Computation of Andersen's LR-test.
This LR-test is based on subject subgroup splitting.
## S3 method for class 'Rm'
LRtest(object, splitcr = "median", se = TRUE)
## S3 method for class 'LR'
plotGOF(x, beta.subset = "all", main = "Graphical Model Check", xlab, ylab,
tlab = "item", xlim, ylim, type = "p", pos = 4, conf = NULL, ctrline = NULL,
smooline = NULL, asp = 1, x_axis = TRUE, y_axis = TRUE, set_par = TRUE,
reset_par = TRUE, ...)object |
Object of class |
splitcr |
Split criterion for subject raw score splitting.
|
se |
controls computation of standard errors in the submodels (default: |
x |
Object of class |
beta.subset |
If |
main |
Title of the plot. |
xlab |
Label on x-axis, default gives name of |
ylab |
Label on y-axis, default gives name of |
tlab |
Specification of item labels: |
xlim |
Limits on x-axis. |
ylim |
Limits on y-axis. |
type |
Plotting type (see |
pos |
Position of the item label (see |
conf |
for plotting confidence ellipses for the item parameters.
If |
ctrline |
for plotting confidence bands (control lines, cf. eg. Wright and Stone, 1999).
If |
smooline |
spline smoothed confidence bands; must be specified as a list with optional elements: |
asp |
sets the y/x ratio of the plot (see |
x_axis |
if |
y_axis |
if |
set_par |
if |
reset_par |
if |
... |
additional parameters. |
If the data set contains missing values and mean or median is specified as split criterion, means or medians are calculated for each missing value subgroup and consequently used for raw score splitting.
When using interactive selection for both labelling of single points (tlab = "identify" and drawing confidence ellipses at certain points (ia = TRUE) then first all plotted points are labelled and afterwards all ellipses are generated.
Both identification processes can be terminated by clicking the second (right) mouse button and selecting ‘Stop’ from the menu, or from the ‘Stop’ menu on the graphics window.
Using the specification which in allows for selectively drawing ellipses for certain items only, e.g., which = 1:3 draws ellipses for items 1 to 3 (as long as they are included in beta.subset).
The default is drawing ellipses for all items.
The element col in the conf list can either be a single color specification such as "blue" or a vector with color specifications for all items.
The length must be the same as the number of ellipses to be drawn.
For color specification a palette can be set up using standard palettes (e.g., rainbow) or palettes from the colorspace or RColorBrewer package.
An example is given below.
summary and print methods are available for objects of class LR.
LRtest returns an object of class LR containing:
LR |
LR-value. |
df |
Degrees of freedom of the test statistic. |
Chisq |
Chi-square value with corresponding df. |
pvalue |
P-value of the test. |
likgroup |
Log-likelihood values for the subgroups |
betalist |
List of beta parameters for the subgroups. |
selist |
List of standard errors of beta's. |
etalist |
List of eta parameters for the subgroups. |
spl.gr |
Names and levels for |
call |
The matched call. |
fitobj |
List containing model objects from subgroup fit. |
Patrick Mair, Reinhold Hatzinger, Marco J. Maier, Adrian Bruegger
Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.
Mair, P., and Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1-20.
Mair, P., and Hatzinger, R. (2007). CML based estimation of extended Rasch models with the eRm package in R. Psychology Science, 49, 26-43.
Wright, B.D., and Stone, M.H. (1999). Measurement essentials. Wide Range Inc., Wilmington. (https://www.rasch.org/measess/me-all.pdf 28Mb).
# the object used is the result of running ... RM(raschdat1)
res <- raschdat1_RM_fitted # see ? raschdat1_RM_fitted
# LR-test on dichotomous Rasch model with user-defined split
splitvec <- sample(1:2, 100, replace = TRUE)
lrres <- LRtest(res, splitcr = splitvec)
lrres
summary(lrres)
## Not run:
# goodness-of-fit plot with interactive labelling of items w/o standard errors
plotGOF(lrres, tlab = "identify")
## End(Not run)
# LR-test with a full raw-score split
X <- sim.rasch(1000, -2:2, seed = 5)
res2 <- RM(X)
full_lrt <- LRtest(res2, splitcr = "all.r")
full_lrt
## Not run:
# LR-test with mean split, standard errors for beta's
lrres2 <- LRtest(res, split = "mean")
## End(Not run)
# to save computation time, the results are loaded from raschdat1_RM_lrres2
lrres2 <- raschdat1_RM_lrres2 # see ?raschdat1_RM_lrres2
# goodness-of-fit plot
# additional 95 percent control line with user specified style
plotGOF(lrres2, ctrline = list(gamma = 0.95, col = "red", lty = "dashed"))
# goodness-of-fit plot for items 1, 14, 24, and 25
# additional 95 percent confidence ellipses, default style
plotGOF(lrres2, beta.subset = c(14, 25, 24, 1), conf = list())
## Not run:
# goodness-of-fit plot for items 1, 14, 24, and 25
# for items 1 and 24 additional 95 percent confidence ellipses
# using colors for these 2 items from the colorspace package
library("colorspace")
my_colors <- rainbow_hcl(2)
plotGOF(lrres2, beta.subset = c(14, 25, 24, 1),
conf = list(which = c(1, 14), col = my_colors))
## End(Not run)
# first, save current graphical parameters in an object
old_par <- par(mfrow = c(1, 2), no.readonly = TRUE)
# plots
plotGOF(lrres2, ctrline = list(gamma = 0.95, col = "red", lty = "dashed"),
xlim = c(-3, 3), x_axis = FALSE, set_par = FALSE)
axis(1, seq(-3, 3, .5))
plotGOF(lrres2, conf = list(), xlim = c(-3, 3), x_axis = FALSE, set_par = FALSE)
axis(1, seq(-3, 3, .5))
text(-2, 2, labels = "Annotation")
# reset graphical parameters
par(old_par)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.