.00 100
## 185: 815 23 42.00 57.5 58.37500 71.00 100
## 186: 816 5 42.00 49.0 49.29231 54.00 100
## 187: 817 15 47.25 65.5 62.96296 75.00 94
# We randomly select 10 participants for plotting the distribution of the time-varying variable happy
n.ID.sample = sample(unique(data.person$ppnr),10)
data.person.sample = data.person[which(data.person$ppnr %in% n.ID.sample),]
# Histogram for the time-varying variable happy by person
ggplot(data.person.sample, aes(happy)) + geom_histogram(color="black", fill="white",bins=30) +
facet_wrap(~ppnr)
# Plot the trajectories of the time-varying variable happy by person
data.person.sample %>%
ggplot(aes(x = obs, y = happy)) +
geom_point() +
geom_line() + # add lines to connect the data for each person
facet_wrap( ~ ppnr)
# We create a variable including the Gender (1 = F, 2 = M), and person's means of the time-varying variable happy
dt.person = aggregate(cbind(as.numeric(as.factor(data.person$gender)),data.person$happy), by = list(data.person$ppnr), mean, na.rm = TRUE)
colnames(dt.person) = c("Group.1","gender","happy")
# See the first 6 rows
head(dt.person)## Group.1 gender happy
## 1 1 1 59.20455
## 2 6 1 61.86364
## 3 7 1 53.29688
## 4 8 1 55.14516
## 5 9 1 82.78333
## 6 10 1 78.16667
# See the last 6 rows
tail(dt.person)## Group.1 gender happy
## 183 812 2 84.63793
## 184 813 2 52.38596
## 185 814 2 65.83077
## 186 815 2 58.37500
## 187 816 2 49.29231
## 188 817 2 62.96296
# Descriptive statistics for person's means of the time-varying variable happy
describe(dt.person$happy)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 188 62.6 12.07 62.26 62.38 12.23 26.65 97.47 70.82 0.1 -0.01
## se
## X1 0.88
# Descriptive statistics for person's means of the time-varying variable happy for women
describe(dt.person$happy[dt.person$gender==1])## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 94 62.23 12.16 62.05 62.34 11.73 26.65 89.09 62.45 -0.19 -0.06
## se
## X1 1.25
# Descriptive statistics for person's means of the time-varying variable happy for men
describe(dt.person$happy[dt.person$gender==2])## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 94 62.97 12.04 62.39 62.39 12.94 36.98 97.47 60.48 0.4 -0.09
## se
## X1 1.24
# We create a variable including the Gender (1 = F, 2 = M), and person's standard deviation of the time-varying variable happy
dt.person = aggregate(data.person$happy, by = list(data.person$ppnr), sd, na.rm = TRUE)
colnames(dt.person) = c("Group.1","happy")
# See the first 6 rows
head(dt.person)## Group.1 happy
## 1 1 15.51830
## 2 6 15.74400
## 3 7 28.39276
## 4 8 14.47762
## 5 9 14.15063
## 6 10 17.01026
# See the last 6 rows
tail(dt.person)## Group.1 happy
## 183 812 14.44250
## 184 813 17.07625
## 185 814 20.78128
## 186 815 19.97777
## 187 816 17.81215
## 188 817 18.31791
# Descriptive statistics for person's standard deviation of the time-varying variable happy
describe(dt.person$happy)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 188 17.49 5.43 16.62 17.24 5.01 4.66 38.75 34.09 0.65 0.9 0.4
# Visualization of the trajectories for the members of a dyad
## We first randomly select a dyad
dyad.ID.sample = sample(unique(data.person$couple),1)
data.dyad.sample = data.person[which(data.person$couple==dyad.ID.sample),]
# Add variable number of observation per person
data.dyad.sample$obs = rep(0,nrow(data.dyad.sample))
for (i in unique(data.dyad.sample$ppnr)){
data.dyad.sample$obs[which(data.dyad.sample$ppnr==i)] = 1:length(which(data.dyad.sample$ppnr==i))
}
# Plot the trajectories of the time-varying variable happy by person
data.dyad.sample %>%
ggplot(aes(x = obs, y = happy)) +
geom_point() +
geom_line() + # add lines to connect the data for each person
facet_wrap( ~ ppnr)
The ESM data set is in a long format. To estimate APIMs models and VAR(1)-based models taking into account the dyadic structure using linear mixed-effects models, we need to re-structure the data set (see e.g., Laurenceau and Bolger (2012)).
# create variable Gender
data.person$Gender = ifelse(data.person$ppnr == data.person$couple,'F','M')
# Create lag outcome (happy): for each person lagged within days
data.person$happy.lag = rep(0,nrow(data.person))
n.subject = unique(data.person$ppnr)
for (j in n.subject){
n.day.j = unique(data.person$DayESM)
for (t in n.day.j){
data.person$happy.lag[which(data.person$ppnr==j & data.person$DayESM==t)] = shift(data.person$happy[which(data.person$ppnr==j & data.person$DayESM==t)])
}}
# Re-shape the data into dyadic data set
data.dyad = data.person[,c('couple','ppnr','beepnr','DayESM','Gender','age','couple_satisf')]
# Re-name the variables
colnames(data.dyad) = c('dyad.ID','subject.ID','Obs','Day','Gender','Age','couple_satisf')
# Create variable Female and Male
data.dyad$Female = ifelse(data.dyad$Gender == 'F',1,0)
data.dyad$Male = ifelse(data.dyad$Gender == 'M',1,0)
# Re-shape data set: Y (happy), X (enact_response)
data.dyad$Y.happy = rep(0,nrow(data.dyad))
data.dyad$Y.happy.lag = rep(0,nrow(data.dyad))
data.dyad$Y.Actor.lag = rep(0,nrow(data.dyad))
data.dyad$Y.Partner.lag = rep(0,nrow(data.dyad))
data.dyad$X.Actor = rep(0,nrow(data.dyad))
data.dyad$X.Partner = rep(0,nrow(data.dyad))
data.dyad$D.Actor = rep(0,nrow(data.dyad))
data.dyad$D.Partner = rep(0,nrow(data.dyad))
N.dyad = unique(data.dyad$dyad.ID)
for (i in N.dyad){
N.id = which(data.dyad$dyad.ID==i)
data.dyad$Y.happy[N.id[which(data.person[N.id,]$Gender=='F')]] = data.person$happy[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$Y.happy[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$happy[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$Y.happy.lag[N.id[which(data.person[N.id,]$Gender=='F')]] = data.person$happy.lag[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$Y.happy.lag[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$happy.lag[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$X.Actor[N.id[which(data.dyad[N.id,]$Gender=='F')]] = data.person$enact_respons[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$X.Actor[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$enact_respons[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$X.Partner[N.id[which(data.dyad[N.id,]$Gender=='F')]] = data.person$enact_respons[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$X.Partner[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$enact_respons[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$Y.Actor.lag[N.id[which(data.dyad[N.id,]$Gender=='F')]] = data.person$happy.lag[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$Y.Actor.lag[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$happy.lag[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$Y.Partner.lag[N.id[which(data.dyad[N.id,]$Gender=='F')]] = data.person$happy.lag[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$Y.Partner.lag[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$happy.lag[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$D.Actor[N.id[which(data.dyad[N.id,]$Gender=='F')]] = data.person$together[N.id[which(data.person[N.id,]$Gender=='F')]]
