View source: R/immer_latent_regression.R
immer_latent_regression | R Documentation |
Fits a unidimensional latent regression θ_{ig}=Y_{ig} \bold{β} + \varepsilon_{ig} with group-specific variances Var(\varepsilon _{ig} )=σ^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 L( \bold{X} | θ ) |
theta |
Grid of \bold values |
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 \bold{β} parameters |
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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