## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
has_erm <- requireNamespace("eRm", quietly = TRUE)

## ----ppass--------------------------------------------------------------------
library(PP)
data(fourpl_df)
res <- PPass(fourpl_df, items = 3:14,
             thres = attr(fourpl_df, "diffpar"), slopes = attr(fourpl_df, "slopes"),
             lowerA = attr(fourpl_df, "lowerA"))
res

## ----summary------------------------------------------------------------------
s <- summary(res)

## ----plot, fig.width = 6, fig.height = 4.5, fig.alt = "lz* of every person against the WLE estimate, flagged persons highlighted and labelled with their id"----
plot(res, label = "id")

## ----df-----------------------------------------------------------------------
res[res$sex == "w" & res$status == "ok", c("id", "estimate", "SE", "lzstar")][1:5, ]
knitr::kable(head(as.data.frame(res)[, c("id", "sex", "estimate", "SE", "lzstar", "infit")]),
             digits = 3)

## ----jk-----------------------------------------------------------------------
head(JKpp(res)$estimates)

## ----erm-note, echo = FALSE, results = "asis", eval = !has_erm----------------
# cat("*The eRm package is not installed, so the code of this section is shown without results.*\n")

## ----rm, eval = has_erm-------------------------------------------------------
library(eRm)
rm1 <- RM(raschdat1)
res_rm <- PPass(rm1)
res_rm

## ----rm-mle, eval = has_erm---------------------------------------------------
res_mle <- PPass(rm1, type = "mle", fitindices = "lzstar")
pp <- person.parameter(rm1)
ok <- res_mle$status == "ok"
max(abs(res_mle$estimate[ok] - pp$theta.table[ok, "Person Parameter"]))

## ----pcm, eval = has_erm------------------------------------------------------
set.seed(2751)
thres <- rbind(c(-2, -1.2, -0.5, 0), c(-1, 0.3, 1, 1.5), c(0.5, NA, NA, 2))
x <- sim_gpcm(thres = thres, alpha = 1, theta = rnorm(300))
res_pcm <- PPass(PCM(x), type = "map")
summary(res_pcm$estimate)

## ----lpcm, eval = has_erm-----------------------------------------------------
x2 <- sim_gpcm(thres = thres, alpha = 1, theta = rnorm(300, 0.5))
lpcm <- LPCM(cbind(x, x2), mpoints = 2, groupvec = rep(1:2, 150))
res_lpcm <- PPass(lpcm)
res_lpcm

## ----pv, eval = has_erm-------------------------------------------------------
set.seed(1)
pv <- PV(res_pcm, npv = 5)
head(pv$pvdraws, 3)