Key actor-actor and actor-partner effects: Given Affection and its Links to Value Consistent Behavior
Data Overview
Basic descriptives about key actor-parter variables
Between and Within Correlations, Taking into Account Nesting
these are the simple pooled correlations…e.g., average within and between. All correlations are significant, which means on average, actor affection is linked to partner valued action, with smallish average effect
| MPartnerYouShowAffection.wg | MPartnerYouActValueConsistent.wg | FPartnerYouShowAffection.wg | FPartnerYouActValueConsistent.wg | |
|---|---|---|---|---|
| MPartnerYouShowAffection.wg | 1.00 | 0.34 | 0.32 | 0.14 |
| MPartnerYouActValueConsistent.wg | 0.34 | 1.00 | 0.22 | 0.19 |
| FPartnerYouShowAffection.wg | 0.32 | 0.22 | 1.00 | 0.36 |
| FPartnerYouActValueConsistent.wg | 0.14 | 0.19 | 0.36 | 1.00 |
| MPartnerYouShowAffection.wg | MPartnerYouActValueConsistent.wg | FPartnerYouShowAffection.wg | FPartnerYouActValueConsistent.wg | |
|---|---|---|---|---|
| MPartnerYouShowAffection.wg | 0 | 0 | 0 | 0 |
| MPartnerYouActValueConsistent.wg | 0 | 0 | 0 | 0 |
| FPartnerYouShowAffection.wg | 0 | 0 | 0 | 0 |
| FPartnerYouActValueConsistent.wg | 0 | 0 | 0 | 0 |
| MPartnerYouShowAffection.bg | MPartnerYouActValueConsistent.bg | FPartnerYouShowAffection.bg | FPartnerYouActValueConsistent.bg | |
|---|---|---|---|---|
| MPartnerYouShowAffection.bg | 1.00 | 0.47 | 0.38 | 0.32 |
| MPartnerYouActValueConsistent.bg | 0.47 | 1.00 | 0.35 | 0.40 |
| FPartnerYouShowAffection.bg | 0.38 | 0.35 | 1.00 | 0.40 |
| FPartnerYouActValueConsistent.bg | 0.32 | 0.40 | 0.40 | 1.00 |
| MPartnerYouShowAffection.bg | MPartnerYouActValueConsistent.bg | FPartnerYouShowAffection.bg | FPartnerYouActValueConsistent.bg | |
|---|---|---|---|---|
| MPartnerYouShowAffection.bg | 0.00 | 0 | 0 | 0.01 |
| MPartnerYouActValueConsistent.bg | 0.00 | 0 | 0 | 0.00 |
| FPartnerYouShowAffection.bg | 0.00 | 0 | 0 | 0.00 |
| FPartnerYouActValueConsistent.bg | 0.01 | 0 | 0 | 0.00 |
I-ARIMAX
Here is where we run i-arimax (only run once as it is very time consuming)
We can see that whilst the pooled effects are significant, there is huge heterogeneity.
Caterpillar Plots
Here we visualize the heterogeneity. the blue bars show means. these means don’t describe many values.. for some tight link between affection and valued action for others little to no link
## <details><summary><b> MPartnerYouShowAffection_MPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> FPartnerYouShowAffection_MPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> MPartnerYouShowAffection_FPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> FPartnerYouShowAffection_FPartnerYouActValueConsistent </b></summary>
## </details><br>Cluster Identification
Visualization of Clusters identified by PAM (partitioning around medoids). second figure shows ideal number of clusters k = 2. Table shows means and sd of links between the indicated variables for cluster 1 (mean_1, sd_1) and cluster 2 (mean_2, sd_2). In cluster 1, all effects (actor-actor and actor-partner effects) seem to be higher than in cluster 2.
