Justin

]]>prob.females<-plogis(samples$b_Intercept)

prob.males<-plogis(samples$b_Intercept + samples$b_sexM)

diffprob.female))

]]>library(brms)

options(mc.cores = parallel::detectCores())

a<-rep(0,43)

b<-rep(1,9)

male<-c(a,b)

a<-rep(0,44)

b<-rep(1,4)

female<-c(a,b)

handedness<-c(male,female)

a<-rep("M",52)

b<-rep("F",48)

sex<-c(a,b)

data<-cbind(sex,handedness)

data<-data.frame(data)

str(data)

model<-brm(handedness~sex, data=data, family=bernoulli())

samples<-posterior_samples(model)

prob.females<-plogis(samples$b_Intercept)

prob.males<-plogis(samples$b_Intercept + samples$b_sexM)

diffprob.female))

#0.9175

library(brms)

options(mc.cores = parallel::detectCores())

a<-rep(0,43)

b<-rep(1,9)

male<-c(a,b)

a<-rep(0,44)

b<-rep(1,4)

female<-c(a,b)

handedness<-c(male,female)

a<-rep("M",52)

b<-rep("F",48)

sex<-c(a,b)

data<-cbind(sex,handedness)

data<-data.frame(data)

str(data)

model<-brm(handedness~sex, data=data, family=bernoulli())

samples<-posterior_samples(model)

prob.females<-plogis(samples$b_Intercept)

prob.males<-plogis(samples$b_Intercept + samples$b_sexM)

diffprob.female))

]]>For goodness of fit, you can also just do a binned chi-square test in most situations.

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