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

Generates simulated rating scale data given item measures, person measures and rating category thresholds.

1 | ```
simdata(items, persons, thresholds, missingProb = 0, minRating = 0)
``` |

`items` |
a numeric vector of item measures with no NA. |

`persons` |
a numeric vector of person measures with no NA. |

`thresholds` |
a numeric vector of ordered rating category thresholds with no NA. |

`missingProb` |
a number between 0 and 1 specifying the probability of missing data. |

`minRating` |
integer representing the smallest ordinal rating category. Default is 0 (see Details). |

It is assumed that the set of ordinal rating categories consists of all integers from the lowest rating category specified by `minRating`

to the highest rating category,
which is `minRating + length(thresholds)`

.

A numeric matrix of simulated rating scale data.

`simdata`

can be used to test the accuracy of `msd`

(see Examples).

Chris Bradley (cbradley05@gmail.com)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# Use simdata to test the accuracy of msd. First, randomly generate item
# measures, person measures and thresholds with 15 percent missing data and
# ordinal rating categories from 0 to 5. Then, set mean item measure to zero
# (axis origin in msd is the mean item measure) and mean threshold to zero
# (any non-zero mean threshold is reflected in the person measures).
im <- runif(100, -2, 2)
pm <- runif(100, -2, 2)
th <- sort(runif(5, -2, 2))
im <- im - mean(im)
th <- th - mean(th)
d <- simdata(im, pm, th, missingProb = 0.15, minRating = 0)
m <- msd(d)
# Compare msd parameters to true values. Linear regression should
# yield a slope very close to 1 and an intercept very close to 0.
lm(m$item_measures ~ im)
lm(m$person_measures ~ pm)
lm(m$thresholds ~ th)
``` |

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