plot.fs.class: Plot fs.class objects
In fwdmsa: Forward search for Mokken scale analysis

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

View source: R/plot.fs.class.R

S3 Method to plot objects of class fs.class. Graphic display of the robust Mokken scale analysis by means of the Forward Search. Seven different plots can be obtained.

## S3 method for class 'fs.class'
plot(
 x, 
 type = "objective", 
 observations = all.observations, 
 id.observation = FALSE, 
 items = all.items, 
 id.item = FALSE, 
 step = default.step, 
 reference.step = default.reference.step,
 id.scale = default.scale,
 tukey.fences = TRUE,
 add = FALSE,
 n0 = FALSE,
 n1 = FALSE,
 n2 = FALSE, 
 lower.c = default.lower.c, 
 col = default.col, 
 lwd = default.lwd, 
 lty = default.lty, 
 ylim = default.ylim, 
 xlim = default.xlim,
 ...)

`x`	Object of `class fs.class` produced by `fs.MSA`.
`type`	Type of forward plot: `"objective"` (default): Forward plot showing the objective function for an observation over all subsample sizes. `"minexcl"`: Forward plot of the minimum objective function values of the observations exluded from the sample. `"maxincl"`: Forward plot of the maximum objective function values of the observations included in the sample. `"gap"`: Forward plot of the `minexcl`-`maxincl`. `"coefH"`: Forward plot of Loevinger's scalability coefficient for items (Hj) and the test (H). `"restscore"`: One graph for each item plotting the estimated expected item response functions. `"IRF"`: Forward plot of the estimated expected item response functions. `"followup"`: Forward plot of the observations entering and leaving the subsample at a specified step. The identitty of the obervations entering or leavind the subsample is provided in the plot. `"scale"`: Forward plot showing whether the selected `items` belong to a scale. `"num.scale"`: Forward plot of the number of scales found by the AISP.
`observations`	Vector containing the observations to show. The default uses all observations. Relevant for `type="objective"`.
`id.observation`	Vector containing the observation(s) for which the results are plotted in a different color and the identity of the observation(s) is added to the plot. Relevant for `type="objective"`.
`items`	Vector containing the items for which the results are plotted. Default the results for all items are depicted. Relevant for `type="coefH"`, `type="restscore"`, `type="IRF"`, and `type="scale"`.
`id.item`	Logical, if `TRUE` the identity of the items are added to the plot. Relevant for `type="coefH"`.
`step`	Single number or vector containing the subsample size. Default is sample size N. For `type="restscore"` `step` is a single number. For `type="individual"` `step` may be a vector. Relevant for `type="restscore"` and `type="individual"`.
`reference.step`	Single number containing the subsample size. Default is `step`-1. Relevant for `type="individual"`.
`id.scale`	Numeric indicating which scale to show; `id.scale`=0 indicates unscalable items, `id.scale`=1 indicates the longest scale, `id.scale`=2 indicates the next longest scale, etc. Default shows all scales. Relevant for `type="scale"`.
`tukey.fences`	Logical, if `TRUE` Tukey's Fences (Q3 + 1.5 * IQR and Q3 + 3 * IQR) are plotted. Relevant for `type="minexcl"`.
`add`	Logical, if `TRUE`, the plot is added to the current plot. The default is `FALSE`. Relevant for `type="objective"`, `type="minexcl"`, `type="maxincl"`, `type="gap"`, `type="coefH"`, and `type="restscore"`.
`n0`	Logical, indicating whether n0 should be added to the plots. Default is `FALSE`
`n1`	Default is `FALSE` otherwise a single number should be given
`n2`	Logical, indicating whether n2 should be added to the plots. Default is `FALSE`
`lower.c`	Numeric scaling criterium; 0 <= `lowerbound` < 1. The default is `0.3`.
`col`	Colour to be used for the lines.
`lwd`	Line width
`lty`	Line type
`ylim, xlim`	Range of `x` and `y` values with sensible defaults.
`...`	Other arguments are ignored.

add=TRUE for type="restscore" can only be used for one item (e.g., items=1).

Returns a graph.

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.

fs.MSA, fs.MSA.n1, 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))