data.dyad$D.Actor[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$together[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$D.Partner[N.id[which(data.dyad[N.id,]$Gender=='F')]] = data.person$together[N.id[which(data.person[N.id,]$Gender=='M')]]
data.dyad$D.Partner[N.id[which(data.dyad[N.id,]$Gender=='M')]] = data.person$together[N.id[which(data.person[N.id,]$Gender=='F')]]
}
# Create variable dyad variable together
data.dyad$D = data.dyad$D.Actor + data.dyad$D.Partner
# Number of beeps where the both partners said they were together
length(which(data.dyad$D==2))## [1] 6067
# Proportion of beeps where the both partners said they were together
length(which(data.dyad$D==2))/length(data.dyad$D.Partner)## [1] 0.5213095
# Number of beeps where the both partners said they were not together
length(which(data.dyad$D==0))## [1] 3782
# Proportion of beeps where the both partners said they were not together
length(which(data.dyad$D==0))/length(data.dyad$D.Partner)## [1] 0.3249699
# Number of beeps where the both partners said they were disagree of being together
length(which(data.dyad$D==1))## [1] 454
# Proportion of beeps where the both partners said they were disagree of being together
length(which(data.dyad$D==1))/length(data.dyad$D.Partner)## [1] 0.03901014
# Select beeps where at least one of the dyad members said that they were together
data.dyad$D = ifelse(data.dyad$D==2, 1, data.dyad$D)
# Number of beeps where the both partners said they were at least one of the partners were together
length(which(data.dyad$D==1))## [1] 6521
# Proportion of beeps where the both partners said they were at least one of the partners were together
length(which(data.dyad$D==1))/length(data.dyad$D.Partner)## [1] 0.5603196
Next, we are going to create variable with person-mean centered predictors:
# Person-mean centered the predictors and create a variable with person's mean
data.dyad <- data.dyad %>%
group_by(subject.ID,dyad.ID) %>%
mutate(X.Actor.c = X.Actor - mean(X.Actor,na.rm = TRUE),
X.Partner.c = X.Partner - mean(X.Partner,na.rm = TRUE),
X.Actor.mean = mean(X.Actor,na.rm = TRUE),
X.Partner.mean = mean(X.Partner,na.rm = TRUE))
# See the first 6 rows
head(data.dyad)## # A tibble: 6 x 22
## # Groups: subject.ID, dyad.ID [1]
## dyad.ID subject.ID Obs Day Gender Age couple_satisf Female Male Y.happy
## <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 3 F 24 5 1 0 74
## 2 1 1 2 3 F 24 5 1 0 47
## 3 1 1 3 3 F 24 5 1 0 68
## 4 1 1 4 3 F 24 5 1 0 66
## 5 1 1 5 3 F 24 5 1 0 48
## 6 1 1 6 4 F 24 5 1 0 57
## # ... with 12 more variables: Y.happy.lag <dbl>, Y.Actor.lag <dbl>,
## # Y.Partner.lag <dbl>, X.Actor <dbl>, X.Partner <dbl>, D.Actor <dbl>,
## # D.Partner <dbl>, D <dbl>, X.Actor.c <dbl>, X.Partner.c <dbl>,
## # X.Actor.mean <dbl>, X.Partner.mean <dbl>
# See the last 6 rows
tail(data.dyad)## # A tibble: 6 x 22
## # Groups: subject.ID, dyad.ID [1]
## dyad.ID subject.ID Obs Day Gender Age couple_satisf Female Male Y.happy
## <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 117 817 56 10 M 30 6.67 0 1 39
## 2 117 817 57 10 M 30 6.67 0 1 47
## 3 117 817 58 10 M 30 6.67 0 1 33
## 4 117 817 59 10 M 30 6.67 0 1 15
## 5 117 817 60 10 M 30 6.67 0 1 62
## 6 117 817 61 10 M 30 6.67 0 1 62
## # ... with 12 more variables: Y.happy.lag <dbl>, Y.Actor.lag <dbl>,
## # Y.Partner.lag <dbl>, X.Actor <dbl>, X.Partner <dbl>, D.Actor <dbl>,
## # D.Partner <dbl>, D <dbl>, X.Actor.c <dbl>, X.Partner.c <dbl>,
## # X.Actor.mean <dbl>, X.Partner.mean <dbl>
We first illustrate how to estimate a linear model for cross-sectional data. Thus, we are going to compute the persons’ means of the variables included in the data set data.dyad.
# First, we re-code the variable Gender from a categorical to numerical variable
# in the new data set 'data.dyad.num' Gender = 0 for female partner and Gender = 1 for male partner
data.dyad.num = data.dyad
data.dyad.num$Gender = as.numeric(as.factor(data.dyad.num$Gender))-1
# Create a data set by computing the person's mean.
data.dyad.mean = aggregate(data.dyad.num, by = list(data.person$ppnr), mean, na.rm = TRUE)We estimate a linear model to investigate the effect of persons’ mean enacted response on the person’s happiness for male partners using a linear model estimates by least squares.
# Estimate the linear model
fit.Model.1 = lm(Y.happy ~ 1 + X.Actor,
data = data.dyad.mean[which(data.dyad.mean$Gender==1),])
summary(fit.Model.1)##
## Call:
## lm(formula = Y.happy ~ 1 + X.Actor, data = data.dyad.mean[which(data.dyad.mean$Gender ==
## 1), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.9688 -5.0747 0.5112 6.9238 27.1168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.02305 5.61808 5.344 6.56e-07 ***
## X.Actor 0.44197 0.07401 5.972 4.37e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.28 on 92 degrees of freedom
## Multiple R-squared: 0.2793, Adjusted R-squared: 0.2715
## F-statistic: 35.66 on 1 and 92 DF, p-value: 4.366e-08
tab_model(fit.Model.1)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
30.02 |
18.87 – 41.18 |
<0.001 |
|
X Actor |
0.44 |
0.29 – 0.59 |
<0.001 |
|
Observations |
94 |
||
|
R2 / R2 adjusted |
0.279 / 0.272 |
||
# Plot predictions for the person-mean enacted response for male partners
ggpredict(fit.Model.1, terms = c("X.Actor")) |> plot()
We estimate a linear model to investigate if the effect of persons’ mean enacted response on the person’s happiness for male partners is moderated by the averate time in which participants are together during the ESM period. We use a linear model estimated by least squares.
# Estimate the linear model
fit.Model.2 = lm(Y.happy ~ 1 + X.Actor + X.Actor*D,
data = data.dyad.mean[which(data.dyad.mean$Gender==1),])
summary(fit.Model.2)##
## Call:
## lm(formula = Y.happy ~ 1 + X.Actor + X.Actor * D, data = data.dyad.mean[which(data.dyad.mean$Gender ==
## 1), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.650 -4.983 1.214 6.379 25.423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6630 18.1307 0.478 0.63395
## X.Actor 0.6793 0.2485 2.734 0.00754 **
## D 34.3404 26.2715 1.307 0.19450
## X.Actor:D -0.3781 0.3548 -1.066 0.28940
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.23 on 90 degrees of freedom
## Multiple R-squared: 0.3024, Adjusted R-squared: 0.2791
## F-statistic: 13 on 3 and 90 DF, p-value: 3.952e-07
tab_model(fit.Model.2)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
8.66 |
-27.36 – 44.68 |
0.634 |
|
X Actor |
0.68 |
0.19 – 1.17 |
0.008 |
|
D |
34.34 |
-17.85 – 86.53 |
0.194 |
|
X Actor * D |
-0.38 |
-1.08 – 0.33 |
0.289 |
|
Observations |
94 |
||
|
R2 / R2 adjusted |
0.302 / 0.279 |
||
# Plot predictions for the person-mean enacted response for male partners
ggpredict(fit.Model.2, terms = c("X.Actor", "D")) |> plot()
We estimate a linear model to investigate the actor and partner effect of persons’ mean enacted response on the person’s happiness for dyadic partners using a generalized linear models.
