linking.haberman: Linking in the 2PL/Generalized Partial Credit Model

Description

This function does the linking of several studies which are calibrated using the 2PL or the generalized item response model according to Haberman (2009). This method is a generalization of log-mean-mean linking from one study to several studies. The default a_log=TRUE logarithmizes item slopes for linking while otherwise an additive regression model is assumed for the original item loadings (see Details; Battauz, 2017)

Usage

linking.haberman(itempars, personpars, estimation="OLS", a_trim=Inf, b_trim=Inf,
    lts_prop=.5, a_log=TRUE, conv=1e-05, maxiter=1000, progress=TRUE,
    adjust_main_effects=TRUE, vcov=TRUE)
# S3 method for linking.haberman
summary(object, digits=3, file=NULL, ...)
linking.haberman.lq(itempars, pow=2, eps=1e-3, a_log=TRUE, use_nu=FALSE,
      est_pow=FALSE, lower_pow=.1, upper_pow=3)
# S3 method for linking.haberman.lq
summary(object, digits=3, file=NULL, ...)
## prepare 'itempars' argument for linking.haberman()
linking_haberman_itempars_prepare(b, a=NULL, wgt=NULL)
## conversion of different parameterizations of item parameters
linking_haberman_itempars_convert(itempars=NULL, lambda=NULL, nu=NULL, a=NULL, b=NULL)
## L0 polish precedure minimizing number of interactions in two-way table
L0_polish(x, tol, conv=0.01, maxiter=30, type=1, verbose=TRUE)

Value

A list with following entries

transf.pars: Data frame with transformation parameters $A_t$ and $B_t$
transf.personpars: Data frame with linear transformation functions for person parameters
joint.itempars: Estimated joint item parameters $a_i$ and $b_i$
a.trans: Transformed $a_{it}$ parameters
b.trans: Transformed $b_{it}$ parameters
a.orig: Original $a_{it}$ parameters
b.orig: Original $b_{it}$ parameters
a.resid: Residual $a_{it}$ parameters (DIF parameters)
b.resid: Residual $b_{it}$ parameters (DIF parameters)
personpars: Transformed person parameters
es.invariance: Effect size measures of invariance, separately for item slopes and intercepts. In the rows, $R^2$ and $\sqrt{1-R^2}$ are reported.
es.robust: Effect size measures of invariance based on robust estimation (if used).
selitems: Indices of items which are present in more than one study.

Arguments

itempars: A data frame with four or five columns. The first four columns contain in the order: study name, item name, $a$ parameter, $b$ parameter. The fifth column is an optional weight for every item and every study.
personpars: A list with vectors (e.g. EAPs or WLEs) or data frames (e.g. plausible values) containing person parameters which should be transformed. If a data frame in each list entry has se or SE (standard error) in a column name, then the corresponding column is only multiplied by $A_t$. If a column is labeled as pid (person ID), then it is left untransformed.
estimation: Estimation method. Can be "OLS" (ordinary least squares), "BSQ" (bisquare weighted regression), "HUB" (regression using Huber weights), "MED" (median regression), "LTS" (trimmed least squares), "L1" (median polish), "L0" (minimizing number of interactions)
a_trim: Trimming parameter for item slopes $a_{it}$ in bisquare regression (see Details).
b_trim: Trimming parameter for item slopes $b_{it}$ in bisquare regression (see Details).
lts_prop: Proportion of retained observations in "LTS" regression estimation
a_log: Logical indicating whether item slopes should be logarithmized for linking.
conv: Convergence criterion.
maxiter: Maximum number of iterations.
progress: An optional logical indicating whether computational progress should be displayed.
adjust_main_effects: Logical indicating whether all elements in the vector of main effects should be simultaneously adjusted
vcov: Optional indicating whether covariance matrix for linking errors should be computed
pow: Power $q$
eps: Epsilon value used in differentiable approximating function
use_nu: Logical indicating whether item intercepts instead of item difficulties are used in linking
est_pow: Logical indicating whether power values should be estimated
lower_pow: Lower bound for estimated power
upper_pow: Upper bound for estimated power
lambda: Matrix containing item loadings
nu: Matrix containing item intercepts
object: Object of class linking.haberman.
digits: Number of digits after decimals for rounding in summary.
file: Optional file name if summary should be sunk into a file.
...: Further arguments to be passed
b: Matrix of item intercepts (items $times$ studies)
a: Matrix of item slopes
wgt: Matrix of weights
x: Matrix
tol: Tolerance value
type: Can be 1 (using Tukey's median polish) or 2 (alternating median regression).
verbose: Logical indicating whether iteration progress should be displayed

