tam2mirt(tamobj)

## Not run: 
library(TAM)
library(mirt)

#############################################################################
# EXAMPLE 1: Estimations in TAM for data.read dataset
#############################################################################

data(data.read)
dat <- data.read

#**************************************
#*** Model 1: Rasch model
#**************************************

# estimation in TAM package
mod <- TAM::tam.mml( dat )
summary(mod)
# conversion to mirt
res <- sirt::tam2mirt(mod)
# generated lavaan syntax
cat(res$lavaan.syntax.fixed)
cat(res$lavaan.syntax.freed)
# extract object of class mirt
mres <- res$mirt
# print and parameter values
print(mres)
mirt::mod2values(mres)
# model fit
mirt::M2(mres)
# residual statistics
mirt::residuals(mres, type="Q3")
mirt::residuals(mres, type="LD")
# item fit
mirt::itemfit(mres)
# person fit
mirt::personfit(mres)
# compute several types of factor scores (quite slow)
f1 <- mirt::fscores(mres, method='WLE',response.pattern=dat[1:10,])
     # method=MAP and EAP also possible
# item plot
mirt::itemplot(mres,"A3")    # item A3
mirt::itemplot(mres,4)       # fourth item
# some more plots
plot(mres,type="info")
plot(mres,type="score")
plot(mres,type="trace")
# compare estimates with estimated Rasch model in mirt
mres1 <- mirt::mirt(dat,1,"Rasch" )
print(mres1)
mirt.wrapper.coef(mres1)

#**************************************
#*** Model 2: 2PL model
#**************************************

# estimation in TAM
mod <- TAM::tam.mml.2pl( dat )
summary(mod)
# conversion to mirt
res <- sirt::tam2mirt(mod)
mres <- res$mirt
# lavaan syntax
cat(res$lavaan.syntax.fixed)
cat(res$lavaan.syntax.freed)
# parameter estimates
print(mres)
mod2values(mres)
mres@nest   # number of estimated parameters
# some plots
plot(mres,type="info")
plot(mres,type="score")
plot(mres,type="trace")
# model fit
mirt::M2(mres)
# residual statistics
mirt::residuals(mres, type="Q3")
mirt::residuals(mres, type="LD")
# item fit
mirt::itemfit(mres)

#**************************************
#*** Model 3: 3-dimensional Rasch model
#**************************************

# define Q-matrix
Q <- matrix( 0, nrow=12, ncol=3 )
Q[ cbind(1:12, rep(1:3,each=4) ) ] <- 1
rownames(Q) <- colnames(dat)
colnames(Q) <- c("A","B","C")
# estimation in TAM
mod <- TAM::tam.mml( resp=dat, Q=Q, control=list(snodes=1000,maxiter=30) )
summary(mod)
# mirt conversion
res <- sirt::tam2mirt(mod)
mres <- res$mirt
# mirt syntax
cat(res$mirt.syntax)
  ##   Dim01=1,2,3,4
  ##   Dim02=5,6,7,8
  ##   Dim03=9,10,11,12
  ##   COV=Dim01*Dim01,Dim02*Dim02,Dim03*Dim03,Dim01*Dim02,Dim01*Dim03,Dim02*Dim03
  ##   MEAN=Dim01,Dim02,Dim03
# lavaan syntax
cat(res$lavaan.syntax.freed)
  ##   Dim01=~ 1*A1+1*A2+1*A3+1*A4
  ##   Dim02=~ 1*B1+1*B2+1*B3+1*B4
  ##   Dim03=~ 1*C1+1*C2+1*C3+1*C4
  ##   A1 | t1_1*t1
  ##   A2 | t1_2*t1
  ##   A3 | t1_3*t1
  ##   A4 | t1_4*t1
  ##   B1 | t1_5*t1
  ##   B2 | t1_6*t1
  ##   B3 | t1_7*t1
  ##   B4 | t1_8*t1
  ##   C1 | t1_9*t1
  ##   C2 | t1_10*t1
  ##   C3 | t1_11*t1
  ##   C4 | t1_12*t1
  ##   Dim01 ~ 0*1
  ##   Dim02 ~ 0*1
  ##   Dim03 ~ 0*1
  ##   Dim01 ~~ Cov_11*Dim01
  ##   Dim02 ~~ Cov_22*Dim02
  ##   Dim03 ~~ Cov_33*Dim03
  ##   Dim01 ~~ Cov_12*Dim02
  ##   Dim01 ~~ Cov_13*Dim03
  ##   Dim02 ~~ Cov_23*Dim03
# model fit
mirt::M2(mres)
# residual statistics
residuals(mres,type="LD")
# item fit
mirt::itemfit(mres)

#**************************************
#*** Model 4: 3-dimensional 2PL model
#**************************************

# estimation in TAM
mod <- TAM::tam.mml.2pl( resp=dat, Q=Q, control=list(snodes=1000,maxiter=30) )
summary(mod)
# mirt conversion
res <- sirt::tam2mirt(mod)
mres <- res$mirt
# generated lavaan syntax
cat(res$lavaan.syntax.fixed)
cat(res$lavaan.syntax.freed)
# write lavaan syntax on disk
  sink( "mod4_lav_freed.txt", split=TRUE )
cat(res$lavaan.syntax.freed)
  sink()
# some statistics from mirt
print(mres)
summary(mres)
mirt::M2(mres)
mirt::residuals(mres)
mirt::itemfit(mres)

# estimate mirt model by using the generated lavaan syntax with freed parameters
res2 <- sirt::lavaan2mirt( dat, res$lavaan.syntax.freed,
            technical=list(NCYCLES=3), verbose=TRUE)
                 # use only few cycles for illustrational purposes
mirt.wrapper.coef(res2$mirt)
summary(res2$mirt)
print(res2$mirt)

#############################################################################
# EXAMPLE 4: mirt conversions for polytomous dataset data.big5
#############################################################################

data(data.big5)
# select some items
items <- c( grep( "O", colnames(data.big5), value=TRUE )[1:6],
     grep( "N", colnames(data.big5), value=TRUE )[1:4] )
# O3 O8 O13 O18 O23 O28 N1 N6 N11 N16
dat <- data.big5[, items ]
library(psych)
psych::describe(dat)

library(TAM)
#******************
#*** Model 1: Partial credit model in TAM
mod1 <- TAM::tam.mml( dat[,1:6] )
summary(mod1)
# convert to mirt object
mmod1 <- sirt::tam2mirt( mod1 )
rmod1 <- mmod1$mirt
# coefficients in mirt
coef(rmod1)
mirt.wrapper.coef(rmod1)
# model fit
mirt::M2(rmod1)
# item fit
mirt::itemfit(rmod1)
# plots
plot(rmod1,type="trace")
plot(rmod1, type="trace", which.items=1:4 )
mirt::itemplot(rmod1,"O3")

#******************
#*** Model 2: Generalized partial credit model in TAM
mod2 <- TAM::tam.mml.2pl( dat[,1:6], irtmodel="GPCM" )
summary(mod2)
# convert to mirt object
mmod2 <- sirt::tam2mirt( mod2 )
rmod2 <- mmod2$mirt
# coefficients in mirt
mirt.wrapper.coef(rmod2)
# model fit
mirt::M2(rmod2)
# item fit
mirt::itemfit(rmod2)

## End(Not run)