# Estimate APIM model for distinguishable partners
fit.Model.3 = gls(Y.happy ~ -1 + Female + Female:X.Actor + Female:X.Partner +
Male + Male:X.Actor + Male:X.Partner,
correlation = corSymm(form= ~1|dyad.ID),
weights = varIdent(form= ~1|Gender),
data = data.dyad.mean,
na.action = na.exclude)
summary(fit.Model.3)## Generalized least squares fit by REML
## Model: Y.happy ~ -1 + Female + Female:X.Actor + Female:X.Partner + Male + Male:X.Actor + Male:X.Partner
## Data: data.dyad.mean
## AIC BIC logLik
## 1422.227 1451.063 -702.1134
##
## Correlation Structure: General
## Formula: ~1 | dyad.ID
## Parameter estimate(s):
## Correlation:
## 1
## 2 0.453
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## 0 1
## 1.0000000 0.9459012
##
## Coefficients:
## Value Std.Error t-value p-value
## Female 39.14790 7.316104 5.350922 0.0000
## Male 31.32386 6.911166 4.532356 0.0000
## Female:X.Actor 0.38960 0.079333 4.910985 0.0000
## Female:X.Partner -0.08013 0.082122 -0.975710 0.3305
## X.Actor:Male 0.44969 0.077445 5.806576 0.0000
## X.Partner:Male -0.02516 0.075068 -0.335196 0.7379
##
## Correlation:
## Female Male Fm:X.A Fm:X.P X.Ac:M
## Male 0.453
## Female:X.Actor -0.575 -0.261
## Female:X.Partner -0.611 -0.276 -0.279
## X.Actor:Male -0.276 -0.609 -0.127 0.452
## X.Partner:Male -0.261 -0.577 0.453 -0.126 -0.279
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -3.32428240 -0.55765150 0.05866253 0.69553659 2.67807664
##
## Residual standard error: 10.92056
## Degrees of freedom: 188 total; 182 residual
We estimate a linear model to investigate the actor and partner effect of persons’ mean enacted response on the person’s happiness for dyadic partners for dyadic partners using a generalized linear models.
# Estimate APIM model for indistinguishable partners
fit.Model.4 = gls(Y.happy ~ 1 + X.Actor + X.Partner,
correlation = corSymm(form= ~1|dyad.ID),
weights = varIdent(form= ~1|Gender),
data = data.dyad.mean,
na.action = na.exclude)
summary(fit.Model.4)## Generalized least squares fit by REML
## Model: Y.happy ~ 1 + X.Actor + X.Partner
## Data: data.dyad.mean
## AIC BIC logLik
## 1414.702 1434.024 -701.3509
##
## Correlation Structure: General
## Formula: ~1 | dyad.ID
## Parameter estimate(s):
## Correlation:
## 1
## 2 0.449
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## 0 1
## 1.000000 0.946108
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 34.84114 6.014607 5.792755 0.0000
## X.Actor 0.42124 0.051589 8.165284 0.0000
## X.Partner -0.04844 0.051583 -0.939027 0.3489
##
## Correlation:
## (Intr) X.Actr
## X.Actor -0.763
## X.Partner -0.763 0.193
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -3.46479841 -0.58192499 0.03848442 0.63937833 2.78917115
##
## Residual standard error: 10.87274
## Degrees of freedom: 188 total; 185 residual
We estimate a linear model to investigate if the actor and partner effect of persons’ mean enacted response on the person’s happiness is moderated by the average number of beeps that dyadic partners spend together.
# Create the interaction variable between enacted response and the average number of beeps that dyadic partners spend together
data.dyad.mean$D.X.Actor = data.dyad.mean$D*data.dyad.mean$X.Actor
data.dyad.mean$D.X.Partner = data.dyad.mean$D*data.dyad.mean$X.Partner
# Estimate APIM model for distinguishable partners with moderation effects
fit.Model.5 = gls(Y.happy ~ -1 + Female + Female:X.Actor + Female:X.Partner +
+ Female:D + Female:D.X.Actor + Female:D.X.Partner +
Male + Male:X.Actor + Male:X.Partner +
Male:D + Male:D.X.Actor + Male:D.X.Partner,
correlation = corSymm(form= ~1|dyad.ID),
weights = varIdent(form= ~1|Gender),
data = data.dyad.mean,
na.action = na.exclude)
summary(fit.Model.5)## Generalized least squares fit by REML
## Model: Y.happy ~ -1 + Female + Female:X.Actor + Female:X.Partner + +Female:D + Female:D.X.Actor + Female:D.X.Partner + Male + Male:X.Actor + Male:X.Partner + Male:D + Male:D.X.Actor + Male:D.X.Partner
## Data: data.dyad.mean
## AIC BIC logLik
## 1410.204 1457.761 -690.1019
##
## Correlation Structure: General
## Formula: ~1 | dyad.ID
## Parameter estimate(s):
## Correlation:
## 1
## 2 0.469
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## 0 1
## 1.000000 0.963139
##
## Coefficients:
## Value Std.Error t-value p-value
## Female 55.82284 20.635627 2.7051681 0.0075
## Male 20.52465 19.875107 1.0326814 0.3032
## Female:X.Actor 0.45259 0.271795 1.6652003 0.0977
## Female:X.Partner -0.49232 0.309517 -1.5906196 0.1135
## Female:D -21.53777 30.906109 -0.6968774 0.4868
## Female:D.X.Actor -0.08078 0.402414 -0.2007503 0.8411
## Female:D.X.Partner 0.56549 0.436848 1.2944815 0.1972
## X.Actor:Male 0.92063 0.298181 3.0874977 0.0023
## X.Partner:Male -0.38209 0.261825 -1.4593229 0.1463
## D:Male 16.57777 29.761357 0.5570232 0.5782
## D.X.Actor:Male -0.70516 0.420848 -1.6755715 0.0956
## D.X.Partner:Male 0.53753 0.387849 1.3859333 0.1675
##
## Correlation:
## Female Male Fm:X.A Fm:X.P Feml:D F:D.X.A F:D.X.P X.Ac:M
## Male 0.469
## Female:X.Actor -0.411 -0.193
## Female:X.Partner -0.519 -0.243 -0.553
## Female:D -0.937 -0.439 0.416 0.451
## Female:D.X.Actor 0.397 0.186 -0.958 0.531 -0.462
## Female:D.X.Partner 0.504 0.236 0.534 -0.965 -0.493 -0.531
## X.Actor:Male -0.243 -0.519 -0.260 0.469 0.211 0.249 -0.453
## X.Partner:Male -0.193 -0.411 0.469 -0.260 0.195 -0.449 0.250 -0.554
## D:Male -0.439 -0.937 0.195 0.212 0.469 -0.217 -0.231 0.451
## D.X.Actor:Male 0.236 0.504 0.251 -0.453 -0.231 -0.249 0.469 -0.965
## D.X.Partner:Male 0.186 0.397 -0.449 0.249 -0.217 0.469 -0.249 0.531
## X.Pr:M D:Male D.X.A:
## Male
## Female:X.Actor
## Female:X.Partner
## Female:D
## Female:D.X.Actor
## Female:D.X.Partner
## X.Actor:Male
## X.Partner:Male
## D:Male 0.416
## D.X.Actor:Male 0.535 -0.492
## D.X.Partner:Male -0.958 -0.462 -0.531
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -3.4394734 -0.5035567 0.1594487 0.6236175 2.2589803
##
## Residual standard error: 10.61073
## Degrees of freedom: 188 total; 176 residual
We estimate a linear model to investigate if the actor and partner effect of persons’ mean enacted response on the person’s happiness is moderated by the average number of beeps that dyadic partners spend together.
