sirt (version 1.14-0)

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" , ...)

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

Value

A list with following entries

Details

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

Only single group models are supported (for now).

See Also

See http://lavaan.ugent.be/ for lavaan resources. See https://groups.google.com/forum/#!forum/lavaan for discussion about the lavaan package. See mirt.wrapper for convenience wrapper functions for mirt::mirt objects.

See TAM::lavaanify.IRT for extensions of lavaanify.

See tam2mirt for converting fitted objects in the TAM package into fitted mirt::mirt objects.

Examples

## Not run: 
# #############################################################################
# # 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 <- 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 <- 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$mirt.pars )
# mirt.wrapper.coef(res2)     # parameter estimates
# 
# #******************
# #*** Model 3: Bifactor model
# lavmodel <- "
#      G=~ A1+A2+A3+A4 + B1+B2+B3+B4  + C1+C2+C3+C4 
#      A=~ A1+A2+A3+A4
#      B=~ B1+B2+B3+B4     
#      C=~ C1+C2+C3+C4   
#      G ~~ 1*G
#      A ~~ 1*A
#      B ~~ 1*B
#      C ~~ 1*C      
#             "           
# res <- lavaan2mirt( dat ,  lavmodel , est.mirt=FALSE )
# # mirt syntax and mirt model
# cat(res$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 <- 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$mirt)
# 
# #******************
# #*** Model 5: 3-dimensional model with some parameter constraints and
# #             parameter fixings
# lavmodel <- "
#      A=~ a*A1+a*A2+1.3*A3+A4  # set loading of A3 to 1.3
#      B=~ B1+1*B2+b3*B3+B4     
#      C=~ c*C1+C2+c*C3+C4          
#      A1 | da*t1        
#      A3 | da*t1  
#      C4 | dg*t1
#      B1 | 0*t1
#      B3 | -1.4*t1   # fix item threshold of B3 to -1.4
#      A ~~ 1*A	    
#      B ~~ B         # estimate variance of B freely 
#      C ~~ 1*C     
#      A ~~ B         # estimate covariance between A and B
#      A ~~ .6 * C    # fix covariance to .6
#      A ~ .5*1       # set mean of A to .5
#      B ~ 1          # estimate mean of B
#             "   
# res <- lavaan2mirt( dat ,  lavmodel , verbose=TRUE , technical=list(NCYCLES=3) )
# mirt.wrapper.coef(res$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 <- 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 <- 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 <- 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 <- 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 <- 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 <- lavaan2mirt( dat, lavmodel, poly.itemtype="graded", technical=list(NCYCLES=20),
#               verbose=TRUE )
# mirt.wrapper.coef(res1$mirt) 
# 
# #******************
# #*** Model 2: Generalized partial credit model with some constraints
# lavmodel <- "
#       O =~ O3+O8+O13+a*O18+a*O23+1.2*O28
#       O ~ 1   # estimate mean
#       O ~~ O  # estimate variance
#       # some constraints among thresholds
#       O3  | d1*t1
#       O13 | d1*t1
#       O3  | d2*t2
#       O8  | d3*t2
#       O28 | (-0.5)*t1
#          "             
# # estimate model in mirt
# res <- lavaan2mirt( dat , lavmodel , technical=list(NCYCLES=5) , verbose=TRUE)
# # estimated mirt model
# mres <- res$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")
# ## End(Not run)