# extract coefficients
mirt.wrapper.coef(mirt.obj)

# summary output
mirt_summary(object, digits=4, file=NULL, ...)

# extract posterior, likelihood, ...
mirt.wrapper.posterior(mirt.obj, weights=NULL, group=NULL)
## S3 method for class 'SingleGroupClass'
IRT.likelihood(object, ...)
## S3 method for class 'MultipleGroupClass'
IRT.likelihood(object, ...)
## S3 method for class 'SingleGroupClass'
IRT.posterior(object, ...)
## S3 method for class 'MultipleGroupClass'
IRT.posterior(object, ...)
## S3 method for class 'SingleGroupClass'
IRT.expectedCounts(object, ...)
## S3 method for class 'MultipleGroupClass'
IRT.expectedCounts(object, ...)

# S3 method for extracting item response functions
## S3 method for class 'SingleGroupClass'
IRT.irfprob(object, ...)
## S3 method for class 'MultipleGroupClass'
IRT.irfprob(object, group=1, ...)

# compute factor scores
mirt.wrapper.fscores(mirt.obj, weights=NULL)

# convenience function for itemplot
mirt.wrapper.itemplot( mirt.obj, ask=TRUE, ...)

## Not run: 
# A development version can be installed from GitHub
if (FALSE){ # default is set to FALSE, use the installed version
   library(devtools)
   devtools::install_github("philchalmers/mirt")
          }
# now, load mirt
library(mirt)

#############################################################################
# EXAMPLE 1: Extracting item parameters and posterior LSAT data
#############################################################################

data(LSAT7, package="mirt")
data <- mirt::expand.table(LSAT7)

#*** Model 1: 3PL model for item 5 only, other items 2PL
mod1 <- mirt::mirt(data, 1, itemtype=c("2PL","2PL","2PL","2PL","3PL"), verbose=TRUE)
print(mod1)
summary(mod1)
# extracting coefficients
coef(mod1)
mirt.wrapper.coef(mod1)$coef
# summary output
mirt_summary(mod1)
# extract parameter values in mirt
mirt::mod2values(mod1)
# extract posterior
post1 <- sirt::mirt.wrapper.posterior(mod1)
# extract item response functions
probs1 <- IRT.irfprob(mod1)
str(probs1)
# extract individual likelihood
likemod1 <- IRT.likelihood(mod1)
str(likemod1)
# extract individual posterior
postmod1 <- IRT.posterior(mod1)
str(postmod1)

#*** Model 2: Confirmatory model with two factors
cmodel <- mirt::mirt.model("
        F1=1,4,5
        F2=2,3
        ")
mod2 <- mirt::mirt(data, cmodel, verbose=TRUE)
print(mod2)
summary(mod2)
# extract coefficients
coef(mod2)
mirt.wrapper.coef(mod2)$coef
# extract posterior
post2 <- sirt::mirt.wrapper.posterior(mod2)

#############################################################################
# EXAMPLE 2: Extracting item parameters and posterior for differering
#            number of response catagories | Dataset Science
#############################################################################

data(Science,package="mirt")
library(psych)
psych::describe(Science)

# modify dataset
dat <- Science
dat[ dat[,1] > 3,1] <- 3
psych::describe(dat)

# estimate generalized partial credit model
mod1 <- mirt::mirt(dat, 1, itemtype="gpcm")
print(mod1)
# extract coefficients
coef(mod1)
mirt.wrapper.coef(mod1)$coef
# extract posterior
post1 <- sirt::mirt.wrapper.posterior(mod1)

#############################################################################
# EXAMPLE 3: Multiple group model; simulated dataset from mirt package
#############################################################################

#*** simulate data (copy from the multipleGroup manual site in mirt package)
set.seed(1234)
a <- matrix(c(abs( stats::rnorm(5,1,.3)), rep(0,15),abs( stats::rnorm(5,1,.3)),
          rep(0,15),abs( stats::rnorm(5,1,.3))), 15, 3)
d <- matrix( stats::rnorm(15,0,.7),ncol=1)
mu <- c(-.4, -.7, .1)
sigma <- matrix(c(1.21,.297,1.232,.297,.81,.252,1.232,.252,1.96),3,3)
itemtype <- rep("dich", nrow(a))
N <- 1000
dataset1 <- mirt::simdata(a, d, N, itemtype)
dataset2 <- mirt::simdata(a, d, N, itemtype, mu=mu, sigma=sigma)
dat <- rbind(dataset1, dataset2)
group <- c(rep("D1", N), rep("D2", N))