# Estimate APIM model for indistinguishable partners with moderation effects
fit.Model.6 = gls(Y.happy ~ 1 + X.Actor + X.Partner +
D + D.X.Actor + D.X.Partner,
correlation = corSymm(form= ~1|dyad.ID),
weights = varIdent(form= ~1|Gender),
data = data.dyad.mean,
na.action = na.exclude)
summary(fit.Model.6)## Generalized least squares fit by REML
## Model: Y.happy ~ 1 + X.Actor + X.Partner + D + D.X.Actor + D.X.Partner
## Data: data.dyad.mean
## AIC BIC logLik
## 1409.872 1438.708 -695.9358
##
## Correlation Structure: General
## Formula: ~1 | dyad.ID
## Parameter estimate(s):
## Correlation:
## 1
## 2 0.441
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## 0 1
## 1.0000000 0.9661392
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 37.59179 17.087024 2.200020 0.0291
## X.Actor 0.56331 0.168680 3.339541 0.0010
## X.Partner -0.30904 0.167738 -1.842416 0.0670
## D -1.56972 25.673801 -0.061141 0.9513
## D.X.Actor -0.22589 0.245951 -0.918438 0.3596
## D.X.Partner 0.37661 0.245240 1.535692 0.1264
##
## Correlation:
## (Intr) X.Actr X.Prtn D D.X.Ac
## X.Actor -0.667
## X.Partner -0.666 -0.086
## D -0.937 0.619 0.620
## D.X.Actor 0.640 -0.953 0.082 -0.676
## D.X.Partner 0.638 0.083 -0.952 -0.677 -0.058
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -3.5709077 -0.5288216 0.1101519 0.6349044 2.6867816
##
## Residual standard error: 10.59265
## Degrees of freedom: 188 total; 182 residual
We estimate a model for a single person. As an illustration, we select the data for women participants and we investigate if enacted response predicts happy.
# Estimate the model assuming errors are independent
fit.Model.7.A = lm(Y.happy ~ 1 + X.Actor,
data = data.dyad[which(data.dyad$subject.ID==6),])
summary(fit.Model.7.A)##
## Call:
## lm(formula = Y.happy ~ 1 + X.Actor, data = data.dyad[which(data.dyad$subject.ID ==
## 6), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.350 -11.399 -2.109 13.239 27.810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.3042 29.4769 1.435 0.156
## X.Actor 0.2098 0.3155 0.665 0.508
##
## Residual standard error: 15.81 on 64 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.006862, Adjusted R-squared: -0.008655
## F-statistic: 0.4422 on 1 and 64 DF, p-value: 0.5084
tab_model(fit.Model.7.A)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
42.30 |
-16.58 – 101.19 |
0.156 |
|
X Actor |
0.21 |
-0.42 – 0.84 |
0.508 |
|
Observations |
66 |
||
|
R2 / R2 adjusted |
0.007 / -0.009 |
||
# Plot predictions for the person-mean centered enacted response for 5 persons
ggpredict(fit.Model.7.A, terms = c("X.Actor")) |> plot()
# Estimate the model assuming errors follow AR(1) process
fit.Model.7.B = gls(Y.happy ~ 1 + X.Actor, correlation = corAR1(form=~1),
data = data.dyad[which(data.dyad$subject.ID==6),], na.action=na.omit)
summary(fit.Model.7.B)## Generalized least squares fit by REML
## Model: Y.happy ~ 1 + X.Actor
## Data: data.dyad[which(data.dyad$subject.ID == 6), ]
## AIC BIC logLik
## 535.2094 543.8449 -263.6047
##
## Correlation Structure: AR(1)
## Formula: ~1
## Parameter estimate(s):
## Phi
## 0.5352299
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 14.764857 25.771604 0.5729118 0.5687
## X.Actor 0.507076 0.273214 1.8559684 0.0681
##
## Correlation:
## (Intr)
## X.Actor -0.99
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -1.75784823 -0.76252586 -0.08924836 0.99728044 1.76737527
##
## Residual standard error: 16.18123
## Degrees of freedom: 66 total; 64 residual
We estimate an AR(1) model for a single participant.
fit.Model.8 = lm(Y.happy ~ 1 + Y.happy.lag,
data = data.dyad[which(data.dyad$subject.ID==6),])
summary(fit.Model.8)##
## Call:
## lm(formula = Y.happy ~ 1 + Y.happy.lag, data = data.dyad[which(data.dyad$subject.ID ==
## 6), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.144 -8.304 0.894 9.760 26.397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.9600 7.3525 3.803 0.000361 ***
## Y.happy.lag 0.5669 0.1167 4.856 1.03e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.57 on 55 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.3001, Adjusted R-squared: 0.2873
## F-statistic: 23.58 on 1 and 55 DF, p-value: 1.032e-05
tab_model(fit.Model.8)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
27.96 |
13.23 – 42.69 |
<0.001 |
|
Y happy lag |
0.57 |
0.33 – 0.80 |
<0.001 |
|
Observations |
57 |
||
|
R2 / R2 adjusted |
0.300 / 0.287 |
||
# Plot predictions for the person-mean centered enacted response for 5 persons
ggpredict(fit.Model.8, terms = c("Y.happy.lag")) |> plot()
Firstly, we estimate a model for a single persons intensive longitudinal design. As an illustration, we select the data for women participants and we investigate if enacted response predicts happy.
8.1 Linear mixed-effects model to estimate the effect of enacted response on happy assuming Level 1 errors are independent
We consider the women, and we estimate a linear mixed-effects model assuming the Level 1 errors are independent.
fit.Model.9 = lme(fixed = Y.happy ~ 1 + X.Actor.c,
random = ~ 1 + X.Actor.c|subject.ID,
data = data.dyad[which(data.dyad$Gender=='M'),], na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.9)## Linear mixed-effects model fit by REML
## Data: data.dyad[which(data.dyad$Gender == "M"), ]
## AIC BIC logLik
## 46494.16 46533.76 -23241.08
##
## Random effects:
## Formula: ~1 + X.Actor.c | subject.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## (Intercept) 11.841049 (Intr)
## X.Actor.c 0.229827 0.064
## Residual 16.856556
##
## Fixed effects: Y.happy ~ 1 + X.Actor.c
## Value Std.Error DF t-value p-value
## (Intercept) 62.97267 1.2427217 5333 50.67319 0
## X.Actor.c 0.31412 0.0310157 5333 10.12783 0
## Correlation:
## (Intr)
## X.Actor.c 0.048
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.79330627 -0.49866163 0.09391017 0.60635013 3.80155090
##
## Number of Observations: 5428
## Number of Groups: 94
tab_model(fit.Model.9)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
62.97 |
60.54 – 65.41 |
<0.001 |
|
X Actor c |
0.31 |
0.25 – 0.37 |
<0.001 |
|
Random Effects |
|||
|
σ2 |
284.14 |
||
|
τ00 subject.ID |
140.21 |
||
|
τ11 subject.ID.X.Actor.c |
0.05 |
||
|
ρ01 subject.ID |
0.06 |
||
|
ICC |
0.35 |
||
|
N subject.ID |
94 |
||
|
Observations |
5428 |
||
|
Marginal R2 / Conditional R2 |
0.043 / 0.375 |
||
# Plot predictions for the person-mean centered enacted response for 5 persons
ggpredict(fit.Model.9, terms = c("X.Actor.c", "subject.ID [811,810,804]"), type = "random") |> plot()
8.2 Linear mixed-effects model to estimate the effect of enacted response on happy assuming Level 1 errors follow an Autorregressive (AR(1)) model
We consider the women, and we estimate a linear mixed-effects model assuming the Level 1 errors follow and AR(1) process.
