Create expected values for fixed effects parameters in latent regression models.
fixef(x)Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06
Chalmers, R. P. (2015). Extended Mixed-Effects Item Response Models with the MH-RM Algorithm. Journal of Educational Measurement, 52, 200-222. doi:10.1111/jedm.12072
# \donttest{
#simulate data
set.seed(1234)
N <- 1000
# covariates
X1 <- rnorm(N); X2 <- rnorm(N)
covdata <- data.frame(X1, X2)
Theta <- matrix(0.5 * X1 + -1 * X2 + rnorm(N, sd = 0.5))
#items and response data
a <- matrix(1, 20); d <- matrix(rnorm(20))
dat <- simdata(a, d, 1000, itemtype = '2PL', Theta=Theta)
#conditional model using X1 and X2 as predictors of Theta
mod1 <- mirt(dat, 1, 'Rasch', covdata=covdata, formula = ~ X1 + X2)
#latent regression fixed effects (i.e., expected values)
fe <- fixef(mod1)
head(fe)
#> F1
#> [1,] 0.6129074
#> [2,] -0.1661799
#> [3,] 2.1652793
#> [4,] -1.8932638
#> [5,] -0.5021655
#> [6,] 2.2405972
# with mixedmirt()
mod1b <- mixedmirt(dat, covdata, 1, lr.fixed = ~ X1 + X2, fixed = ~ 0 + items)
fe2 <- fixef(mod1b)
head(fe2)
#> F1
#> [1,] 0.6165760
#> [2,] -0.1671332
#> [3,] 2.1737979
#> [4,] -1.8995517
#> [5,] -0.5047064
#> [6,] 2.2499237
# }