Details

For $t=1,\ldots,T$ studies, item difficulties $b_{it}$ and item slopes $a_{it}$ are available. For dichotomous responses, these parameters are defined by the 2PL response equation $$ logit P(X_{pi}=1| \theta_p )=a_i ( \theta_p - b_i ) $$ while for polytomous responses the generalized partial credit model holds $$ log \frac{P(X_{pi}=k| \theta_p )}{P(X_{pi}=k-1| \theta_p )} =a_i ( \theta_p - b_i + d_{ik} ) $$

The parameters $ \{ a_{it}, b_{it} \}$ of all items and studies are linearly transformed using equations $a_{it} \approx a_i / A_t$ (if a_log=TRUE) or $a_{it} \approx a_i + A_t$ (if a_log=FALSE) and $b_{it} \cdot A_t \approx B_t + b_i$. For identification reasons, we define $A_1=1$ and $B_1$=0.

The optimization function (which is a least squares criterion; see Haberman, 2009) seeks the transformation parameters $A_t$ and $B_t$ with an alternating least squares method (estimation="OLS"). Note that every item $i$ and every study $t$ can be weighted (specified in the fifth column of itempars). Alternatively, a robust regression method based on bisquare weighting (Fox, 2015) can be employed for linking using the argument estimation="BSQ". For example, in the case of item loadings, bisquare weighting is applied to residuals $e_{it}=a_{it} - a_i - A_t $ (where logarithmized or non-logarithmized item loadings are employed) forming weights $w_{it}=[ 1 - ( e_{it} / k )^2 ]^2$ for $e_{it} <k$ and 0 for $e_{it} \ge k$ where $k$ is the trimming constant which can be estimated or fixed during estimation using arguments a_trim or b_trim. Items in studies with large residuals (i.e., presence differential item functioning) are effectively set to zero in the linking procedure. Alternatively, Huber weights (estimation="HUB") downweight large residuals by applying $w_{it}=k / | e_{it} |$ for residuals $|e_{it}|>k$. The method estimation="LTS" employs trimmed least squares where the proportion of data retained is specified in lts_prop with default set to .50.

The method estimation="MED" estimates item parameters and linking constants based on alternating median regression. A similar approach is the median polish procedure of Tukey (Tukey, 1977, p. 362ff.; Maronna, Martin & Yohai, 2006, p. 104; see also stats::medpolish) implemented in estimation="L1" which aims to minimize $\sum_{i,t} | e_{it} |$. For a pre-specified tolerance value $t$ (in a_trim or b_trim), the approach estimation="L0" minimizes the number of interactions (i.e., DIF effects) in the $e_{it}$ effects. In more detail, it minimizes $\sum_{i,t} \# \{ | e_{it} | > t \} $ which is computationally conducted by repeatedly applying the median polish procedure in which one cell is omitted (Davies, 2012; Terbeck & Davies, 1998).

Effect sizes of invariance are calculated as R-squared measures of explained item slopes and intercepts after linking in comparison to item parameters across groups (Asparouhov & Muthen, 2014).

The function $linking.haberman.lq$ uses the loss function $\rho(x)=|x|^q$. The originally proposed Haberman linking can be obtained with pow=2 ($q=2$). The powers can also be estimated (argument est_pow=TRUE).