fit.Model.10 = lme(fixed = Y.happy ~ 1 + X.Actor.c,
random = ~ 1 + X.Actor.c|subject.ID,
corr = corAR1(),
data = data.dyad[which(data.dyad$Gender=='M'),], na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.10)## Linear mixed-effects model fit by REML
## Data: data.dyad[which(data.dyad$Gender == "M"), ]
## AIC BIC logLik
## 45774.94 45821.13 -22880.47
##
## Random effects:
## Formula: ~1 + X.Actor.c | subject.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## (Intercept) 11.5664744 (Intr)
## X.Actor.c 0.1801181 0.088
## Residual 17.1436922
##
## Correlation Structure: AR(1)
## Formula: ~1 | subject.ID
## Parameter estimate(s):
## Phi
## 0.3746919
## Fixed effects: Y.happy ~ 1 + X.Actor.c
## Value Std.Error DF t-value p-value
## (Intercept) 63.04149 1.241241 5333 50.78908 0
## X.Actor.c 0.25109 0.026434 5333 9.49866 0
## Correlation:
## (Intr)
## X.Actor.c 0.059
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.56349832 -0.50025726 0.09552875 0.61481281 3.11810604
##
## Number of Observations: 5428
## Number of Groups: 94
tab_model(fit.Model.10)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
63.04 |
60.61 – 65.47 |
<0.001 |
|
X Actor c |
0.25 |
0.20 – 0.30 |
<0.001 |
|
Random Effects |
|||
|
σ2 |
293.91 |
||
|
τ00 subject.ID |
133.78 |
||
|
τ11 subject.ID.X.Actor.c |
0.03 |
||
|
ρ01 subject.ID |
0.09 |
||
|
ICC |
0.32 |
||
|
N subject.ID |
94 |
||
|
Observations |
5428 |
||
|
Marginal R2 / Conditional R2 |
0.028 / 0.342 |
||
# Plot predictions for the person-mean centered enacted response for 5 persons
ggpredict(fit.Model.10, terms = c("X.Actor.c", "subject.ID [811,810,804]"), type = "random") |> plot()
8.3 Linear mixed-effects model to estimate the effect of enacted response on happy assuming Level 1 errors are independent including moderation effects
We consider the women, and we estimate a linear mixed-effects model assuming the Level 1 errors are independent and including moderation effects of a time-varying dummy variable indicating whether partners where together in the moment of the assessment.
fit.Model.11 = lme(fixed = Y.happy ~ 1 + X.Actor.c + D + X.Actor.c*D,
random = ~ 1 + X.Actor.c + D|subject.ID,
data = data.dyad[which(data.dyad$Gender=='M'),], na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.11)## Linear mixed-effects model fit by REML
## Data: data.dyad[which(data.dyad$Gender == "M"), ]
## AIC BIC logLik
## 43965.57 44037.57 -21971.78
##
## Random effects:
## Formula: ~1 + X.Actor.c + D | subject.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## (Intercept) 12.0163929 (Intr) X.Act.
## X.Actor.c 0.2266308 0.11
## D 7.0841993 -0.28 -0.30
## Residual 16.4261894
##
## Fixed effects: Y.happy ~ 1 + X.Actor.c + D + X.Actor.c * D
## Value Std.Error DF t-value p-value
## (Intercept) 60.11914 1.3186985 5053 45.58976 0
## X.Actor.c 0.18833 0.0396116 5053 4.75432 0
## D 4.32581 0.9238511 5053 4.68237 0
## X.Actor.c:D 0.19214 0.0396783 5053 4.84256 0
## Correlation:
## (Intr) X.Act. D
## X.Actor.c 0.086
## D -0.381 -0.167
## X.Actor.c:D -0.027 -0.627 0.010
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.87094305 -0.48430754 0.08383557 0.59187470 3.49175473
##
## Number of Observations: 5150
## Number of Groups: 94
tab_model(fit.Model.11)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
60.12 |
57.53 – 62.70 |
<0.001 |
|
X Actor c |
0.19 |
0.11 – 0.27 |
<0.001 |
|
D |
4.33 |
2.51 – 6.14 |
<0.001 |
|
X Actor c * D |
0.19 |
0.11 – 0.27 |
<0.001 |
|
Random Effects |
|||
|
σ2 |
269.82 |
||
|
τ00 subject.ID |
144.39 |
||
|
τ11 subject.ID.X.Actor.c |
0.05 |
||
|
τ11 subject.ID.D |
50.19 |
||
|
ρ01 |
0.11 |
||
|
-0.28 |
|||
|
ICC |
0.37 |
||
|
N subject.ID |
94 |
||
|
Observations |
5150 |
||
|
Marginal R2 / Conditional R2 |
0.059 / 0.403 |
||
# Plot predictions for the person-mean centered enacted response for 5 persons
ggpredict(fit.Model.10, terms = c("X.Actor.c", "subject.ID [811,810,804]"), type = "random") |> plot()
Next, we estimate a multilevel AR(1) model using a linear mixed-effects model for women.
fit.Model.12 = lme(fixed = Y.happy ~ 1 + Y.happy.lag,
random = ~ 1 + Y.happy.lag|subject.ID,
data = data.dyad[which(data.dyad$Gender=='M'),], na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.12)## Linear mixed-effects model fit by REML
## Data: data.dyad[which(data.dyad$Gender == "M"), ]
## AIC BIC logLik
## 38257.09 38295.62 -19122.54
##
## Random effects:
## Formula: ~1 + Y.happy.lag | subject.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## (Intercept) 11.3325530 (Intr)
## Y.happy.lag 0.1560351 -0.838
## Residual 15.6965118
##
## Fixed effects: Y.happy ~ 1 + Y.happy.lag
## Value Std.Error DF t-value p-value
## (Intercept) 35.16224 1.5045429 4454 23.37071 0
## Y.happy.lag 0.44760 0.0215689 4454 20.75221 0
## Correlation:
## (Intr)
## Y.happy.lag -0.888
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.42617148 -0.46931246 0.08405591 0.57015702 4.61786596
##
## Number of Observations: 4549
## Number of Groups: 94
tab_model(fit.Model.12)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
(Intercept) |
35.16 |
32.21 – 38.11 |
<0.001 |
|
Y happy lag |
0.45 |
0.41 – 0.49 |
<0.001 |
|
Random Effects |
|||
|
σ2 |
246.38 |
||
|
τ00 subject.ID |
128.43 |
||
|
τ11 subject.ID.Y.happy.lag |
0.02 |
||
|
ρ01 subject.ID |
-0.84 |
||
|
ICC |
0.17 |
||
|
N subject.ID |
94 |
||
|
Observations |
4549 |
||
|
Marginal R2 / Conditional R2 |
0.234 / 0.361 |
||
We estimate the longitudinal APIM using ESM data.