#group models
model <- mirt::mirt.model("
   F1=1-5
   F2=6-10
   F3=11-15
      ")

# separate analysis
mod_configural <- mirt::multipleGroup(dat, model, group=group, verbose=TRUE)
mirt.wrapper.coef(mod_configural)

# equal slopes (metric invariance)
mod_metric <- mirt::multipleGroup(dat, model, group=group, invariance=c("slopes"),
                verbose=TRUE)
mirt.wrapper.coef(mod_metric)

# equal slopes and intercepts (scalar invariance)
mod_scalar <- mirt::multipleGroup(dat, model, group=group,
          invariance=c("slopes","intercepts","free_means","free_varcov"), verbose=TRUE)
mirt.wrapper.coef(mod_scalar)

# full constraint
mod_fullconstrain <- mirt::multipleGroup(dat, model, group=group,
             invariance=c("slopes", "intercepts", "free_means", "free_var"), verbose=TRUE )
mirt.wrapper.coef(mod_fullconstrain)

#############################################################################
# EXAMPLE 4: Nonlinear item response model
#############################################################################

data(data.read)
dat <- data.read
# specify mirt model with some interactions
mirtmodel <- mirt.model("
   A=1-4
   B=5-8
   C=9-12
   (A*B)=4,8
   (C*C)=9
   (A*B*C)=12
   " )
# estimate model
res <- mirt::mirt( dat, mirtmodel, verbose=TRUE, technical=list(NCYCLES=3) )
# look at estimated parameters
mirt.wrapper.coef(res)
coef(res)
mirt::mod2values(res)
# model specification
res@model

#############################################################################
# EXAMPLE 5: Extracting factor scores
#############################################################################

data(data.read)
dat <- data.read
# define lavaan model and convert syntax to mirt
lavmodel <- "
    A=~ a*A1+a*A2+1.3*A3+A4       # set loading of A3 to 1.3
    B=~ B1+1*B2+b3*B3+B4
    C=~ c*C1+C2+c*C3+C4
    A1 | da*t1
    A3 | da*t1
    C4 | dg*t1
    B1 | 0*t1
    B3 | -1.4*t1                  # fix item threshold of B3 to -1.4
    A ~~ B                        # estimate covariance between A and B
    A ~~ .6 * C                   # fix covariance to .6
    B ~~ B                        # estimate variance of B
    A ~ .5*1                      # set mean of A to .5
    B ~ 1                         # estimate mean of B
    "
res <- sirt::lavaan2mirt( dat, lavmodel, verbose=TRUE, technical=list(NCYCLES=3) )
# estimated coefficients
mirt.wrapper.coef(res$mirt)
# extract factor scores
fres <- sirt::mirt.wrapper.fscores(res$mirt)
# look at factor scores
head( round(fres$person,2))
  ##     case    M EAP.Var1 SE.EAP.Var1 EAP.Var2 SE.EAP.Var2 EAP.Var3 SE.EAP.Var3 MLE.Var1
  ##   1    1 0.92     1.26        0.67     1.61        0.60     0.05        0.69     2.65
  ##   2    2 0.58     0.06        0.59     1.14        0.55    -0.80        0.56     0.00
  ##   3    3 0.83     0.86        0.66     1.15        0.55     0.48        0.74     0.53
  ##   4    4 1.00     1.52        0.67     1.57        0.60     0.73        0.76     2.65
  ##   5    5 0.50    -0.13        0.58     0.85        0.48    -0.82        0.55    -0.53
  ##   6    6 0.75     0.41        0.63     1.09        0.54     0.27        0.71     0.00
  ##     MLE.Var2 MLE.Var3 MAP.Var1 MAP.Var2 MAP.Var3
  ##   1     2.65    -0.53     1.06     1.59     0.00
  ##   2     1.06    -1.06     0.00     1.06    -1.06
  ##   3     1.06     2.65     1.06     1.06     0.53
  ##   4     2.65     2.65     1.59     1.59     0.53
  ##   5     0.53    -1.06    -0.53     0.53    -1.06
  ##   6     1.06     2.65     0.53     1.06     0.00
# EAP reliabilities
round(fres$EAP.rel,3)
  ##    Var1  Var2  Var3
  ##   0.574 0.452 0.541

## End(Not run)