Main, Paxton, & Dale Data Analysis

Transcription

1 Main, Paxton, & Dale Data Analysis Interest/Validation setwd('/main_paxton_dale-analyses') # preliminaries rm(list=ls()) getwd() [1] "/Main_Paxton_Dale-Analyses" source('globals_functions.r')

2 Loading required package: Matrix Loading required package: tserieschaos Loading required package: desolve Attaching package: 'desolve' The following object is masked from 'package:graphics': matplot Loading required package: fields Loading required package: spam Loading required package: grid Spam version ( ) is loaded. Type 'help( Spam)' or 'demo( spam)' for a short introduction and overview of this package. Help for individual functions is also obtained by adding the suffix '.spam' to the function name, e.g. 'help( chol.spam)'. Attaching package: 'spam' The following objects are masked from 'package:base': backsolve, forwardsolve Loading required package: maps # ATTENTION: maps v3.0 has an updated 'world' map. # # Many country borders and names have changed since # # Type '?world' or 'news(package="maps")'. See README_v3. # Loading required package: plot3d Loading required package: pracma Attaching package: 'pracma' The following object is masked from 'package:desolve': rk4 The following objects are masked from 'package:matrix': expm, lu, tril, triu

3 # specify parents' and adolescents' affect a_target_emotion = c(int,val) p_target_emotion = c(int,val) # run RQA source('get_rqa_measures.r')

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5 Let s look at the location of maximum lags. If one or the other is leading, we will find maximum lag location to be more on the left (parent leading) or right (adolescent leading). In general we do not find this imbalance: A one-sample t-test with maximum observed lag as the dependent variable is not significant less or greater than 0. In fact, it is centered around 0 (see plots); interestingly, in the interest/validation case, the maximum lags do show a bimodality, suggesting the turn taking pattern holds even in the maximum lag value. # only include those for which a maximum RR is observed descriptives = descriptives[descriptives$maxrec>0,] # print it out pander(t.test(descriptives[descriptives$shuff=='observed',]$maxlag)) One Sample t-test: descriptives[descriptives$shuff == "observed", ]$maxlag Test statistic df P value Alternative hypothesis two.sided # plot interaction with satisfaction -- use PDF if we want to save as a file in a sub directory # pdf(file='plots/interestvalidation_satisfaction.pdf',width=8,height=5.25) source('plot_drps_satisfaction.r')

6 # dev.off() # plot interaction with age -- use PDF if we want to save as a file in a subdirectory # pdf(file='plots/interestvalidation_age.pdf',width=8,height=5.25) source('plot_drps_age.r')

7 # dev.off() # print model results using standardized and unstandardized coefficients source('lmer_stats.r') pander(coefs.simple) # simple (just lag terms): standardized (Intercept) e e-11 1 LinearLag QuadLag pander(coefs.simple.raw) # simple (just lag terms): unstandardized (Intercept) e-05 LinearLag

8 QuadLag pander(coefs.satisfaction) # lag terms and satisfaction: standardized (Intercept) e e-12 1 Satisfaction LinearLag QuadLag SatisXLinear SatisXQuad pander(coefs.satisfaction.raw) # lag terms and satisfaction: unstandardized (Intercept) Satisfaction LinearLag QuadLag Satisfaction:LinearLag Satisfaction:QuadLag pander(coefs.age) # lag terms and age: standardized (Intercept) e e-11 1 Age LinearLag QuadLag AgeXLinear AgeXQuad

9 pander(coefs.age.raw) # lag terms and age: unstandardized (Intercept) Age LinearLag QuadLag Age:LinearLag Age:QuadLag Negative Emotion # preliminaries rm(list=ls()) source('globals_functions.r') # set the target emotion for these analyses and then run RQA a_target_emotion = neg p_target_emotion = neg source('get_rqa_measures.r')