We estimate the L-APIM using linear mixed-effects models for distinguishable partners.
fit.Model.13 = lme(fixed = Y.happy ~ -1 + Female + Female:X.Actor.c + Female:X.Partner.c +
Male + Male:X.Actor.c + Male:X.Partner.c,
random = ~ -1 + Female + Male |dyad.ID,
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.13)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 88628.18 88715.06 -44302.09
##
## Random effects:
## Formula: ~-1 + Female + Male | dyad.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## Female 11.83800 Female
## Male 11.85367 0.351
## Residual 18.05502
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.2263809
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9429955
## Fixed effects: Y.happy ~ -1 + Female + Female:X.Actor.c + Female:X.Partner.c + Male + Male:X.Actor.c + Male:X.Partner.c
## Value Std.Error DF t-value p-value
## Female 62.42505 1.2470215 10204 50.05932 0
## Male 63.02523 1.2457716 10204 50.59132 0
## Female:X.Actor.c 0.34897 0.0176373 10204 19.78607 0
## Female:X.Partner.c 0.08547 0.0178220 10204 4.79579 0
## X.Actor.c:Male 0.28383 0.0168072 10204 16.88750 0
## X.Partner.c:Male 0.14460 0.0166336 10204 8.69306 0
## Correlation:
## Female Male F:X.A. F:X.P. X.A.:M
## Male 0.346
## Female:X.Actor.c 0.000 0.000
## Female:X.Partner.c 0.001 0.000 -0.131
## X.Actor.c:Male 0.000 0.001 -0.030 0.226
## X.Partner.c:Male 0.000 0.000 0.226 -0.030 -0.131
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.77735205 -0.53067129 0.06530498 0.63679587 4.09250772
##
## Number of Observations: 10303
## Number of Groups: 94
tab_model(fit.Model.13)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
Female |
62.43 |
59.98 – 64.87 |
<0.001 |
|
Male |
63.03 |
60.58 – 65.47 |
<0.001 |
|
Female * X Actor c |
0.35 |
0.31 – 0.38 |
<0.001 |
|
Female * X Partner c |
0.09 |
0.05 – 0.12 |
<0.001 |
|
X Actor c * Male |
0.28 |
0.25 – 0.32 |
<0.001 |
|
X Partner c * Male |
0.14 |
0.11 – 0.18 |
<0.001 |
|
Random Effects |
|||
|
σ2 |
325.98 |
||
|
τ00 |
|
||
|
τ00 |
|
||
|
τ11 dyad.ID.Male |
140.51 |
||
|
ρ01 dyad.ID |
0.35 |
||
|
ICC |
0.30 |
||
|
N dyad.ID |
94 |
||
|
Observations |
10303 |
||
|
Marginal R2 / Conditional R2 |
0.052 / 0.337 |
||
We estimate the L-APIM using linear mixed-effects models for indistinguishable partners.
fit.Model.14 = lme(fixed = Y.happy ~ 1 + X.Actor.c + X.Partner.c,
random = list(dyad.ID = pdCompSymm(~ Gender -1)),
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.14)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 88621.06 88678.98 -44302.53
##
## Random effects:
## Formula: ~Gender - 1 | dyad.ID
## Structure: Compound Symmetry
## StdDev Corr
## GenderF 11.82966
## GenderM 11.82966 0.355
## Residual 18.06366
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.227246
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.000000 0.942975
## Fixed effects: Y.happy ~ 1 + X.Actor.c + X.Partner.c
## Value Std.Error DF t-value p-value
## (Intercept) 62.72811 1.0228125 10207 61.32905 0
## X.Actor.c 0.31511 0.0119893 10207 26.28228 0
## X.Partner.c 0.11667 0.0119820 10207 9.73732 0
## Correlation:
## (Intr) X.Act.
## X.Actor.c 0.000
## X.Partner.c 0.000 0.098
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.74277797 -0.53104361 0.06490643 0.64563509 4.23851584
##
## Number of Observations: 10303
## Number of Groups: 94
We estimate L-APIM including moderation effects of a dyad-level time-varying variable on the actor and partner effects.
##L-APIM for distinguishable dyads with moderation effects
# Create variables including interaction between predictor enacted response and the time-varying moderation time spend together
data.dyad$D.X.Actor.c = data.dyad$D* data.dyad$X.Actor.c
data.dyad$D.X.Partner.c = data.dyad$D* data.dyad$X.Partner.c
# Estimate L-APIM with moderation effects
fit.Model.15 = lme(fixed = Y.happy ~ -1 + Female + Female:X.Actor.c + Female:X.Partner.c +
+ Female:D + Female:D.X.Actor.c + Female:D.X.Actor.c +
Male + Male:X.Actor.c + Male:X.Partner.c +
+ Male:D + Male:D.X.Actor.c + Male:D.X.Actor.c,
random = ~ -1 + Female + Male |dyad.ID,
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.15)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 88519.71 88635.54 -44243.86
##
## Random effects:
## Formula: ~-1 + Female + Male | dyad.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## Female 11.69163 Female
## Male 11.75162 0.328
## Residual 17.89983
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.2141135
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9455494
## Fixed effects: Y.happy ~ -1 + Female + Female:X.Actor.c + Female:X.Partner.c + +Female:D + Female:D.X.Actor.c + Female:D.X.Actor.c + Male + Male:X.Actor.c + Male:X.Partner.c + +Male:D + Male:D.X.Actor.c + Male:D.X.Actor.c
## Value Std.Error DF t-value p-value
## Female 59.33392 1.2832740 10200 46.23636 0.0000
## Male 60.57244 1.2813122 10200 47.27376 0.0000
## Female:X.Actor.c 0.22549 0.0303327 10200 7.43378 0.0000
## Female:X.Partner.c 0.07374 0.0177191 10200 4.16161 0.0000
## Female:D 4.82943 0.5745518 10200 8.40557 0.0000
## Female:D.X.Actor.c 0.17041 0.0369152 10200 4.61626 0.0000
## X.Actor.c:Male 0.20681 0.0273643 10200 7.55777 0.0000
## X.Partner.c:Male 0.13624 0.0165721 10200 8.22124 0.0000
## D:Male 3.83438 0.5437269 10200 7.05204 0.0000
## D.X.Actor.c:Male 0.10915 0.0345510 10200 3.15922 0.0016
## Correlation:
## Female Male F:X.A. F:X.P. Feml:D F:D.X. X.A.:M X.P.:M
## Male 0.316
## Female:X.Actor.c 0.028 0.003
## Female:X.Partner.c 0.022 0.004 -0.065
## Female:D -0.280 -0.057 -0.065 -0.075
## Female:D.X.Actor.c -0.022 -0.001 -0.816 -0.010 0.035
## X.Actor.c:Male 0.003 0.032 0.007 0.131 -0.011 -0.020
## X.Partner.c:Male 0.004 0.017 0.124 -0.027 -0.014 -0.001 -0.061
## D:Male -0.060 -0.266 -0.009 -0.016 0.215 0.001 -0.077 -0.064
## D.X.Actor.c:Male -0.001 -0.025 -0.021 0.000 0.001 0.026 -0.791 -0.020
## D:Male
## Male
## Female:X.Actor.c
## Female:X.Partner.c
## Female:D
## Female:D.X.Actor.c
## X.Actor.c:Male
## X.Partner.c:Male
## D:Male
## D.X.Actor.c:Male 0.040
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.81870485 -0.52908832 0.05837108 0.63816340 3.76552568
##
## Number of Observations: 10303
## Number of Groups: 94
tab_model(fit.Model.15)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
Female |
59.33 |
56.82 – 61.85 |
<0.001 |
|
Male |
60.57 |
58.06 – 63.08 |
<0.001 |
|
Female * X Actor c |
0.23 |
0.17 – 0.28 |
<0.001 |
|
Female * X Partner c |
0.07 |
0.04 – 0.11 |
<0.001 |
|
Female * D |
4.83 |
3.70 – 5.96 |
<0.001 |
|
Female * D X Actor c |
0.17 |
0.10 – 0.24 |
<0.001 |
|
X Actor c * Male |
0.21 |
0.15 – 0.26 |
<0.001 |
|
X Partner c * Male |
0.14 |
0.10 – 0.17 |
<0.001 |
|
D * Male |
3.83 |
2.77 – 4.90 |
<0.001 |
|
D X Actor c * Male |
0.11 |
0.04 – 0.18 |
0.002 |
|
Random Effects |
|||
|
σ2 |
320.40 |
||
|
τ00 |
|
||
|
τ00 |
|
||
|
τ11 dyad.ID.Male |
138.10 |
||
|
ρ01 dyad.ID |
0.33 |
||
|
ICC |
0.30 |
||
|
N dyad.ID |
94 |
||
|
Observations |
10303 |
||
|
Marginal R2 / Conditional R2 |
0.063 / 0.344 |
||
fit.Model.16 = lme(fixed = Y.happy ~ 1 + X.Actor.c + X.Partner.c +
+ D + D.X.Actor.c + D.X.Partner.c,
random = list(dyad.ID = pdCompSymm(~ Gender -1)),
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.16)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 88494.62 88574.26 -44236.31
