ppc.plot: Plot ppc.step2step3 output

In Replication: Test Replications by Means of the Prior Predictive p-Value

Description

Creates a plot using the output of ppc.step2step3.

Usage

ppc.plot(llratio.s, llratio.r)

Arguments

llratio.s	A vector with llratio values for the predicted data.
llratio.r	The llratio value for the new data

Value

Returns a plot histogram of llratio values for the predicted data, and indicates the value of llratio for the new data with a red line. If llratio is equal to 0 for at least 10% of the llratio for the predicted data, the function will create a seperate thick black line for this group of values.

Author(s)

Zondervan-Zwijnenburg, M.A.J.

Examples

#the following example can be used, but may take >10 seconds

#create data
rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) }

# simple regression -------------------------------------------------------

set.seed(9)
#step 1 input
#create/load data
n.o=30 #sample size original data
y.o <- data.frame(y=rnorm2(n.o,0,1),x=rnorm2(n.o,3,1))
#y.o <- correlate(as.matrix(y.o), corm=.70); y.o <- data.frame(y=y.o[,1],x=y.o[,2])
n.r=50 #sample size new data
y.r <- data.frame(y=rnorm2(n.r,0.5,1),x=rnorm2(n.r,3,1))

#blavaan model
model <- '
y ~ x     #regression
y ~1      #intercept not default in lavaan (but is in blavaan)
'

#Warning: This is a minimal example;
step1.reg <- ppc.step1(y.o=y.o,model=model,nchains=2,n.r=50)

print(step1.reg$pT)
#H0: #reg > est, int = est       =       B1>0.302 & B0= -0.878
pT <- step1.reg$pT #parameter table
int.id <- which(pT$lhs=="y"&pT$op=="~1"&pT$rhs=="") #identify B0
reg.id <- which(pT$lhs=="y"&pT$op=="~"&pT$rhs=="x") #identify B1
hyp <- cbind(pT[c(int.id,reg.id),"plabel"],c("=",">"),c(pT[c(int.id,reg.id),"est"]))
print(hyp)
H0 <- paste(hyp[,1],hyp[,2],hyp[,3],collapse="&")

step23.reg <- ppc.step2step3(step1=step1.reg,y.r=y.r,model=model,H0)

ppc.plot(step23.reg$llratio.s,step23.reg$llratio.r)

Replication documentation built on April 14, 2020, 6:15 p.m.