| summary {ltm} | R Documentation |
Summarizes the fit of either grm, ltm, rasch or tpm objects.
## S3 method for class 'grm': summary(object, ...) ## S3 method for class 'ltm': summary(object, robust.se = FALSE, ...) ## S3 method for class 'rasch': summary(object, robust.se = FALSE, ...) ## S3 method for class 'tpm': summary(object, ...)
object |
an object inheriting from either class grm, class ltm, class rasch or class tpm. |
robust.se |
logical; if TRUE robust estimation of standard errors is used, based on the sandwich estimator. |
... |
additional argument; currently none is used. |
An object of either class summ.grm, class summ.ltm or class summ.rasch with components,
coefficients |
the estimated coefficients' table. |
Var.betas |
the approximate covariance matrix for the estimated parameters; returned only in summ.ltm
and summ.rasch. |
logLik |
the log-likelihood of object. |
AIC |
the AIC for object. |
BIC |
the BIC for object. |
max.sc |
the maximum absolute value of the score vector at convergence. |
conv |
the convergence identifier returned by optim(). |
counts |
the counts argument returned by optim(). |
call |
the matched call of object. |
ltn.struct |
a character vector describing the latent structure used in object; returned only in
summ.ltm. |
control |
the values used in the control argument in the fit of object. |
nitems |
the number of items in the data set; returned only in summ.ltm and summ.rasch. |
For the parameters that have been constrained, the standard errors and z-values are printed as NA.
When the coefficients' estimates are reported under the usual IRT parameterization (i.e., IRT.param = TRUE
in the call of either grm, ltm or rasch), their standard errors are calculated using the
Delta method.
Dimitris Rizopoulos dimitris.rizopoulos@med.kuleuven.be
# use Hessian = TRUE if you want standard errors fit <- grm(Science[c(1,3,4,7)], Hessian = TRUE) summary(fit) ## One factor model using the WIRS data; ## results are reported under the IRT ## parameterization fit <- ltm(WIRS ~ z1) summary(fit)