heplots (version 1.6.2)

SocialCog: Social Cognitive Measures in Psychiatric Groups

Description

The general purpose of the study (Hartman, 2016, Heinrichs et al. (2015)) was to evaluate patterns and levels of performance on neurocognitive measures among individuals with schizophrenia and schizoaffective disorder using a well-validated, comprehensive neurocognitive battery specifically designed for individuals with psychosis (Heinrichs et al. (2008))

Format

A data frame with 139 observations on the following 5 variables.

Dx

Diagnostic group, a factor with levels Schizophrenia, Schizoaffective, Control

MgeEmotions

Score on the Managing emotions test, a numeric vector

ToM

Score on the The Reading the Mind in the Eyes test (theory of mind), a numeric vector

ExtBias

Externalizing Bias score, a numeric vector

PersBias

Personal Bias score, a numeric vector

Details

The data here are for a subset of the observations in NeuroCog for which measures on various scales of social cognition were also available. Interest here is on whether the schizophrenia group can be distinguished from the schizoaffective group on these measures.

The Social Cognitive measures were designed to tap various aspects of the perception and cognitive procession of emotions of others. Emotion perception was assessed using a Managing Emotions (MgeEmotions) score from the MCCB. A "theory of mind" (ToM) score assessed ability to read the emotions of others from photographs of the eye region of male and female faces. Two other measures, externalizing bias (ExtBias) and personalizing bias (PersBias) were calculated from a scale measuring the degree to which individuals attribute internal, personal or situational causal attributions to positive and negative social events.

See NeuroCog for a description of the sample. Only those with complete data on all the social cognitive measures are included in this data set.

There is one extreme outlier in the schizophrenia group and other possible outliers in the control group, left in here for tutorial purposes.

Examples

library(car)
data(SocialCog)
SC.mod <- lm(cbind(MgeEmotions, ToM, ExtBias, PersBias) ~ Dx, data=SocialCog)
SC.mod
car::Anova(SC.mod)

# test hypotheses of interest in terms of contrasts
print(linearHypothesis(SC.mod, "Dx1"), SSP=FALSE)
print(linearHypothesis(SC.mod, "Dx2"), SSP=FALSE)

#' ## HE plots

heplot(SC.mod, hypotheses=list("Dx1"="Dx1", "Dx2"="Dx2"),
	fill=TRUE, fill.alpha=.1)
	
pairs(SC.mod, fill=c(TRUE,FALSE), fill.alpha=.1)