summary | R Documentation |
Summarizes the fit of either grm
, ltm
, rasch
or tpm
objects.
## S3 method for class 'gpcm' summary(object, robust.se = FALSE, ...) ## 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 |
robust.se |
logical; if |
... |
additional argument; currently none is used. |
An object of either class summ.gpcm
, 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 |
logLik |
the log-likelihood of |
AIC |
the AIC for |
BIC |
the BIC for |
max.sc |
the maximum absolute value of the score vector at convergence. |
conv |
the convergence identifier returned by |
counts |
the |
call |
the matched call of |
ltn.struct |
a character vector describing the latent structure used in |
control |
the values used in the |
nitems |
the number of items in the data set; returned only in |
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 d.rizopoulos@erasmusmc.nl
gpcm
,
grm
,
ltm
,
rasch
,
tpm
# 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)
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