grm {ltm} | R Documentation |
Graded Response Model - Polytomous IRT
Description
Fits the Graded Response model for ordinal polytomous data, under the Item Response Theory approach.
Usage
grm(data, constrained = FALSE, IRT.param = TRUE, Hessian = FALSE,
start.val = NULL, na.action = NULL, control = list())
Arguments
data |
a |
constrained |
logical; if |
IRT.param |
logical; if |
Hessian |
logical; if |
start.val |
a list of starting values or the character string |
na.action |
the |
control |
a list of control values,
|
Details
The Graded Response Model is a type of polytomous IRT model, specifically designed for ordinal manifest variables. This model was first discussed by Samejima (1969) and it is mainly used in cases where the assumption of ordinal levels of response options is plausible.
The model is defined as follows
\log\left(\frac{\gamma_{ik}}{1-\gamma_{ik}}\right) = \beta_i z -
\beta_{ik},
where \gamma_{ik}
denotes the cumulative
probability of a response in category k
th or lower to the i
th item, given the latent ability z
.
If constrained = TRUE
it is assumed that \beta_i = \beta
for all i
.
If IRT.param = TRUE
, then the parameters estimates are reported under the usual IRT parameterization,
i.e.,
\log\left(\frac{\gamma_{ik}}{1-\gamma_{ik}}\right) = \beta_i (z - \beta_{ik}^*),
where \beta_{ik}^* = \beta_{ik} / \beta_i
.
The fit of the model is based on approximate marginal Maximum Likelihood, using the Gauss-Hermite quadrature rule for the approximation of the required integrals.
Value
An object of class grm
with components,
coefficients |
a named list with components the parameter values at convergence for each item. These are always
the estimates of |
log.Lik |
the log-likelihood value at convergence. |
convergence |
the convergence identifier returned by |
hessian |
the approximate Hessian matrix at convergence returned by |
counts |
the number of function and gradient evaluations used by the quasi-Newton algorithm. |
patterns |
a list with two components: (i) |
GH |
a list with two components used in the Gauss-Hermite rule: (i) |
max.sc |
the maximum absolute value of the score vector at convergence. |
constrained |
the value of the |
IRT.param |
the value of the |
X |
a copy of the response data matrix. |
control |
the values used in the |
na.action |
the value of the |
call |
the matched call. |
Warning
In case the Hessian matrix at convergence is not positive definite try to re-fit the model,
using start.val = "random"
.
Note
grm()
returns the parameter estimates such that the discrimination parameter for the first item
\beta_1
is positive.
When the coefficients' estimates are reported under the usual IRT parameterization (i.e., IRT.param = TRUE
),
their standard errors are calculated using the Delta method.
grm()
can also handle binary items, which should be coded as ‘1, 2’ instead of ‘0, 1’.
Some parts of the code used for the calculation of the log-likelihood and the score vector have been based
on polr()
from package MASS.
Author(s)
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
References
Baker, F. and Kim, S-H. (2004) Item Response Theory, 2nd ed. New York: Marcel Dekker.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34, 100–114.
Rizopoulos, D. (2006) ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17(5), 1–25. URL doi: 10.18637/jss.v017.i05
See Also
coef.grm
,
fitted.grm
,
summary.grm
,
anova.grm
,
plot.grm
,
vcov.grm
,
margins
,
factor.scores
Examples
## The Graded Response model for the Science data:
grm(Science[c(1,3,4,7)])
## The Graded Response model for the Science data,
## assuming equal discrimination parameters across items:
grm(Science[c(1,3,4,7)], constrained = TRUE)
## The Graded Response model for the Environment data
grm(Environment)