sirt (version 1.14-0)

data.wide2long: Converting a Data Frame from Wide Format in a Long Format

Description

Converts a data frame in wide format into long format.

Usage

data.wide2long(dat, id = NULL, X = NULL, Q = NULL)

Arguments

dat

Data frame with item responses and a person identifier if id != NULL.

id

An optional string with the variable name of the person identifier.

X

Data frame with person covariates for inclusion in the data frame of long format

Q

Data frame with item predictors. Item labels must be included as a column named by "item".

Value

Data frame in long format

Examples

## Not run: 
# #############################################################################
# # EXAMPLE 1: data.pisaRead
# #############################################################################
# miceadds::library_install("lme4")
# 
# data(data.pisaRead) 
# dat <- data.pisaRead$data 
# Q <- data.pisaRead$item   # item predictors
# 
# # define items
# items <- colnames(dat)[ substring( colnames(dat) , 1 , 1 ) == "R" ]
# dat1 <- dat[ , c( "idstud" , items ) ]
# # matrix with person predictors
# X <- dat[ , c("idschool" , "hisei" , "female" , "migra") ]
# 
# # create dataset in long format
# dat.long <- data.wide2long( dat=dat1 , id="idstud" , X=X , Q=Q )
# 
# #***
# # Model 1: Rasch model
# mod1 <- lme4::glmer( resp ~ 0 + ( 1 | idstud ) + as.factor(item) , data = dat.long ,
#             family="binomial" , verbose=TRUE)
# summary(mod1)
# 
# #***
# # Model 2: Rasch model and inclusion of person predictors
# mod2 <- lme4::glmer( resp ~ 0 + ( 1 | idstud ) + as.factor(item) + female + hisei + migra,
#            data = dat.long , family="binomial" , verbose=TRUE)
# summary(mod2)
# 
# #***
# # Model 3: LLTM
# mod3 <- lme4::glmer(resp ~ (1|idstud) + as.factor(ItemFormat) + as.factor(TextType), 
#             data = dat.long , family="binomial" , verbose=TRUE)
# summary(mod3)
# 
# #############################################################################
# # EXAMPLE 2: Rasch model in lme4
# #############################################################################
# 
# set.seed(765)
# N <- 1000  # number of persons
# I <- 10    # number of items
# b <- seq(-2,2,length=I)
# dat <- sirt::sim.raschtype( stats::rnorm(N,sd=1.2) , b=b )
# dat.long <- data.wide2long( dat=dat )
# #***
# # estimate Rasch model with lmer
# library(lme4)
# mod1 <- lme4::glmer( resp ~ 0 + as.factor( item ) + ( 1 | id_index) , data = dat.long ,
#              verbose=TRUE , family="binomial")
# summary(mod1)
#   ##   Random effects:
#   ##    Groups   Name        Variance Std.Dev.
#   ##    id_index (Intercept) 1.454    1.206   
#   ##   Number of obs: 10000, groups: id_index, 1000
#   ##   
#   ##   Fixed effects:
#   ##                        Estimate Std. Error z value Pr(>|z|)    
#   ##   as.factor(item)I0001  2.16365    0.10541  20.527  < 2e-16 ***
#   ##   as.factor(item)I0002  1.66437    0.09400  17.706  < 2e-16 ***
#   ##   as.factor(item)I0003  1.21816    0.08700  14.002  < 2e-16 ***
#   ##   as.factor(item)I0004  0.68611    0.08184   8.383  < 2e-16 ***
#   ##   [...]
# ## End(Not run)