##
## Random effects:
## Formula: ~Gender - 1 | dyad.ID
## Structure: Compound Symmetry
## StdDev Corr
## GenderF 11.69559
## GenderM 11.69559 0.338
## Residual 17.87489
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.2140127
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9471027
## Fixed effects: Y.happy ~ 1 + X.Actor.c + X.Partner.c + +D + D.X.Actor.c + D.X.Partner.c
## Value Std.Error DF t-value p-value
## (Intercept) 59.86932 1.0414303 10204 57.48759 0.0000
## X.Actor.c 0.20713 0.0204554 10204 10.12602 0.0000
## X.Partner.c 0.02943 0.0205126 10204 1.43458 0.1514
## D 4.36701 0.4352674 10204 10.03294 0.0000
## D.X.Actor.c 0.15175 0.0256247 10204 5.92203 0.0000
## D.X.Partner.c 0.11877 0.0256425 10204 4.63171 0.0000
## Correlation:
## (Intr) X.Act. X.Prt. D D.X.A.
## X.Actor.c 0.031
## X.Partner.c 0.031 0.109
## D -0.262 -0.077 -0.076
## D.X.Actor.c -0.023 -0.812 -0.088 0.038
## D.X.Partner.c -0.023 -0.088 -0.813 0.038 0.097
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -4.81437775 -0.52893676 0.05914612 0.64125756 3.75156307
##
## Number of Observations: 10303
## Number of Groups: 94
We estimate a VAR(1)-based models for the partners using linear mixed-effects models.
fit.Model.17 = lme(fixed = Y.happy ~ -1 + Female +
Female:Y.Actor.lag + Female:Y.Partner.lag +
Male + Male:Y.Actor.lag + Male:Y.Partner.lag,
random = ~ -1 + Female + Male |dyad.ID,
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.17)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 72617.95 72702.65 -36296.97
##
## Random effects:
## Formula: ~-1 + Female + Male | dyad.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## Female 6.103995 Female
## Male 6.316127 0.042
## Residual 16.587331
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.1814789
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9601722
## Fixed effects: Y.happy ~ -1 + Female + Female:Y.Actor.lag + Female:Y.Partner.lag + Male + Male:Y.Actor.lag + Male:Y.Partner.lag
## Value Std.Error DF t-value p-value
## Female 28.483855 1.2664571 8497 22.49098 0
## Male 29.911782 1.2388790 8497 24.14423 0
## Female:Y.Actor.lag 0.456698 0.0136888 8497 33.36291 0
## Female:Y.Partner.lag 0.093657 0.0146641 8497 6.38683 0
## Y.Actor.lag:Male 0.439102 0.0141337 8497 31.06775 0
## Y.Partner.lag:Male 0.092962 0.0131854 8497 7.05037 0
## Correlation:
## Female Male F:Y.A. F:Y.P. Y.A.:M
## Male 0.136
## Female:Y.Actor.lag -0.473 -0.079
## Female:Y.Partner.lag -0.544 -0.090 -0.272
## Y.Actor.lag:Male -0.091 -0.530 -0.048 0.169
## Y.Partner.lag:Male -0.080 -0.462 0.171 -0.048 -0.278
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.68638978 -0.48207390 0.07434082 0.59426129 4.03303254
##
## Number of Observations: 8596
## Number of Groups: 94
tab_model(fit.Model.17)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
Female |
28.48 |
26.00 – 30.97 |
<0.001 |
|
Male |
29.91 |
27.48 – 32.34 |
<0.001 |
|
Female * Y Actor lag |
0.46 |
0.43 – 0.48 |
<0.001 |
|
Female * Y Partner lag |
0.09 |
0.06 – 0.12 |
<0.001 |
|
Y Actor lag * Male |
0.44 |
0.41 – 0.47 |
<0.001 |
|
Y Partner lag * Male |
0.09 |
0.07 – 0.12 |
<0.001 |
|
Random Effects |
|||
|
σ2 |
275.14 |
||
|
τ00 |
|
||
|
τ00 |
|
||
|
τ11 dyad.ID.Male |
39.89 |
||
|
ρ01 dyad.ID |
0.04 |
||
|
ICC |
0.12 |
||
|
N dyad.ID |
94 |
||
|
Observations |
8596 |
||
|
Marginal R2 / Conditional R2 |
0.262 / 0.353 |
||
fit.Model.18 = fit.Model.2 = lme(fixed = Y.happy ~ 1 + Y.Actor.lag + Y.Partner.lag,
random = list(dyad.ID = pdCompSymm(~ Gender -1)),
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.18)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 72600.49 72656.96 -36292.24
##
## Random effects:
## Formula: ~Gender - 1 | dyad.ID
## Structure: Compound Symmetry
## StdDev Corr
## GenderF 6.186447
## GenderM 6.186447 0.05
## Residual 16.580557
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.1813624
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9608526
## Fixed effects: Y.happy ~ 1 + Y.Actor.lag + Y.Partner.lag
## Value Std.Error DF t-value p-value
## (Intercept) 29.195838 0.9412369 8500 31.01859 0
## Y.Actor.lag 0.448422 0.0095934 8500 46.74298 0
## Y.Partner.lag 0.092802 0.0095642 8500 9.70300 0
## Correlation:
## (Intr) Y.Act.
## Y.Actor.lag -0.566
## Y.Partner.lag -0.564 -0.110
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.69242918 -0.48201831 0.07417099 0.59343946 4.06217998
##
## Number of Observations: 8596
## Number of Groups: 94
This model incorporates interaction effects between the lagged predictors of the two partners and a time-varying predictor. The model allows estimating moderation effects in the auto- and cross-regressive effects.