References

Asparouhov, T., & Muthen, B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling, 21(4), 1-14. tools:::Rd_expr_doi("10.1080/10705511.2014.919210")

Battauz, M. (2017). Multiple equating of separate IRT calibrations. Psychometrika, 82(3), 610-636. tools:::Rd_expr_doi("10.1007/s11336-016-9517-x")

Davies, P. L. (2012). Interactions in the analysis of variance. Journal of the American Statistical Association, 107(500), 1502-1509. tools:::Rd_expr_doi("10.1080/01621459.2012.726895")

Fox, J. (2015). Applied regression analysis and generalized linear models. Thousand Oaks: Sage.

Haberman, S. J. (2009). Linking parameter estimates derived from an item response model through separate calibrations. ETS Research Report ETS RR-09-40. Princeton, ETS. tools:::Rd_expr_doi("10.1002/j.2333-8504.2009.tb02197.x")

Kolen, M. J., & Brennan, R. L. (2014). Test equating, scaling, and linking: Methods and practices. New York: Springer. tools:::Rd_expr_doi("10.1007/978-1-4939-0317-7")

Magis, D., & De Boeck, P. (2012). A robust outlier approach to prevent type I error inflation in differential item functioning. Educational and Psychological Measurement, 72(2), 291-311. tools:::Rd_expr_doi("10.1177/0013164411416975")

Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006). Robust statistics. West Sussex: Wiley. tools:::Rd_expr_doi("10.1002/0470010940")

Terbeck, W., & Davies, P. L. (1998). Interactions and outliers in the two-way analysis of variance. Annals of Statistics, 26(4), 1279-1305. doi: 10.1214/aos/1024691243

Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.

Weeks, J. P. (2010). plink: An R package for linking mixed-format tests using IRT-based methods. Journal of Statistical Software, 35(12), 1-33. tools:::Rd_expr_doi("10.18637/jss.v035.i12")

Examples

Run this code

#############################################################################
# EXAMPLE 1: Item parameters data.pars1.rasch and data.pars1.2pl
#############################################################################

# Model 1: Linking three studies calibrated by the Rasch model
data(data.pars1.rasch)
mod1 <- sirt::linking.haberman( itempars=data.pars1.rasch )
summary(mod1)

# Model 1b: Linking these studies but weigh these studies by
#     proportion weights 3 : 0.5 : 1 (see below).
#     All weights are the same for each item but they could also
#     be item specific.
itempars <- data.pars1.rasch
itempars$wgt <- 1
itempars[ itempars$study=="study1","wgt"] <- 3
itempars[ itempars$study=="study2","wgt"] <- .5
mod1b <- sirt::linking.haberman( itempars=itempars )
summary(mod1b)

# Model 2: Linking three studies calibrated by the 2PL model
data(data.pars1.2pl)
mod2 <- sirt::linking.haberman( itempars=data.pars1.2pl )
summary(mod2)

# additive model instead of logarithmic model for item slopes
mod2b <- sirt::linking.haberman( itempars=data.pars1.2pl, a_log=FALSE )
summary(mod2b)

if (FALSE) {
#############################################################################
# EXAMPLE 2: Linking longitudinal data
#############################################################################
data(data.long)

#******
# Model 1: Scaling with the 1PL model

# scaling at T1
dat1 <- data.long[, grep("T1", colnames(data.long) ) ]
resT1 <- sirt::rasch.mml2( dat1 )
itempartable1 <- data.frame( "study"="T1", resT1$item[, c("item", "a", "b" ) ] )
# scaling at T2
dat2 <- data.long[, grep("T2", colnames(data.long) ) ]
resT2 <- sirt::rasch.mml2( dat2 )
summary(resT2)
itempartable2 <- data.frame( "study"="T2", resT2$item[, c("item", "a", "b" ) ] )
itempartable <- rbind( itempartable1, itempartable2 )
itempartable[,2] <- substring( itempartable[,2], 1, 2 )
# estimate linking parameters
mod1 <- sirt::linking.haberman( itempars=itempartable )

