View source: R/immer_latent_regression.R
immer_latent_regression | R Documentation |
Fits a unidimensional latent regression
\theta_{ig}=Y_{ig} \bm{\beta} + \varepsilon_{ig}
with group-specific variances Var(\varepsilon _{ig} )=\sigma^2_g
based on
the individual likelihood of a fitted model.
immer_latent_regression(like, theta=NULL, Y=NULL, group=NULL, weights=NULL,
conv=1e-05, maxit=200, verbose=TRUE)
## S3 method for class 'immer_latent_regression'
summary(object, digits=3, file=NULL, ...)
## S3 method for class 'immer_latent_regression'
coef(object, ...)
## S3 method for class 'immer_latent_regression'
vcov(object, ...)
## S3 method for class 'immer_latent_regression'
logLik(object, ...)
## S3 method for class 'immer_latent_regression'
anova(object, ...)
like |
Matrix containing the individual likelihood |
theta |
Grid of |
Y |
Predictor matrix |
group |
Group identifiers |
weights |
Optional vector of weights |
conv |
Convergence criterion |
maxit |
Maximum number of iterations |
verbose |
Logical indicating whether progress should be displayed |
object |
Object of class |
digits |
Number of digits after decimal to print |
file |
Name of a file in which the output should be sunk |
... |
Further arguments to be passed. |
List containing values (selection)
coef |
Parameter vector |
vcov |
Covariance matrix for estimated parameters |
beta |
Regression coefficients |
gamma |
Standard deviations |
beta_stat |
Data frame with |
gamma_stat |
Data frame with standard deviations |
ic |
Information criteria |
deviance |
Deviance |
N |
Number of persons |
G |
Number of groups |
group |
Group identifier |
iter |
Number of iterations |
The IRT.likelihood
method can be used for
extracting the individual likelihood.
Adams, R. J., & Wu, M. L. (2007). The mixed-coefficients multinomial logit model. A generalized form of the Rasch model. In M. von Davier & C. H. Carstensen (Eds.): Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 55-76). New York: Springer.
See TAM::tam.latreg
for latent regression estimation
in the TAM package.
## Not run:
#############################################################################
# EXAMPLE 1: Latent regression for Rasch model with simulated data
#############################################################################
library(sirt)
#-- simulate data
set.seed(9877)
I <- 15 # number of items
N <- 700 # number of persons per group
G <- 3 # number of groups
b <- seq(-2,2,len=I)
group <- rep( 1:G, each=N)
mu <- seq(0,1, length=G)
sigma <- seq(1, 1.5, length=G)
dat <- sirt::sim.raschtype( stats::rnorm( N*G, mean=mu[group], sd=sigma[group] ), b)
#-- estimate Rasch model with JML
mod1 <- immer::immer_jml( dat )
summary(mod1)
#-- compute individual likelihood
like1 <- IRT.likelihood(mod1)
#-- estimate latent regression
mod2 <- immer::immer_latent_regression( like=like1, group=group)
summary(mod2)
## End(Not run)
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