## 19 22 54 1 12 16 17 18 21 23 25 26 27 29 30 31 32 33 35 36 38 40 41 42 44 46
## 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 1 1
## 48 49 50 51 52 53 55 56 58 59 60 61 62 63 64 2 3 4 5 6 7 8 10 11 13 14
## 1 1 2 2 1 2 1 2 1 2 1 2 2 2 1 2 2 1 1 1 1 2 1 2 2 2
## 15
## 1## MPartnerYouShowAffection_predicts_MPartnerYouActValueConsistent_xreg
## 7 0.3682417
## 50 -0.3290244
## FPartnerYouShowAffection_predicts_MPartnerYouActValueConsistent_xreg
## 7 -0.04792348
## 50 -0.16011734
## MPartnerYouShowAffection_predicts_FPartnerYouActValueConsistent_xreg
## 7 0.4035706
## 50 0.0526245
## FPartnerYouShowAffection_predicts_FPartnerYouActValueConsistent_xreg
## 7 0.6929557
## 50 -0.5931348
## [1] 0.2935605 0.2804541 0.1428481 0.2350668 0.2584915
mlVAR on Clusters
contemporaneous and lagged effects.
## Estimating temporal and between-subjects effects## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%## Estimating contemporaneous effects## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%## Computing random effects## | | | 0% | |====== | 9% | |============= | 18% | |=================== | 27% | |========================= | 36% | |================================ | 45% | |====================================== | 55% | |============================================= | 64% | |=================================================== | 73% | |========================================================= | 82% | |================================================================ | 91% | |======================================================================| 100%## Estimating temporal and between-subjects effects## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%## Estimating contemporaneous effects## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%## Computing random effects## | | | 0% | |==== | 6% | |======== | 11% | |============ | 17% | |================ | 22% | |=================== | 28% | |======================= | 33% | |=========================== | 39% | |=============================== | 44% | |=================================== | 50% | |======================================= | 56% | |=========================================== | 61% | |=============================================== | 67% | |=================================================== | 72% | |====================================================== | 78% | |========================================================== | 83% | |============================================================== | 89% | |================================================================== | 94% | |======================================================================| 100%##
## mlVAR estimation completed. Input was:
## - Variables: MPartnerYouShowAffection FPartnerYouShowAffection MPartnerYouActValueConsistent FPartnerYouActValueConsistent
## - Lags: 1
## - Estimator: lmer
## - Temporal: fixed
##
## Information indices:
## var aic bic
## MPartnerYouShowAffection 225.7577 250.1842
## FPartnerYouShowAffection 222.4311 246.8576
## MPartnerYouActValueConsistent 197.5126 221.9391
## FPartnerYouActValueConsistent 106.6535 131.0800
##
##
## Temporal effects:
## from to lag fixed SE
## MPartnerYouShowAffection MPartnerYouShowAffection 1 0.287 0.104
## MPartnerYouShowAffection FPartnerYouShowAffection 1 0.136 0.106
## MPartnerYouShowAffection MPartnerYouActValueConsistent 1 0.136 0.089
## MPartnerYouShowAffection FPartnerYouActValueConsistent 1 -0.020 0.046
## FPartnerYouShowAffection MPartnerYouShowAffection 1 0.085 0.138
## FPartnerYouShowAffection FPartnerYouShowAffection 1 0.019 0.142
## FPartnerYouShowAffection MPartnerYouActValueConsistent 1 0.146 0.119
## FPartnerYouShowAffection FPartnerYouActValueConsistent 1 -0.013 0.061
## MPartnerYouActValueConsistent MPartnerYouShowAffection 1 0.097 0.109
## MPartnerYouActValueConsistent FPartnerYouShowAffection 1 0.103 0.109
## MPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 0.021 0.093
## MPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 -0.017 0.049
## FPartnerYouActValueConsistent MPartnerYouShowAffection 1 -0.128 0.156
## FPartnerYouActValueConsistent FPartnerYouShowAffection 1 -0.091 0.160
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 -0.055 0.136
## FPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 0.057 0.069
## MPartnerYouShowAffection MPartnerYouShowAffection 1 0.287 0.104
## MPartnerYouShowAffection FPartnerYouShowAffection 1 0.136 0.106
## MPartnerYouShowAffection MPartnerYouActValueConsistent 1 0.136 0.089
## MPartnerYouShowAffection FPartnerYouActValueConsistent 1 -0.020 0.046
## FPartnerYouShowAffection MPartnerYouShowAffection 1 0.085 0.138
## FPartnerYouShowAffection FPartnerYouShowAffection 1 0.019 0.142
## FPartnerYouShowAffection MPartnerYouActValueConsistent 1 0.146 0.119
## FPartnerYouShowAffection FPartnerYouActValueConsistent 1 -0.013 0.061
## MPartnerYouActValueConsistent MPartnerYouShowAffection 1 0.097 0.109
## MPartnerYouActValueConsistent FPartnerYouShowAffection 1 0.103 0.109
## MPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 0.021 0.093
## MPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 -0.017 0.049
## FPartnerYouActValueConsistent MPartnerYouShowAffection 1 -0.128 0.156
## FPartnerYouActValueConsistent FPartnerYouShowAffection 1 -0.091 0.160
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 -0.055 0.136
## FPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 0.057 0.069
## P ran_SD
## 0.006 0
## 0.198 0
## 0.126 0
## 0.657 0
## 0.539 0
## 0.892 0
## 0.222 0
## 0.826 0
## 0.375 0
## 0.347 0
## 0.824 0
## 0.736 0
## 0.413 0
## 0.572 0
## 0.686 0
## 0.414 0
## 0.006 0
## 0.198 0
## 0.126 0
## 0.657 0
## 0.539 0
## 0.892 0
## 0.222 0
## 0.826 0
## 0.375 0
## 0.347 0
## 0.824 0
## 0.736 0
## 0.413 0
## 0.572 0
## 0.686 0
## 0.414 0
##
##
## Contemporaneous effects (posthoc estimated):
## v1 v2 P 1->2 P 1<-2
## FPartnerYouShowAffection MPartnerYouShowAffection 0.010 0.020
## MPartnerYouActValueConsistent MPartnerYouShowAffection 0.084 0.182
## MPartnerYouActValueConsistent FPartnerYouShowAffection 0.808 0.487
## FPartnerYouActValueConsistent MPartnerYouShowAffection 0.909 0.995
## FPartnerYouActValueConsistent FPartnerYouShowAffection 0.238 0.243
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 0.357 0.430
## pcor ran_SD_pcor cor ran_SD_cor
## 0.300 0.015 0.326 0.011
## 0.231 0.068 0.265 0.101
## 0.053 0.002 0.147 0.018
## -0.007 0.041 0.064 0.053
## 0.130 0.003 0.149 0.004
## 0.100 0.012 0.120 0.016
##
##
## Between-subject effects:
## v1 v2 P 1->2 P 1<-2 pcor
## FPartnerYouShowAffection MPartnerYouShowAffection 0.380 0.932 NA
## MPartnerYouActValueConsistent MPartnerYouShowAffection 0.724 0.323 NA
## MPartnerYouActValueConsistent FPartnerYouShowAffection 0.170 0.311 NA
## FPartnerYouActValueConsistent MPartnerYouShowAffection 0.671 0.034 NA
## FPartnerYouActValueConsistent FPartnerYouShowAffection 0.500 0.049 NA
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 0.519 0.681 NA
## cor
## NA
## NA
## NA
## NA
## NA
## NA##
## mlVAR estimation completed. Input was:
## - Variables: MPartnerYouShowAffection FPartnerYouShowAffection MPartnerYouActValueConsistent FPartnerYouActValueConsistent
## - Lags: 1
## - Estimator: lmer
## - Temporal: fixed
##
## Information indices:
## var aic bic
## MPartnerYouShowAffection 486.1209 518.953
## FPartnerYouShowAffection 435.9679 468.800
## MPartnerYouActValueConsistent 400.7189 433.551
## FPartnerYouActValueConsistent 465.9600 498.792
##
##
## Temporal effects:
## from to lag fixed SE
## MPartnerYouShowAffection MPartnerYouShowAffection 1 0.036 0.081
## MPartnerYouShowAffection FPartnerYouShowAffection 1 -0.008 0.071
## MPartnerYouShowAffection