information | R Documentation |

returns information function, expected score function, score simulation function, or score distribution for a single item, an arbitrary group of items or all items

information(parms, items = NULL, booklet_id = NULL, which.draw = NULL) expected_score(parms, items = NULL, booklet_id = NULL, which.draw = NULL) r_score(parms, items = NULL, booklet_id = NULL, which.draw = NULL) p_score(parms, items = NULL, booklet_id = NULL, which.draw = NULL)

`parms` |
object produced by |

`items` |
vector of one or more item_id's. If NULL and booklet_id is also NULL, all items in parms are used |

`booklet_id` |
id of a single booklet (e.g. the test information function), if items is not NULL this is ignored |

`which.draw` |
the number of the random draw (only applicable if calibration method was Bayes). If NULL, the mean beta parameter will be used |

Each function returns a new function which accepts a vector of theta's. These return the following values:

- information
an equal length vector with the information estimate at each value of theta.

- expected_score
an equal length vector with the expected score at each value of theta

- r_score
a matrix with length(theta) rows and one column for each item containing simulated scores based on theta. To obtain test scores, use rowSums on this matrix

- p_score
a matrix with length(theta) rows and one column for each possible sumscore containing the probability of the score given theta

db = start_new_project(verbAggrRules,':memory:') add_booklet(db,verbAggrData, "agg") p = fit_enorm(db) # plot information function for single item ifun = information(p, "S1DoScold") plot(ifun,from=-4,to=4) # compare test information function to the population ability distribution ifun = information(p, booklet="agg") pv = plausible_values(db,p) op = par(no.readonly=TRUE) par(mar = c(5,4,2,4)) plot(ifun,from=-4,to=4, xlab='theta', ylab='test information') par(new=TRUE) plot(density(pv$PV1), col='green', axes=FALSE, xlab=NA, ylab=NA, main=NA) axis(side=4) mtext(side = 4, line = 2.5, 'population density (green)') par(op) close_project(db)

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