# Create variables including interaction between lagged predictor and the time-varying moderation time spend together
data.dyad$D.Y.Actor.lag = data.dyad$D*data.dyad$Y.Actor.lag
data.dyad$D.Y.Partner.lag = data.dyad$D*data.dyad$Y.Partner.lag
# Estimate VAR(1) model
fit.Model.19 = lme(fixed = Y.happy ~ -1 + Female +
Female:Y.Actor.lag + Female:Y.Partner.lag +
Female:D + Female:D.Y.Actor.lag + Female:D.Y.Partner.lag +
Male + Male:Y.Actor.lag + Male:Y.Partner.lag +
Male:D + Male:D.Y.Actor.lag + Male:D.Y.Partner.lag,
random = ~ -1 + Female + Male |dyad.ID,
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.19)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 70291.66 70418.14 -35127.83
##
## Random effects:
## Formula: ~-1 + Female + Male | dyad.ID
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## Female 5.997208 Female
## Male 6.340239 0.017
## Residual 16.393312
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.1792322
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9667957
## Fixed effects: Y.happy ~ -1 + Female + Female:Y.Actor.lag + Female:Y.Partner.lag + Female:D + Female:D.Y.Actor.lag + Female:D.Y.Partner.lag + Male + Male:Y.Actor.lag + Male:Y.Partner.lag + Male:D + Male:D.Y.Actor.lag + Male:D.Y.Partner.lag
## Value Std.Error DF t-value p-value
## Female 30.409093 1.8844468 8228 16.136880 0.0000
## Male 30.548583 1.8439558 8228 16.566874 0.0000
## Female:Y.Actor.lag 0.480722 0.0221902 8228 21.663765 0.0000
## Female:Y.Partner.lag 0.002169 0.0221770 8228 0.097820 0.9221
## Female:D -2.164813 2.1325014 8228 -1.015152 0.3101
## Female:D.Y.Actor.lag -0.054652 0.0264488 8228 -2.066331 0.0388
## Female:D.Y.Partner.lag 0.146864 0.0273841 8228 5.363116 0.0000
## Y.Actor.lag:Male 0.451559 0.0214866 8228 21.015812 0.0000
## Y.Partner.lag:Male 0.045689 0.0214972 8228 2.125358 0.0336
## D:Male 0.197127 2.0642681 8228 0.095495 0.9239
## D.Y.Actor.lag:Male -0.032031 0.0265058 8228 -1.208454 0.2269
## D.Y.Partner.lag:Male 0.066684 0.0255946 8228 2.605405 0.0092
## Correlation:
## Female Male F:Y.A. F:Y.P. Feml:D F:D.Y.A F:D.Y.P
## Male 0.157
## Female:Y.Actor.lag -0.588 -0.102
## Female:Y.Partner.lag -0.607 -0.106 -0.142
## Female:D -0.738 -0.130 0.486 0.498
## Female:D.Y.Actor.lag 0.463 0.081 -0.782 0.101 -0.576
## Female:D.Y.Partner.lag 0.454 0.080 0.101 -0.744 -0.631 -0.209
## Y.Actor.lag:Male -0.107 -0.600 -0.024 0.175 0.089 0.018 -0.132
## Y.Partner.lag:Male -0.104 -0.582 0.176 -0.024 0.086 -0.139 0.018
## D:Male -0.131 -0.729 0.086 0.089 0.177 -0.102 -0.112
## D.Y.Actor.lag:Male 0.081 0.449 0.018 -0.132 -0.112 -0.037 0.177
## D.Y.Partner.lag:Male 0.082 0.457 -0.139 0.018 -0.102 0.178 -0.037
## Y.A.:M Y.P.:M D:Male D.Y.A.
## Male
## Female:Y.Actor.lag
## Female:Y.Partner.lag
## Female:D
## Female:D.Y.Actor.lag
## Female:D.Y.Partner.lag
## Y.Actor.lag:Male
## Y.Partner.lag:Male -0.142
## D:Male 0.497 0.485
## D.Y.Actor.lag:Male -0.743 0.101 -0.631
## D.Y.Partner.lag:Male 0.101 -0.781 -0.576 -0.209
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.74554990 -0.49382616 0.07164591 0.59296796 4.15742434
##
## Number of Observations: 8333
## Number of Groups: 94
tab_model(fit.Model.19)|
|
Y.happy |
||
|---|---|---|---|
|
Predictors |
Estimates |
CI |
p |
|
Female |
30.41 |
26.72 – 34.10 |
<0.001 |
|
Male |
30.55 |
26.93 – 34.16 |
<0.001 |
|
Female * Y Actor lag |
0.48 |
0.44 – 0.52 |
<0.001 |
|
Female * Y Partner lag |
0.00 |
-0.04 – 0.05 |
0.922 |
|
Female * D |
-2.16 |
-6.35 – 2.02 |
0.310 |
|
Female * D Y Actor lag |
-0.05 |
-0.11 – -0.00 |
0.039 |
|
Female * D Y Partner lag |
0.15 |
0.09 – 0.20 |
<0.001 |
|
Y Actor lag * Male |
0.45 |
0.41 – 0.49 |
<0.001 |
|
Y Partner lag * Male |
0.05 |
0.00 – 0.09 |
0.034 |
|
D * Male |
0.20 |
-3.85 – 4.24 |
0.924 |
|
D Y Actor lag * Male |
-0.03 |
-0.08 – 0.02 |
0.227 |
|
D Y Partner lag * Male |
0.07 |
0.02 – 0.12 |
0.009 |
|
Random Effects |
|||
|
σ2 |
268.74 |
||
|
τ00 |
|
||
|
τ00 |
|
||
|
τ11 dyad.ID.Male |
40.20 |
||
|
ρ01 dyad.ID |
0.02 |
||
|
ICC |
0.12 |
||
|
N dyad.ID |
94 |
||
|
Observations |
8333 |
||
|
Marginal R2 / Conditional R2 |
0.275 / 0.365 |
||
fit.Model.20 = fit.Model.2 = lme(fixed = Y.happy ~ 1 + Y.Actor.lag + Y.Partner.lag +
D + D.Y.Actor.lag + D.Y.Partner.lag,
random = list(dyad.ID = pdCompSymm(~ Gender -1)),
correlation = corCompSymm(form = ~1|dyad.ID/Obs),
weights = varIdent(form = ~1|Gender),
data = data.dyad, na.action=na.omit,
control=list(msVerbose=FALSE, maxIter=500, msMaxIter=500))
summary(fit.Model.20)## Linear mixed-effects model fit by REML
## Data: data.dyad
## AIC BIC logLik
## 70267.46 70344.76 -35122.73
##
## Random effects:
## Formula: ~Gender - 1 | dyad.ID
## Structure: Compound Symmetry
## StdDev Corr
## GenderF 6.149968
## GenderM 6.149968 0.031
## Residual 16.388263
##
## Correlation Structure: Compound symmetry
## Formula: ~1 | dyad.ID/Obs
## Parameter estimate(s):
## Rho
## 0.1788796
## Variance function:
## Structure: Different standard deviations per stratum
## Formula: ~1 | Gender
## Parameter estimates:
## F M
## 1.0000000 0.9677423
## Fixed effects: Y.happy ~ 1 + Y.Actor.lag + Y.Partner.lag + D + D.Y.Actor.lag + D.Y.Partner.lag
## Value Std.Error DF t-value p-value
## (Intercept) 30.468482 1.4180257 8234 21.486552 0.0000
## Y.Actor.lag 0.466729 0.0152414 8234 30.622515 0.0000
## Y.Partner.lag 0.023313 0.0152416 8234 1.529555 0.1262
## D -0.831104 1.6058916 8234 -0.517534 0.6048
## D.Y.Actor.lag -0.043226 0.0183377 8234 -2.357204 0.0184
## D.Y.Partner.lag 0.104420 0.0183159 8234 5.701050 0.0000
## Correlation:
## (Intr) Y.Act. Y.Prt. D D.Y.A.
## Y.Actor.lag -0.658
## Y.Partner.lag -0.658 0.035
## D -0.742 0.542 0.542
## D.Y.Actor.lag 0.510 -0.769 -0.036 -0.669
## D.Y.Partner.lag 0.510 -0.037 -0.770 -0.668 -0.031
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.77349921 -0.49387880 0.07457649 0.59239824 4.20974901
##
## Number of Observations: 8333
## Number of Groups: 94
Laurenceau, Jean-Philippe, and Niall Bolger. 2012. “Analyzing Diary and Intensive Longitudinal Data from Dyads.” Handbook of Research Methods for Studying Daily Life, 407–22.
Sels, Laura, Eva Ceulemans, and Peter Kuppens. 2019. “All’s Well That Ends Well? A Test of the Peak-End Rule in Couples’ Conflict Discussions.” European Journal of Social Psychology 49 (4): 794–806.