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11 # only include those for which a maximum RR is observed descriptives = descriptives[descriptives$maxrec>0,] # print it out pander(t.test(descriptives[descriptives$shuff=='observed',]$maxlag)) One Sample t-test: descriptives[descriptives$shuff == "observed", ]$maxlag Test statistic df P value Alternative hypothesis two.sided # plot interaction with satisfaction -- use PDF if we want to save as a file in a sub directory # pdf(file='plots/negative_satisfaction.pdf',width=8,height=5.25) source('plot_drps_satisfaction.r')

12 # dev.off() # plot interaction with age -- use PDF if we want to save as a file in a subdirectory # pdf(file='plots/negative_age.pdf',width=8,height=5.25) source('plot_drps_age.r') # dev.off() # print model results using standardized and unstandardized ("raw") coefficients source('lmer_stats.r') pander(coefs.simple) # simple (just lag terms): standardized (Intercept) e e-11 1 LinearLag QuadLag pander(coefs.simple.raw) # simple (just lag terms): unstandardized

13 (Intercept) e-07 LinearLag QuadLag pander(coefs.satisfaction) # lag terms and satisfaction: standardized (Intercept) e e-11 1 Satisfaction LinearLag QuadLag SatisXLinear SatisXQuad pander(coefs.satisfaction.raw) # lag terms and satisfaction: unstandardized (Intercept) e-05 Satisfaction LinearLag QuadLag Satisfaction:LinearLag Satisfaction:QuadLag pander(coefs.age) # lag terms and age: standardized (Intercept) e e-11 1 Age LinearLag QuadLag

14 AgeXLinear AgeXQuad pander(coefs.age.raw) # lag terms and age: unstandardized (Intercept) Age LinearLag QuadLag Age:LinearLag Age:QuadLag Positive Emotion # preliminaries rm(list=ls()) source('globals_functions.r') # set the target emotion for these analyses and then run RQA a_target_emotion = pos p_target_emotion = pos source('get_rqa_measures.r')

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16 # only include those for which a maximum RR is observed descriptives = descriptives[descriptives$maxrec>0,] # print it out pander(t.test(descriptives[descriptives$shuff=='observed',]$maxlag)) One Sample t-test: descriptives[descriptives$shuff == "observed", ]$maxlag Test statistic df P value Alternative hypothesis two.sided # plot interaction with satisfaction -- use PDF if we want to save as a file in a sub directory # pdf(file='plots/positive_satisfaction.pdf',width=8,height=5.25) source('plot_drps_satisfaction.r')

17 # dev.off() # plot interaction with age -- use PDF if we want to save as a file in a subdirectory # pdf(file='plots/positive_age.pdf',width=8,height=5.25) source('plot_drps_age.r') # dev.off() # print model results using standardized and unstandardized ("raw") coefficients source('lmer_stats.r') pander(coefs.simple) # simple (just lag terms): standardized (Intercept) 2.632e e-12 1 LinearLag QuadLag e-05 pander(coefs.simple.raw) # simple (just lag terms): unstandardized

18 (Intercept) LinearLag QuadLag e-05 pander(coefs.satisfaction) # lag terms and satisfaction: standardized (Intercept) 2.788e e-12 1 Satisfaction LinearLag QuadLag SatisXLinear SatisXQuad pander(coefs.satisfaction.raw) # lag terms and satisfaction: unstandardized (Intercept) Satisfaction LinearLag QuadLag Satisfaction:LinearLag Satisfaction:QuadLag pander(coefs.age) # lag terms and age: standardized (Intercept) 3.156e e-12 1 Age LinearLag QuadLag

19 AgeXLinear AgeXQuad pander(coefs.age.raw) # lag terms and age: unstandardized (Intercept) Age LinearLag QuadLag Age:LinearLag Age:QuadLag Correlation between Dyad Satisfaction and Adolescent Age Given systematic similarities between the age and satisfaction models, we checked for but did not find a reliable correlation between adolescent age and dyad satisfaction score. # grab only one slice from each dyad corr_check = drps_raw[drps_raw$rawlag==0,] pander(cor.test(corr_check$satisfaction,corr_check$age)) Pearson s product-moment correlation: corr_check$satisfaction and corr_check$age Test statistic df P value Alternative hypothesis two.sided