Anna_APA_2019

Loading packages

library(dplyr)

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## Attaching package: 'dplyr'

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library(tidyverse)

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library(lme4)

## Loading required package: Matrix

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## Attaching package: 'Matrix'

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library(lmerTest)

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## Attaching package: 'lmerTest'

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library(sjstats)
library(sjPlot)

## #refugeeswelcome

library(jmRtools)
library(MuMIn)
library(konfound)

## Sensitivity analysis as described in Frank, Maroulis, Duong, and Kelcey (2013) and in Frank (2000).
## For more information visit https://jmichaelrosenberg.shinyapps.io/shinykonfound/.

library(haven)

Loading data

Anna_Practicum <- read_sav("PRACTICUM MASTER LONG.sav")

Predicting SI

M1<-lmer(SI_comp ~ Day + 
            cond + 
            Day*cond + 
            T1PIcomp + 
            T1AUTcomp + 
            (1|StudyID) + (1|Day), data = Anna_Practicum)
summary(M1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SI_comp ~ Day + cond + Day * cond + T1PIcomp + T1AUTcomp + (1 |  
##     StudyID) + (1 | Day)
##    Data: Anna_Practicum
## 
## REML criterion at convergence: 1815.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4154 -0.5236  0.0614  0.5457  3.9219 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  StudyID  (Intercept) 0.362011 0.60167 
##  Day      (Intercept) 0.004259 0.06526 
##  Residual             0.370687 0.60884 
## Number of obs: 832, groups:  StudyID, 129; Day, 8
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   1.733323   0.350856 139.203778   4.940  2.2e-06 ***
## Day          -0.075497   0.021995  20.404340  -3.432 0.002579 ** 
## cond          0.001341   0.151388 275.375786   0.009 0.992941    
## T1PIcomp      0.269479   0.071607 125.282999   3.763 0.000256 ***
## T1AUTcomp     0.252415   0.067992 124.838372   3.712 0.000308 ***
## Day:cond      0.066307   0.022311 705.084024   2.972 0.003060 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) Day    cond   T1PIcm T1AUTc
## Day       -0.252                            
## cond      -0.238  0.414                     
## T1PIcomp  -0.543 -0.003  0.065              
## T1AUTcomp -0.558  0.006 -0.129 -0.290       
## Day:cond   0.179 -0.703 -0.584  0.003 -0.006

ranova(M1)

## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## SI_comp ~ Day + cond + T1PIcomp + T1AUTcomp + (1 | StudyID) + 
##     (1 | Day) + Day:cond
##               npar   logLik    AIC     LRT Df Pr(>Chisq)    
## <none>           9  -907.68 1833.4                          
## (1 | StudyID)    8 -1062.68 2141.4 309.996  1     <2e-16 ***
## (1 | Day)        8  -908.97 1833.9   2.572  1     0.1088    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

sjstats::icc(M1)

## 
## Linear mixed model
## 
## Family : gaussian (identity)
## Formula: SI_comp ~ Day + cond + Day * cond + T1PIcomp + T1AUTcomp + (1 | StudyID) + (1 | Day)
## 
##   ICC (StudyID): 0.4912
##       ICC (Day): 0.0058

#sjPlot::sjp.int(M1, type = "eff")
#konfound::konfound(M33, interest_c)
#konfound::konfound(M33, ch_time)
#konfound::konfound(M33, female)
#M33r<-r2glmm::r2beta(M33, method = "nsj")
#M33r
#MuMIn::r.squaredGLMM(M33)
#plot(M33r)