View source: R/r_identified_function.R
r_identified | R Documentation |
This function samples random variates from the distribution of true scores for identified students using rejection sampling.
r_identified(
n,
relyt = 1,
test.cutoff,
valid = 1e-07,
nom.cutoff = 1e-07,
mu = 0
)
n |
The number of values to sample. |
relyt |
Confirmatory test reliability coefficient. Range (0, 1].
Must not be exactly 0. Defaults to 1; in this case, the returned values are
observed scores. If an alternative value is supplied for
|
test.cutoff |
Confirmatory test cutoff percentile. Range (0, 1). Must not be exactly 0 or 1. |
valid |
Nomination validity coefficient. Controls the relatedness of the nomination scores and the confirmatory test scores. Range (0, 1). Must not be exactly 0 or 1, and must be less than the square root of the test reliability. |
nom.cutoff |
Nomination cutoff percentile. Range (0, 1). Must not be exactly 0 or 1. |
mu |
Population mean true score on a standardized (z-score) metric. Defaults to zero. |
The returned values are interpreted as true scores if a value is provided for
argument relyt
; otherwise, they are observed scores.
See also d_identified
for the normalized density, p_identified
for the cumulative density function, and q_identified
for the quantile
function.
# generate true scores
r_identified(
n = 10, relyt = .9, valid = .6,
test.cutoff = .9, nom.cutoff = .1, mu = 0
)
# generate observed scores
r_identified(
n = 10, relyt = .9, valid = .6,
test.cutoff = .9, nom.cutoff = .1, mu = 0
)
# make a histogram of data from 100000 draws
draws <- r_identified(
n = 100000, relyt = .99, valid = .6,
test.cutoff = .95, nom.cutoff = .9, mu = 0
)
hist(draws, breaks = 80, freq = FALSE, xlab = "True score")
# superimpose the theoretical density
# create vector of true scores
Tscores <- seq(0, 4, length.out = 200)
# add the density to the histogram
p.id <- sapply(Tscores, d_identified,
relyt = .99,
test.cutoff = .95, nom.cutoff = .9, valid = .6
)
points(x = Tscores, y = p.id, type = "l", col = "red")
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