vignettes/My Examples/Vignette_DiDiSTATIS_Dowling_balanced_Arousal_Valence.R

In michaelkriegsman/DiDiSTATIS: Run DiDiSTATIS and related methods

# Vignette_DiDiSTATIS_Dowling_balanced_Arousal_Valence

rm(list=ls())
library(DiDiSTATIS)

## 1. Initialize DATA and DESIGNs
source('C:/Users/Michael A. Kriegsman/Box Sync/Dissertation/RStudio2Git/DiDiSTATIS/vignettes/My Examples/Initialize_Balanced_Dowling_ArousalValence.R')


## 2. Analyze (Preprocessing is done according to what the 'data_are')
res_DiDiSTATIS <- DiDiSTATIS(DATA = Sort_balanced, data_are = 'sort', n2k = NULL,
                             DESIGN_rows = DESIGN$DESIGN_rows,
                             DESIGN_tables = DESIGN$DESIGN_tables,
                             MFA1_Flag = TRUE, RV1_Flag = TRUE,
                             MFA2_Flag = FALSE, RV2_Flag = TRUE, #currently, Bootstrap is breaking on MFA2_Flag==FALSE
                             Perm_omni_sort = T, Perm_omni_sort_niter=1000,
                             Boot_tables = T, Boot_tables_niter = 1000,
                             LOO_rows = T, LOO_rows_multiplier=2,
                             SH_rows = T, SH_rows_niter=100)


library(PlotDiDiSTATIS)
#Factor Scores
Plot_DiDiSTATIS_F.B..(res_DiDiSTATIS)
Plot_DiDiSTATIS_F.B.D(res_DiDiSTATIS)

Plot_DiDiSTATIS_FAB..(res_DiDiSTATIS)
Plot_DiDiSTATIS_FaB.d(res_DiDiSTATIS, priority = "ab", connect = c(8, 14, 31), quiet_B.. = TRUE)

#Effect Sizes
res_DiDiSTATIS$res_BaryGrand$EffectSize$r2_Categories
res_DiDiSTATIS$res_BaryGrand$EffectSize$r2_Groups
res_DiDiSTATIS$res_BaryGrand$EffectSize$r2_BD_ABCD
res_DiDiSTATIS$res_BaryGrand$EffectSize$r2_Plain_Disc_..
#Not sure how really to interpret these guys...
# res_DiDiSTATIS$res_BaryGrand$EffectSize$r2_Plain_Disc_.d
# res_DiDiSTATIS$res_BaryGrand$EffectSize$r2_Plain_Disc_cd

#Quality of Prediction
PlotConfusion(res_DiDiSTATIS$res_BaryGrand$Prediction_Fixed_Rows$Confusion_Rows_norm)

PlotConfusion(res_DiDiSTATIS$res_BaryGrand$Prediction_Fixed_Rows$Confusion_norm.d[,,1])
Plot_DiDiSTATIS_FaB.d(res_DiDiSTATIS, priority = "d", connect = 1, quiet_B.. = F)

PlotConfusion(res_DiDiSTATIS$res_BaryGrand$Prediction_Fixed_Rows$Confusion_norm.d[,,2])
Plot_DiDiSTATIS_FaB.d(res_DiDiSTATIS, priority = "d", connect = 2, quiet_B.. = F)

PlotConfusion(res_DiDiSTATIS$res_BaryGrand$Prediction_Fixed_Rows$Confusion_norm.d[,,3])
Plot_DiDiSTATIS_FaB.d(res_DiDiSTATIS, priority = "d", connect = 3, quiet_B.. = F)


#Permutation
Plot_DiDiSTATIS_Perm_r2_Plain_Disc_..(res_DiDiSTATIS)
Plot_DiDiSTATIS_Perm_r2_Categories(res_DiDiSTATIS)
Plot_DiDiSTATIS_Perm_r2_Groups(res_DiDiSTATIS)



#Bootstrap
Plot_DiDiSTATIS_Boot_centered_CIs_BD(res_DiDiSTATIS)


#LOO
PlotConfusion(res_DiDiSTATIS$LOO_Rows$Grand$Confusion_rand_norm, is.percent = T)

PlotConfusion(res_DiDiSTATIS$LOO_Rows$Group$Confusion_rand_D_norm[,,1], is.percent = T)
PlotConfusion(res_DiDiSTATIS$LOO_Rows$Group$Confusion_rand_D_norm[,,2], is.percent = T)
PlotConfusion(res_DiDiSTATIS$LOO_Rows$Group$Confusion_rand_D_norm[,,3], is.percent = T)


#SH
PlotConfusion(res_DiDiSTATIS$SH_Rows$Grand$Confusion_rand_norm, is.percent = T)

PlotConfusion(res_DiDiSTATIS$SH_Rows$Group$Confusion_rand_D_norm[,,1], is.percent = T)
PlotConfusion(res_DiDiSTATIS$SH_Rows$Group$Confusion_rand_D_norm[,,2], is.percent = T)
PlotConfusion(res_DiDiSTATIS$SH_Rows$Group$Confusion_rand_D_norm[,,3], is.percent = T)