# msdprob: Rating Category Probabilities In msd: Method of Successive Dichotomizations

## Description

Estimates the probability of observing each rating category given a set of ordered rating category thresholds.

## Usage

 `1` ```msdprob(x, thresholds) ```

## Arguments

 `x` a real number or a vector of real numbers with no NA representing a set of person minus item measures. `thresholds` a numeric vector of ordered rating category thresholds with no NA.

## Details

It is assumed that `thresholds` partitions the real line into `length(thresholds)+1` ordered intervals that represent the rating categories.

## Value

A matrix of probabilities where each of the `length(thresholds)+1` rows represents a different rating category (lowest rating category is the top row) and each of the `length(x)` columns represents a different person minus item measure.

## Note

`msdprob` can be used to create probability curves, which represent the probability of rating an item with each rating category as a function of the person measure minus item measure (see Examples).

## Author(s)

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# Simple example p <- msdprob(c(1.4, -2.2), thresholds = c(-1.1, -0.3, 0.5, 1.7, 2.2)) # Plot probability curves — each curve represents the probability of # rating an item with a given rating category as a function of the # person measure minus item measure. x <- seq(-6, 6, 0.1) p <- msdprob(x, thresholds = c(-3.2, -1.4, 0.5, 1.7, 3.5)) plot(0, 0, xlim = c(-6, 6), ylim = c(0, 1), type = "n", xlab = "Person minus item measure", ylab = "Probability") for (i in seq(1, dim(p))){ lines(x, p[i,], type = "l", lwd = "2" , col = rainbow(6)[i]) } ```