MPartnerYouActValueConsistent 1 -0.150 0.064
## MPartnerYouShowAffection FPartnerYouActValueConsistent 1 0.000 0.076
## FPartnerYouShowAffection MPartnerYouShowAffection 1 0.059 0.092
## FPartnerYouShowAffection FPartnerYouShowAffection 1 0.184 0.080
## FPartnerYouShowAffection MPartnerYouActValueConsistent 1 0.109 0.072
## FPartnerYouShowAffection FPartnerYouActValueConsistent 1 0.198 0.085
## MPartnerYouActValueConsistent MPartnerYouShowAffection 1 0.119 0.099
## MPartnerYouActValueConsistent FPartnerYouShowAffection 1 0.158 0.086
## MPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 0.230 0.078
## MPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 0.147 0.092
## FPartnerYouActValueConsistent MPartnerYouShowAffection 1 -0.030 0.088
## FPartnerYouActValueConsistent FPartnerYouShowAffection 1 -0.019 0.076
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 0.016 0.068
## FPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 -0.117 0.081
## MPartnerYouShowAffection MPartnerYouShowAffection 1 0.036 0.081
## MPartnerYouShowAffection FPartnerYouShowAffection 1 -0.008 0.071
## MPartnerYouShowAffection MPartnerYouActValueConsistent 1 -0.150 0.064
## MPartnerYouShowAffection FPartnerYouActValueConsistent 1 0.000 0.076
## FPartnerYouShowAffection MPartnerYouShowAffection 1 0.059 0.092
## FPartnerYouShowAffection FPartnerYouShowAffection 1 0.184 0.080
## FPartnerYouShowAffection MPartnerYouActValueConsistent 1 0.109 0.072
## FPartnerYouShowAffection FPartnerYouActValueConsistent 1 0.198 0.085
## MPartnerYouActValueConsistent MPartnerYouShowAffection 1 0.119 0.099
## MPartnerYouActValueConsistent FPartnerYouShowAffection 1 0.158 0.086
## MPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 0.230 0.078
## MPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 0.147 0.092
## FPartnerYouActValueConsistent MPartnerYouShowAffection 1 -0.030 0.088
## FPartnerYouActValueConsistent FPartnerYouShowAffection 1 -0.019 0.076
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 1 0.016 0.068
## FPartnerYouActValueConsistent FPartnerYouActValueConsistent 1 -0.117 0.081
## P ran_SD
## 0.660 0
## 0.908 0
## 0.019 0
## 0.998 0
## 0.522 0
## 0.021 0
## 0.130 0
## 0.020 0
## 0.229 0
## 0.067 0
## 0.003 0
## 0.111 0
## 0.728 0
## 0.802 0
## 0.811 0
## 0.149 0
## 0.660 0
## 0.908 0
## 0.019 0
## 0.998 0
## 0.522 0
## 0.021 0
## 0.130 0
## 0.020 0
## 0.229 0
## 0.067 0
## 0.003 0
## 0.111 0
## 0.728 0
## 0.802 0
## 0.811 0
## 0.149 0
##
##
## Contemporaneous effects (posthoc estimated):
## v1 v2 P 1->2 P 1<-2
## FPartnerYouShowAffection MPartnerYouShowAffection 0.009 0.003
## MPartnerYouActValueConsistent MPartnerYouShowAffection 0.000 0.000
## MPartnerYouActValueConsistent FPartnerYouShowAffection 0.015 0.099
## FPartnerYouActValueConsistent MPartnerYouShowAffection 0.867 0.980
## FPartnerYouActValueConsistent FPartnerYouShowAffection 0.026 0.036
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 0.127 0.071
## pcor ran_SD_pcor cor ran_SD_cor
## 0.263 0.024 0.388 0.011
## 0.330 0.001 0.432 0.002
## 0.205 0.034 0.379 0.018
## -0.005 0.000 0.170 0.005
## 0.220 0.029 0.308 0.026
## 0.179 0.028 0.282 0.015
##
##
## Between-subject effects:
## v1 v2 P 1->2 P 1<-2
## FPartnerYouShowAffection MPartnerYouShowAffection 0.361 0.234
## MPartnerYouActValueConsistent MPartnerYouShowAffection 0.196 0.166
## MPartnerYouActValueConsistent FPartnerYouShowAffection 0.737 0.610
## FPartnerYouActValueConsistent MPartnerYouShowAffection 0.060 0.547
## FPartnerYouActValueConsistent FPartnerYouShowAffection 0.101 0.070
## FPartnerYouActValueConsistent MPartnerYouActValueConsistent 0.009 0.058
## pcor cor
## 0.207 0.661
## 0.294 0.736
## 0.086 0.646
## 0.316 0.762
## 0.354 0.725
## 0.484 0.803Perceptual Agreement per Cluster
Table showing the mean and sd of Missed Cues ARIMAX outputs per Cluster (mean_1 -> Cluster 1).