#******
# Model 2: Scaling with the 2PL model

# scaling at T1
dat1 <- data.long[, grep("T1", colnames(data.long) ) ]
resT1 <- sirt::rasch.mml2( dat1, est.a=1:6)
itempartable1 <- data.frame( "study"="T1", resT1$item[, c("item", "a", "b" ) ] )

# scaling at T2
dat2 <- data.long[, grep("T2", colnames(data.long) ) ]
resT2 <- sirt::rasch.mml2( dat2, est.a=1:6)
summary(resT2)
itempartable2 <- data.frame( "study"="T2", resT2$item[, c("item", "a", "b" ) ] )
itempartable <- rbind( itempartable1, itempartable2 )
itempartable[,2] <- substring( itempartable[,2], 1, 2 )
# estimate linking parameters
mod2 <- sirt::linking.haberman( itempars=itempartable )

#############################################################################
# EXAMPLE 3: 2 Studies - 1PL and 2PL linking
#############################################################################
set.seed(789)
I <- 20        # number of items
N <- 2000       # number of persons
# define item parameters
b <- seq( -1.5, 1.5, length=I )
# simulate data
dat1 <- sirt::sim.raschtype( stats::rnorm( N, mean=0,sd=1 ), b=b )
dat2 <- sirt::sim.raschtype( stats::rnorm( N, mean=0.5,sd=1.50 ), b=b )

#*** Model 1: 1PL
# 1PL Study 1
mod1 <- sirt::rasch.mml2( dat1, est.a=rep(1,I) )
summary(mod1)
# 1PL Study 2
mod2 <- sirt::rasch.mml2( dat2, est.a=rep(1,I) )
summary(mod2)

# collect item parameters
dfr1 <- data.frame( "study1", mod1$item$item, mod1$item$a, mod1$item$b )
dfr2 <- data.frame( "study2", mod2$item$item, mod2$item$a, mod2$item$b )
colnames(dfr2) <- colnames(dfr1) <- c("study", "item", "a", "b" )
itempars <- rbind( dfr1, dfr2 )

# Haberman linking
linkhab1 <- sirt::linking.haberman(itempars=itempars)
  ## Transformation parameters (Haberman linking)
  ##    study    At     Bt
  ## 1 study1 1.000  0.000
  ## 2 study2 1.465 -0.512
  ##
  ## Linear transformation for item parameters a and b
  ##    study   A_a   A_b    B_b
  ## 1 study1 1.000 1.000  0.000
  ## 2 study2 0.682 1.465 -0.512
  ##
  ## Linear transformation for person parameters theta
  ##    study A_theta B_theta
  ## 1 study1   1.000   0.000
  ## 2 study2   1.465   0.512
  ##
  ## R-Squared Measures of Invariance
  ##        slopes intercepts
  ## R2          1     0.9979
  ## sqrtU2      0     0.0456

#*** Model 2: 2PL
# 2PL Study 1
mod1 <- sirt::rasch.mml2( dat1, est.a=1:I )
summary(mod1)
# 2PL Study 2
mod2 <- sirt::rasch.mml2( dat2, est.a=1:I )
summary(mod2)

# collect item parameters
dfr1 <- data.frame( "study1", mod1$item$item, mod1$item$a, mod1$item$b )
dfr2 <- data.frame( "study2", mod2$item$item, mod2$item$a, mod2$item$b )
colnames(dfr2) <- colnames(dfr1) <- c("study", "item", "a", "b" )
itempars <- rbind( dfr1, dfr2 )

# Haberman linking
linkhab2 <- sirt::linking.haberman(itempars=itempars)
  ## Transformation parameters (Haberman linking)
  ##    study    At     Bt
  ## 1 study1 1.000  0.000
  ## 2 study2 1.468 -0.515
  ##
  ## Linear transformation for item parameters a and b
  ##    study   A_a   A_b    B_b
  ## 1 study1 1.000 1.000  0.000
  ## 2 study2 0.681 1.468 -0.515
  ##
  ## Linear transformation for person parameters theta
  ##    study A_theta B_theta
  ## 1 study1   1.000   0.000
  ## 2 study2   1.468   0.515
  ##
  ## R-Squared Measures of Invariance
  ##        slopes intercepts
  ## R2     0.9984     0.9980
  ## sqrtU2 0.0397     0.0443

