ralertingp: Inferential test for ralerting-CV
In qcv: Quantifying Construct Validity
Description
Usage
Arguments
Details
Examples
Description
Computes a p value for the ralerting-CV effect size
Usage
1
ralertingp(actr, predr, iter)
Arguments
actr
vector of actual validity correlations
predr
vector of predicted validity correlations (in same order as actr)
iter
the number of randomization samples to be generated
Details
An inferential test for ralerting-CV, not presented in the original Westen and Rosenthal (2003).
The inferential test is based upon randomization procedures.
That is, given a set of predicted correlations and a set of actual correlations,
ralertingp() randomly pairs values from the two sets, computes an ralerting-CV
value, and repeats this process many times (as determined by the user).
The actual ralerting-CV value is then compared to the distribution of
ralerting-CV values derived from the randomization process.
The proportion of values from that distribution that are greater than the
actual ralerting-CV value is then taken as a p value.
Important: When entering values for the "actr" and "predr" arguments, order them identically. For example, if
the "actr" values are ordered in terms of c(criterion A, criterion B, criterion C), then the values in "predr" should
be placed in that same order. Otherwise results will be incorrect.
For relatively small values of k (the number of criterion variables), it is recommended to increase iter (the number of randomization samples.
For low-k situations, a small number of randomization samples can produce unstable p values.
A larger number of randomization samples would produce more stable results.
It is possible that the randomization process produces no values that are greater than the actual ralerting-CV value. In such
cases, ralertingp() reports the p value as < 1/iter.
Examples
1
2
3
actrIM <- c(.46, .13, -.24, -.03, .12, .03, .39, .06, .51, .08, .24, .66)
predrIM <- c(.58, .24, -.04, .06, -.04, .18, .36, .08, .64, .56, .36, .56)
ralertingp(actr=actrIM, predr=predrIM, iter=1000)
qcv documentation built on May 1, 2019, 9:15 p.m.
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Description Usage Arguments Details Examples
Description
Computes a p value for the ralerting-CV effect size
Usage
1 | ralertingp(actr, predr, iter)
|
Arguments
actr |
vector of actual validity correlations |
predr |
vector of predicted validity correlations (in same order as actr) |
iter |
the number of randomization samples to be generated |
Details
An inferential test for ralerting-CV, not presented in the original Westen and Rosenthal (2003). The inferential test is based upon randomization procedures. That is, given a set of predicted correlations and a set of actual correlations, ralertingp() randomly pairs values from the two sets, computes an ralerting-CV value, and repeats this process many times (as determined by the user). The actual ralerting-CV value is then compared to the distribution of ralerting-CV values derived from the randomization process. The proportion of values from that distribution that are greater than the actual ralerting-CV value is then taken as a p value.
Important: When entering values for the "actr" and "predr" arguments, order them identically. For example, if the "actr" values are ordered in terms of c(criterion A, criterion B, criterion C), then the values in "predr" should be placed in that same order. Otherwise results will be incorrect.
For relatively small values of k (the number of criterion variables), it is recommended to increase iter (the number of randomization samples. For low-k situations, a small number of randomization samples can produce unstable p values. A larger number of randomization samples would produce more stable results.
It is possible that the randomization process produces no values that are greater than the actual ralerting-CV value. In such cases, ralertingp() reports the p value as < 1/iter.
Examples
1 2 3 | actrIM <- c(.46, .13, -.24, -.03, .12, .03, .39, .06, .51, .08, .24, .66)
predrIM <- c(.58, .24, -.04, .06, -.04, .18, .36, .08, .64, .56, .36, .56)
ralertingp(actr=actrIM, predr=predrIM, iter=1000)
|
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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