lavaan2mirt: Converting a `lavaan` Model into a `mirt` Model

Description

Converts a lavaan model into a mirt model. Optionally, the model can be estimated with the mirt::mirt function (est.mirt=TRUE) or just mirt syntax is generated (est.mirt=FALSE).

Extensions of the lavaan syntax include guessing and slipping parameters (operators ?=g1 and ?=s1) and a shortage operator for item groups (see __). See TAM::lavaanify.IRT for more details.

Usage

lavaan2mirt(dat, lavmodel, est.mirt=TRUE, poly.itemtype="gpcm", ...)

Value

A list with following entries

mirt: Object generated by mirt function if est.mirt=TRUE
mirt.model: Generated mirt model
mirt.syntax: Generated mirt syntax
mirt.pars: Generated parameter specifications in mirt
lavaan.model: Used lavaan model transformed by lavaanify function
dat: Used dataset. If necessary, only items used in the model are included in the dataset.

Arguments

dat: Dataset with item responses
lavmodel: Model specified in lavaan syntax (see lavaan::lavaanify)
est.mirt: An optional logical indicating whether the model should be estimated with mirt::mirt
poly.itemtype: Item type for polytomous data. This can be gpcm for the generalized partial credit model or graded for the graded response model.
...: Further arguments to be passed for estimation in mirt

Details

This function uses the lavaan::lavaanify (lavaan) function.

Only single group models are supported (for now).

Examples

Run this code

if (FALSE) {
#############################################################################
# EXAMPLE 1: Convert some lavaan syntax to mirt syntax for data.read
#############################################################################

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

#******************
#*** Model 1: Single factor model
lavmodel <- "
     # omit item C3
     F=~ A1+A2+A3+A4 + C1+C2+C4 + B1+B2+B3+B4
     F ~~ 1*F
            "

# convert syntax and estimate model
res <- sirt::lavaan2mirt( dat,  lavmodel, verbose=TRUE, technical=list(NCYCLES=3) )
# inspect coefficients
coef(res$mirt)
mirt.wrapper.coef(res$mirt)
# converted mirt model and parameter table
cat(res$mirt.syntax)
res$mirt.pars

#******************
#*** Model 2: Rasch Model with first six items
lavmodel <- "
     F=~ a*A1+a*A2+a*A3+a*A4+a*B1+a*B2
     F ~~ 1*F
            "
# convert syntax and estimate model
res <- sirt::lavaan2mirt( dat,  lavmodel, est.mirt=FALSE)
# converted mirt model
cat(res$mirt.syntax)
# mirt parameter table
res$mirt.pars
# estimate model using generated objects
res2 <- mirt::mirt( res$dat, res$mirt.model, pars=res$mirt.pars )
mirt.wrapper.coef(res2)     # parameter estimates

#******************
#*** Model 3: Bifactor model
lavmodel <- "
     G=~ A1+A2+A3+A4 + B1+B2+B3+B4  + C1+C2+C3+C4
     A=~ A1+A2+A3+A4
     B=~ B1+B2+B3+B4
     C=~ C1+C2+C3+C4
     G ~~ 1*G
     A ~~ 1*A
     B ~~ 1*B
     C ~~ 1*C
            "
res <- sirt::lavaan2mirt( dat,  lavmodel, est.mirt=FALSE )
# mirt syntax and mirt model
cat(res$mirt.syntax)
res$mirt.model
res$mirt.pars

#******************
#*** Model 4: 3-dimensional model with some parameter constraints
lavmodel <- "
     # some equality constraints among loadings
     A=~ a*A1+a*A2+a2*A3+a2*A4
     B=~ B1+B2+b3*B3+B4
     C=~ c*C1+c*C2+c*C3+c*C4
     # some equality constraints among thresholds
     A1 | da*t1
     A3 | da*t1
     B3 | da*t1
     C3 | dg*t1
     C4 | dg*t1
     # standardized latent variables
     A ~~ 1*A
     B ~~ 1*B
     C ~~ 1*C
     # estimate Cov(A,B) and Cov(A,C)
     A ~~ B
     A ~~ C
     # estimate mean of B
     B ~ 1
            "
res <- sirt::lavaan2mirt( dat,  lavmodel, verbose=TRUE, technical=list(NCYCLES=3) )
# estimated parameters
mirt.wrapper.coef(res$mirt)
# generated mirt syntax
cat(res$mirt.syntax)
# mirt parameter table
mirt::mod2values(res$mirt)

#******************
#*** Model 5: 3-dimensional model with some parameter constraints and
#             parameter fixings
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 ~~ 1*A
     B ~~ B         # estimate variance of B freely
     C ~~ 1*C
     A ~~ B         # estimate covariance between A and B
     A ~~ .6 * C    # fix covariance to .6
     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) )
mirt.wrapper.coef(res$mirt)

#******************
#*** Model 6: 1-dimensional model with guessing and slipping parameters
#******************

lavmodel <- "
     F=~ c*A1+c*A2+1*A3+1.3*A4 + C1__C4 + a*B1+b*B2+b*B3+B4
     # guessing parameters
     A1+A2 ?=guess1*g1
     A3 ?=.25*g1
     B1+C1 ?=g1
     B2__B4 ?=0.10*g1
     # slipping parameters
     A1+A2+C3 ?=slip1*s1
     A3 ?=.02*s1
     # fix item intercepts
     A1 | 0*t1
     A2 | -.4*t1
     F ~ 1    # estimate mean of F
     F ~~ 1*F   # fix variance of F
            "
# convert syntax and estimate model
res <- sirt::lavaan2mirt( dat,  lavmodel, verbose=TRUE, technical=list(NCYCLES=3) )
# coefficients
mirt.wrapper.coef(res$mirt)
# converted mirt model
cat(res$mirt.syntax)