#############################################################################
# EXAMPLE 4: 3 Studies - 1PL and 2PL linking
#############################################################################
set.seed(789)
I <- 20         # number of items
N <- 1500       # number of persons
# define item parameters
b <- seq( -1.5, 1.5, length=I )
# simulate data
dat1 <- sirt::sim.raschtype( stats::rnorm( N, mean=0, sd=1), b=b )
dat2 <- sirt::sim.raschtype( stats::rnorm( N, mean=0.5, sd=1.50), b=b )
dat3 <- sirt::sim.raschtype( stats::rnorm( N, mean=-0.2, sd=0.8), b=b )
# set some items to non-administered
dat3 <- dat3[, -c(1,4) ]
dat2 <- dat2[, -c(1,2,3) ]

#*** Model 1: 1PL in sirt
# 1PL Study 1
mod1 <- sirt::rasch.mml2( dat1, est.a=rep(1,ncol(dat1)) )
summary(mod1)
# 1PL Study 2
mod2 <- sirt::rasch.mml2( dat2, est.a=rep(1,ncol(dat2)) )
summary(mod2)
# 1PL Study 3
mod3 <- sirt::rasch.mml2( dat3, est.a=rep(1,ncol(dat3)) )
summary(mod3)

# collect item parameters
dfr1 <- data.frame( "study1", mod1$item$item, mod1$item$a, mod1$item$b )
dfr2 <- data.frame( "study2", mod2$item$item, mod2$item$a, mod2$item$b )
dfr3 <- data.frame( "study3", mod3$item$item, mod3$item$a, mod3$item$b )
colnames(dfr3) <- colnames(dfr2) <- colnames(dfr1) <- c("study", "item", "a", "b" )
itempars <- rbind( dfr1, dfr2, dfr3 )

# use person parameters
personpars <- list( mod1$person[, c("EAP","SE.EAP") ], mod2$person[, c("EAP","SE.EAP") ],
    mod3$person[, c("EAP","SE.EAP") ] )

# Haberman linking
linkhab1 <- sirt::linking.haberman(itempars=itempars, personpars=personpars)
# compare item parameters
round( cbind( linkhab1$joint.itempars[,-1], linkhab1$b.trans )[1:5,], 3 )
  ##            aj     bj study1 study2 study3
  ##   I0001 0.998 -1.427 -1.427     NA     NA
  ##   I0002 0.998 -1.290 -1.324     NA -1.256
  ##   I0003 0.998 -1.140 -1.068     NA -1.212
  ##   I0004 0.998 -0.986 -1.003 -0.969     NA
  ##   I0005 0.998 -0.869 -0.809 -0.872 -0.926

# summary of person parameters of second study
round( psych::describe( linkhab1$personpars[[2]] ), 2 )
  ##   var    n mean   sd median trimmed  mad   min  max range  skew kurtosis
  ## EAP      1 1500 0.45 1.36   0.41    0.47 1.52 -2.61 3.25  5.86 -0.08    -0.62
  ## SE.EAP   2 1500 0.57 0.09   0.53    0.56 0.04  0.49 0.84  0.35  1.47     1.56
  ##          se
  ## EAP    0.04
  ## SE.EAP 0.00

#*** Model 2: 2PL in TAM
library(TAM)
# 2PL Study 1
mod1 <- TAM::tam.mml.2pl( resp=dat1, irtmodel="2PL" )
pvmod1 <- TAM::tam.pv(mod1, ntheta=300, normal.approx=TRUE) # draw plausible values
summary(mod1)
# 2PL Study 2
mod2 <- TAM::tam.mml.2pl( resp=dat2, irtmodel="2PL" )
pvmod2 <- TAM::tam.pv(mod2, ntheta=300, normal.approx=TRUE)
summary(mod2)
# 2PL Study 3
mod3 <- TAM::tam.mml.2pl( resp=dat3, irtmodel="2PL" )
pvmod3 <- TAM::tam.pv(mod3, ntheta=300, normal.approx=TRUE)
summary(mod3)

