```
# NOT RUN {
# }
# NOT RUN {
prep <- estimateEffect(1:3 ~ treatment, gadarianFit, gadarian)
plot(prep, "treatment", model=gadarianFit,
method="pointestimate")
plot(prep, "treatment", model=gadarianFit,
method="difference",cov.value1=1,cov.value2=0)
#If the covariate were a binary factor,
#the factor labels can be used to
#specify the values of cov.value1 (e.g., cov.value1="treat").
# String variables must be turned to factors prior to plotting.
#If you see this error, Error in rep.int(c(1, numeric(n)), n - 1L) :
# invalid 'times' value, then you likely have not done this.
#Example of binary times binary interaction
gadarian$binaryvar <- sample(c(0,1), nrow(gadarian), replace=T)
temp <- textProcessor(gadarian$open.ended.response,metadata=gadarian)
out <- prepDocuments(temp$documents, temp$vocab, temp$meta)
stm1 <- stm(out$documents, out$vocab, 3, prevalence=~treatment*binaryvar,
data=gadarian)
prep <- estimateEffect(c(2) ~ treatment*binaryvar, stmobj=stm1,
metadata=gadarian)
par(mfrow=c(1,2))
plot(prep, "treatment", method="pointestimate",
cov.value1=1, cov.value2=0, xlim=c(-1,1), moderator="binaryvar", moderator.value=1)
plot(prep, "treatment", method="pointestimate",
cov.value1=1, cov.value2=0, xlim=c(-1,1), moderator="binaryvar",
moderator.value=0)
# }
```

Run the code above in your browser using DataCamp Workspace