#############################################################################
# EXAMPLE 2: Convert some lavaan syntax to mirt syntax for
#            longitudinal data data.long
#############################################################################

data(data.long)
dat <- data.long[,-1]

#******************
#*** Model 1: Rasch model for T1
lavmodel <- "
     F=~ 1*I1T1 +1*I2T1+1*I3T1+1*I4T1+1*I5T1+1*I6T1
     F ~~ F
            "
# convert syntax and estimate model
res <- sirt::lavaan2mirt( dat,  lavmodel, verbose=TRUE, technical=list(NCYCLES=20) )
# inspect coefficients
mirt.wrapper.coef(res$mirt)
# converted mirt model
cat(res$mirt.syntax)

#******************
#*** Model 2: Rasch model for two time points
lavmodel <- "
     F1=~ 1*I1T1 +1*I2T1+1*I3T1+1*I4T1+1*I5T1+1*I6T1
     F2=~ 1*I3T2 +1*I4T2+1*I5T2+1*I6T2+1*I7T2+1*I8T2
     F1 ~~ F1
     F1 ~~ F2
     F2 ~~ F2
     # equal item difficulties of same items
     I3T1 | i3*t1
     I3T2 | i3*t1
     I4T1 | i4*t1
     I4T2 | i4*t1
     I5T1 | i5*t1
     I5T2 | i5*t1
     I6T1 | i6*t1
     I6T2 | i6*t1
     # estimate mean of F1, but fix mean of F2
     F1 ~ 1
     F2 ~ 0*1
            "
# convert syntax and estimate model
res <- sirt::lavaan2mirt( dat,  lavmodel, verbose=TRUE, technical=list(NCYCLES=20) )
# inspect coefficients
mirt.wrapper.coef(res$mirt)
# converted mirt model
cat(res$mirt.syntax)

#-- compare estimation with smirt function
# define Q-matrix
I <- ncol(dat)
Q <- matrix(0,I,2)
Q[1:6,1] <- 1
Q[7:12,2] <- 1
rownames(Q) <- colnames(dat)
colnames(Q) <- c("T1","T2")
# vector with same items
itemnr <- as.numeric( substring( colnames(dat),2,2) )
# fix mean at T2 to zero
mu.fixed <- cbind( 2,0 )
# estimate model in smirt
mod1 <- sirt::smirt(dat, Qmatrix=Q, irtmodel="comp", est.b=itemnr, mu.fixed=mu.fixed )
summary(mod1)

#############################################################################
# EXAMPLE 3: Converting lavaan syntax for polytomous data
#############################################################################

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)

#******************
#*** Model 1: Partial credit model
lavmodel <- "
      O=~ 1*O3+1*O8+1*O13+1*O18+1*O23+1*O28
      O ~~ O
         "
# estimate model in mirt
res <- sirt::lavaan2mirt( dat, lavmodel, technical=list(NCYCLES=20), verbose=TRUE)
# estimated mirt model
mres <- res$mirt
# mirt syntax
cat(res$mirt.syntax)
  ##   O=1,2,3,4,5,6
  ##   COV=O*O
# estimated parameters
mirt.wrapper.coef(mres)
# some plots
mirt::itemplot( mres, 3 )   # third item
plot(mres)   # item information
plot(mres,type="trace")  # item category functions

# graded response model with equal slopes
res1 <- sirt::lavaan2mirt( dat, lavmodel, poly.itemtype="graded", technical=list(NCYCLES=20),
              verbose=TRUE )
mirt.wrapper.coef(res1$mirt)

#******************
#*** Model 2: Generalized partial credit model with some constraints
lavmodel <- "
      O=~ O3+O8+O13+a*O18+a*O23+1.2*O28
      O ~ 1   # estimate mean
      O ~~ O  # estimate variance
      # some constraints among thresholds
      O3  | d1*t1
      O13 | d1*t1
      O3  | d2*t2
      O8  | d3*t2
      O28 | (-0.5)*t1
         "
# estimate model in mirt
res <- sirt::lavaan2mirt( dat, lavmodel, technical=list(NCYCLES=5), verbose=TRUE)
# estimated mirt model
mres <- res$mirt
# estimated parameters
mirt.wrapper.coef(mres)

#*** generate syntax for mirt for this model and estimate it in mirt package
# Items: O3  O8  O13 O18 O23 O28
mirtmodel <- mirt::mirt.model( "
             O=1-6
             # a(O18)=a(O23), t1(O3)=t1(O18), t2(O3)=t2(O8)
             CONSTRAIN=(4,5,a1), (1,3,d1), (1,2,d2)
             MEAN=O
             COV=O*O
               ")
# initial table of parameters in mirt
mirt.pars <- mirt::mirt( dat[,1:6], mirtmodel, itemtype="gpcm", pars="values")
# fix slope of item O28 to 1.2
ind <- which( ( mirt.pars$item=="O28" ) & ( mirt.pars$name=="a1") )
mirt.pars[ ind, "est"] <- FALSE
mirt.pars[ ind, "value"] <- 1.2
# fix d1 of item O28 to -0.5
ind <- which( ( mirt.pars$item=="O28" ) & ( mirt.pars$name=="d1") )
mirt.pars[ ind, "est"] <- FALSE
mirt.pars[ ind, "value"] <- -0.5
# estimate model
res2 <- mirt::mirt( dat[,1:6], mirtmodel, pars=mirt.pars,
             verbose=TRUE, technical=list(NCYCLES=4) )
mirt.wrapper.coef(res2)
plot(res2, type="trace")
}