Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the necessary input for forward plots for Mokken scale analysis
1 2 3 4 5 6 7 8 |
X |
Matrix or data frame of numeric data containing the responses of |
initial.subsample |
Default is "random". Otherwise, a vector containing the rows of |
initial.subsample.size |
Integer giving the size of the initial subsample.
Only effective if |
minsize |
Integer giving the minimum size of a rest score group. By default
|
seed |
Numeric; fixes the random number generation |
n.low |
Numeric; n2 should be larger than |
verbose |
Logical, indicating whether the subsample size should be printed on the screen. If |
Function fs.MSA
computes the required input for forward plots (plot.fs.class
).
Therefore, its values should be assigned to an object.
Function fs.MSA
may take a long time for data if the number of items and/or observations is large.
A large initial.subsample.size
reduces the computation time but may affect the results.
Object of class fs.class
containing the required input for forward plots (plot.fs.class
).
Only few of the items are of direct interest:
initial.subsample.size
. Shows n0.
n2
. Shows n2.
suspect
. Shows the suspect observations.
order.objective.function
. Shows the observations in descending order of their objective function values, for each subsample.
Hence, the element row 1 and column 189 is the most suspect observation given the subsample of size 189.
W. P. Zijlstra w.p.zijlstra@uvt.nl
Zijlstra, W. P., Van der Ark, L. A., and Sijtsma, K. (2011). Robust Mokken scale analysis by means of the forward search algorithm for outlier detection. Multivariate Behavioral Research, 46, 58-89.
Van der Ark, L. A. (2007). Mokken scale analysis in R. Journal of Statistical Software. http://www.jstatsoft.org
fs.MSA.n1
, plot.fs.class
, plot.fs.n1.class
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # Retrieve data (588 observations)
data(acs)
# Run Forward Search for Mokken scale analysis starting with
# 550 observations in the initial subsample size to save time
fwdmsa.res <- fs.MSA(acs, initial.subsample.size=550)
# Plot the objective function
plot(fwdmsa.res, xlim = c(540,588))
# Plot the objective function for observations 1, 2, and 4
plot(fwdmsa.res, id.observation = c(1,2,4), add=TRUE, col=2, xlim = c(540,588))
# Gap plot for subsamples 570 through 588
plot(fwdmsa.res, type = "gap", ylim = c(0,4), xlim = c(570,588))
# Follow-up plots
plot(fwdmsa.res, type="followup", step=560:565, reference.step=560, xlim = c(540,588))
# Min-excl plot.
plot(fwdmsa.res, type = "minexcl", n2=TRUE, xlim=c(540,588))
# Plot of number of scales
plot(fwdmsa.res, type="num.scale", n2=TRUE, xlim=c(540,588))
# Item entry plot for the longest scale
plot(fwdmsa.res, type="scale", id.scale=1, n2=TRUE, xlim=c(540,588))
# Plot of estimated IRF of item 1
plot(fwdmsa.res, type="IRF", items=1, n2=TRUE, xlim=c(540,588))
# Plot of coefH
plot(fwdmsa.res, type="coefH", n2=TRUE, ylim=c(.1,.8), xlim=c(540,588))
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