# collect item parameters
#!!  Note that in TAM the parametrization is a*theta - b while linking.haberman
#!!  needs the parametrization a*(theta-b)
dfr1 <- data.frame( "study1", mod1$item$item, mod1$B[,2,1], mod1$xsi$xsi / mod1$B[,2,1] )
dfr2 <- data.frame( "study2", mod2$item$item, mod2$B[,2,1], mod2$xsi$xsi / mod2$B[,2,1] )
dfr3 <- data.frame( "study3", mod3$item$item, mod3$B[,2,1], mod3$xsi$xsi / mod3$B[,2,1] )
colnames(dfr3) <- colnames(dfr2) <- colnames(dfr1) <- c("study", "item", "a", "b" )
itempars <- rbind( dfr1, dfr2, dfr3 )

# define list containing person parameters
personpars <- list(  pvmod1$pv[,-1], pvmod2$pv[,-1], pvmod3$pv[,-1] )

# Haberman linking
linkhab2 <- sirt::linking.haberman(itempars=itempars,personpars=personpars)
  ##   Linear transformation for person parameters theta
  ##      study A_theta B_theta
  ##   1 study1   1.000   0.000
  ##   2 study2   1.485   0.465
  ##   3 study3   0.786  -0.192

# extract transformed person parameters
personpars.trans <- linkhab2$personpars

#############################################################################
# EXAMPLE 5: Linking with simulated item parameters containing outliers
#############################################################################

# simulate some parameters
I <- 38
set.seed(18785)
b <- stats::rnorm( I, mean=.3, sd=1.4 )
# simulate DIF effects plus some outliers
bdif <- stats::rnorm(I,mean=.4,sd=.09)+( stats::runif(I)>.9 )* rep( 1*c(-1,1)+.4, each=I/2 )
# create item parameter table
itempars <- data.frame( "study"=paste0("study",rep(1:2, each=I)),
                "item"=paste0( "I", 100 + rep(1:I,2) ), "a"=1,
                 "b"=c( b, b + bdif  )  )

#*** Model 1: Haberman linking with least squares regression
mod1 <- sirt::linking.haberman( itempars=itempars )
summary(mod1)

#*** Model 2: Haberman linking with robust bisquare regression with fixed trimming value
mod2 <- sirt::linking.haberman( itempars=itempars, estimation="BSQ", b_trim=.4)
summary(mod2)

#*** Model 2: Haberman linking with robust bisquare regression with estimated trimming value
mod3 <- sirt::linking.haberman( itempars=itempars, estimation="BSQ")
summary(mod3)

## see also Example 3 of ?sirt::robust.linking

#############################################################################
# EXAMPLE 6: Toy example of Magis and De Boeck (2012)
#############################################################################

# define item parameters from Magis & De Boeck (20212, p. 293)
b1 <- c(1,1,1,1)
b2 <- c(1,1,1,2)
itempars <- data.frame(study=rep(1:2, each=4), item=rep(1:4,2), a=1, b=c(b1,b2) )

#- Least squares regression
mod1 <- sirt::linking.haberman( itempars=itempars, estimation="OLS")
summary(mod1)

#- Bisquare regression with estimated and fixed trimming factors
mod2 <- sirt::linking.haberman( itempars=itempars, estimation="BSQ")
mod2a <- sirt::linking.haberman( itempars=itempars, estimation="BSQ", b_trim=.4)
mod2b <- sirt::linking.haberman( itempars=itempars, estimation="BSQ", b_trim=1.2)
summary(mod2)
summary(mod2a)
summary(mod2b)