## # A tibble: 2 × 5
## Variable mean_1 mean_2 sd_1 sd_2
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 FPartnerYouShowAffection_MReceivedAffection_xreg 0.56 0.42 0.33 0.35
## 2 MPartnerYouShowAffection_FReceivedAffection_xreg 0.43 0.42 0.31 0.44Hypothesis 4 | Perceptual Agreement linked to Actor-Partner Effect
These are correlations between: idiographic ARIMAX effects of Actor Giving Affection x Partner Valued Behavior and idiographic ARIMAX effects of Actor Giving Affection x Partner Receiving Affection
Note that “Missed Cues” is a bit misleading here. The higher the ARIMAX effect between Given and Received Affection, the LOWER are Missed Cues. So we can see that the degree to which actors’ and partners’ perception of given and received affection agree correlate with the Affection x Valued Behavior links - but only for the Male-Female link!!!
## correlation p_value
## MAffection_FValues_vs_MAffGiven_FAffReceived 0.42302177 0.001600566
## FAffection_MValues_vs_FAffGiven_MAffReceived 0.04010713 0.775539486ARIMA2X
Here, I included Missed Cues (difference between Given and Received Affection) as second exogenous predictor for the iARIMAX.
## This function will create a dataframe with person-mean standardized variables
## ...
## If all values for a feature within ID are NA, will return NA
## If there is less than two values, will return 0 (zero variance)
## If values are constant, will return 0 (zero variance)
## If there is enough variance, will return deviations from the mean
##
## Standardization finished: Your original dataframe with standardized columns appended at the end was returned## <details><summary><b> MPartnerYouShowAffection_MPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> FMissedCues_MPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> MPartnerYouShowAffection_FPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> FMissedCues_FPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> FPartnerYouShowAffection_MPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> MMissedCues_MPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> FPartnerYouShowAffection_FPartnerYouActValueConsistent </b></summary>
## </details><br><details><summary><b> MMissedCues_FPartnerYouActValueConsistent </b></summary>
## </details><br>ARIMAX Moderator Analysis
Covariates of Association Strength of Actor-Partner Links
These are meta-regressions for actor-actor and actor-partner links and covariates (note that: marital status 1 = unmarried, 2 = married).
Covariates of Trait-Level Affection and Valued Behavior
These are multiple regression models for trait-level Given Affection and Valued Behavior (aggregated per person, across time). Marital status 1 = unmarried, 2 = married.
Covariates of Perceptual Agreement (Difference Scores)
##
## Call:
## lm(formula = physical_contact_mean ~ M_missed_cues_mean + F_missed_cues_mean,
## data = final_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.242 -11.280 -1.235 11.308 34.922
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.9504 2.0945 26.236 <2e-16 ***
## M_missed_cues_mean -0.9233 0.3814 -2.421 0.0187 *
## F_missed_cues_mean -0.5065 0.3697 -1.370 0.1761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.63 on 56 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.1589, Adjusted R-squared: 0.1288
## F-statistic: 5.289 on 2 and 56 DF, p-value: 0.007868##
## Call:
## lm(formula = sexual_activity_mean ~ M_missed_cues_mean + F_missed_cues_mean,
## data = final_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29959 -0.19907 -0.00677 0.10523 0.64523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.232048 0.034054 6.814 7.01e-09 ***
## M_missed_cues_mean 0.005050 0.006201 0.814 0.419
## F_missed_cues_mean 0.001679 0.006011 0.279 0.781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2541 on 56 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.03586, Adjusted R-squared: 0.00143
## F-statistic: 1.042 on 2 and 56 DF, p-value: 0.3597