jml {MiscPsycho} | R Documentation |
JML is used to estimate item parameters for the Rasch model.
jml(...) ## Default S3 method: jml(dat, con = 1e-3, bias=FALSE, ...) ## S3 method for class 'formula': jml(formula, data, na.action, subset, con = 1e-3, bias = FALSE, ...)
formula |
an object of class "formula" (or one that can be
coerced to that class): a symbolic description of the model
to be fitted. The details of model specification are given
under Details . |
data |
an optional data frame, list or environment (or object
coercible by as.data.frame to a data frame) containing
the variables in the model. If not found in data , the
variables are taken from environment(formula) ,
typically the environment from which jml is called. |
dat |
A data frame or matrix with item responses. Implemented only for the jml.default method. |
na.action |
a function which indicates what should happen when the data
contain NA s. Defaults to getOption("na.action") . |
subset |
an optional vector specifying a subset of observations to be used. |
con |
Convergence criterion |
bias |
Implements the correction for bias as described by Wright and Stone |
... |
Not implemented |
Models for jml
are specified symbolically. A typical model
has the form ~item1 + item2
where the terms to the right of the
~
are the columns of the data matrix containing the binary item responses.
A list with class "jml"
containing the following components:
Estimate |
the value of Rasch item parameter (b-value) |
Std.Error |
the standard error of the item parameter |
Infit |
The Rasch infit statistic |
Outfit |
The Rasch outfit statistic |
Iterations |
the number of Newton-Raphson iterations used |
model.frame |
the data matrix used for estimating item parameters. In JML estimation,
individuals with all items correct and all items incorrect cannot be used in the calibration. Hence, they are
dropped from the original data matrix provided by the data argument. |
Harold Doran
set.seed(1234) tmp <- data.frame(item1 = sample(c(0,1), 20, replace=TRUE), item2 = sample(c(0,1), 20, replace=TRUE), item3 = sample(c(0,1), 20, replace=TRUE),item4 = sample(c(0,1), 20, replace=TRUE),item5 = sample(c(0,1), 20, replace=TRUE)) ## Formula interface fm1 <- jml(~ item1 + item2 + item3 + item4 + item5, data = tmp) summary(fm1) coef(fm1) plot(fm1) ## Default interface fm1 <- jml(tmp) summary(fm1)