#- Least squares trimmed regression
mod3 <- sirt::linking.haberman( itempars=itempars, estimation="LTS")
summary(mod3)

#- median regression
mod4 <- sirt::linking.haberman( itempars=itempars, estimation="MED")
summary(mod4)

#############################################################################
# EXAMPLE 7: Simulated example with directional DIF
#############################################################################

set.seed(98)
I <- 8
mu <- c(-.5, 0, .5)
b <- sample(seq(-1.5,1.5, len=I))
sd_dif <- 0.001
pars <- outer(b, mu, "+") + stats::rnorm(I*3, sd=sd_dif)
ind <- c(1,2); pars[ind,1] <- pars[ind,1] + c(.5,.5)
ind <- c(3,4); pars[ind,2] <- pars[ind,2] + (-1)*c(.6,.6)
ind <- c(5,6); pars[ind,3] <- pars[ind,3] + (-1)*c(1,1)

# median polish (=stats::medpolish())
tmod1 <- sirt:::L1_polish(x=pars)
# L0 polish with tolerance criterion of .3
tmod2 <- sirt::L0_polish(x=pars, tol=.3)

#- prepare itempars input
itempars <- sirt::linking_haberman_itempars_prepare(b=pars)

#- compare different estimation functions for Haberman linking
mod01 <- sirt::linking.haberman(itempars, estimation="L1")
mod02 <- sirt::linking.haberman(itempars, estimation="L0", b_trim=.3)
mod1 <- sirt::linking.haberman(itempars, estimation="OLS")
mod2 <- sirt::linking.haberman(itempars, estimation="BSQ")
mod2a <- sirt::linking.haberman(itempars, estimation="BSQ", b_trim=.4)
mod3 <- sirt::linking.haberman(itempars, estimation="MED")
mod4 <- sirt::linking.haberman(itempars, estimation="LTS")
mod5 <- sirt::linking.haberman(itempars, estimation="HUB")
mod01$transf.pars
mod02$transf.pars
mod1$transf.pars
mod2$transf.pars
mod2a$transf.pars
mod3$transf.pars
mod4$transf.pars
mod5$transf.pars

#############################################################################
# EXAMPLE 8: Many studies and directional DIF
#############################################################################

## dataset 2
set.seed(98)
I <- 10 # number of items
S <- 7  # number of studies
mu <- round( seq(0, 1, len=S))
b <- sample(seq(-1.5,1.5, len=I))
sd_dif <- 0.001
pars0 <- pars <- outer(b, mu, "+") + stats::rnorm(I*S, sd=sd_dif)

# select n_dif items at random per group and set it to dif or -dif
n_dif <- 2
dif <- .6
for (ss in 1:S){
    ind <- sample( 1:I, n_dif )
    pars[ind,ss] <- pars[ind,ss] + dif*sign( runif(1) - .5 )
}

# check DIF
pars - pars0

#* estimate models
itempars <- sirt::linking_haberman_itempars_prepare(b=pars)
mod0 <- sirt::linking.haberman(itempars, estimation="L0", b_trim=.2)
mod1 <- sirt::linking.haberman(itempars, estimation="OLS")
mod2 <- sirt::linking.haberman(itempars, estimation="BSQ")
mod2a <- sirt::linking.haberman(itempars, estimation="BSQ", b_trim=.4)
mod3 <- sirt::linking.haberman(itempars, estimation="MED")
mod3a <- sirt::linking.haberman(itempars, estimation="L1")
mod4 <- sirt::linking.haberman(itempars, estimation="LTS")
mod5 <- sirt::linking.haberman(itempars, estimation="HUB")
mod0$transf.pars
mod1$transf.pars
mod2$transf.pars
mod2a$transf.pars
mod3$transf.pars
mod3a$transf.pars
mod4$transf.pars
mod5$transf.pars

#* compare results with Haebara linking
mod11 <- sirt::linking.haebara(itempars, dist="L2")
mod12 <- sirt::linking.haebara(itempars, dist="L1")
summary(mod11)
summary(mod12)
}

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