fs.msa: Forward Search for Mokken Scale Analysis
In fwdmsa: Forward search for Mokken scale analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

Computes the necessary input for forward plots for Mokken scale analysis

fs.MSA(
 X, 
 initial.subsample = "random", 
 initial.subsample.size = default.initial.subsample.size, 
 minsize = default.minsize, 
 seed = default.seed,
 n.low = default.n.low,  
 verbose = TRUE)

`X`	Matrix or data frame of numeric data containing the responses of `nrow(X)` respondents to `ncol(X)` items. Each row is called an observation. Each item has m+1 response options 0, …, m. Other scores (e.g., 1, …, m+1), are converted to 0, …, m. Missing values are not allowed.
`initial.subsample`	Default is "random". Otherwise, a vector containing the rows of `X` that make up the initial subsample.
`initial.subsample.size`	Integer giving the size of the initial subsample. Only effective if `initial.subsample="random"`. By default `initial.subsample.size` equals the minimum of the number of restscore groups over all items multiplied by the number of items. If `initial.subsample` is not `"random"`, the size of the initial subsample equals the length of the vector provided at `initial.subsample`.
`minsize`	Integer giving the minimum size of a rest score group. By default `minsize` = N/10 if N ≥ 500; `minsize` = N/5 if 250 ≤ N < 500; and `minsize` = max(N/3,50) if N < 250
`seed`	Numeric; fixes the random number generation `set.seed` in order to control the initial subsample. Default is a randomly drawn value between 1 and 10000.
`n.low`	Numeric; n2 should be larger than `n.low`. Default `n.low` = N/4.
`verbose`	Logical, indicating whether the subsample size should be printed on the screen. If `FALSE`, no output is produced. The default is `TRUE`.

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

# 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))