PredPercentileNN: Experimental function for getting percentiles using a...

In CambridgeAssessmentResearch/KernEqWPS: Kernel Equating Without Pre-Smoothing

Experimental function for getting percentiles using a standard neural network

Description

This function takes a data frame of test form and anchor scores and estimates the values of the percentiles (1st-99th) for given change in the distribution of anchor scores.

Usage

PredPercentileNN(
  dx,
  anchortargettable = NA,
  maxx = NA,
  maxa = NA,
  WeightsList = EquateNNWeights
)

Arguments

dx	Data frame with variables "x" and "a" representing scores for individual candidates on form X and on the anchor test.
anchortargettable	Table giving distribution of anchor test scores in the target population.
maxx	Maximum score available on form X (calculated from the data by default).
maxa	Maximum score available on anchor test (calculated from the data by default).
WeightsList	A list of neural network parameters used in calculations. Changing this from the default value is not recommended.

Examples

#example (compare real and estimated percentiles within a fixed population)
n1=1000
t1=rnorm(n1,0.5,1)
dx=data.frame(x=round(pmin(100,pmax(0,50+20*(0.9*t1+rnorm(n1,0,sqrt(1-0.9^2))))))
	,a=round(pmin(10,pmax(0,5+2*(0.7*t1+rnorm(n1,0,sqrt(1-0.7^2)))))))

percNN=PredPercentileNN(dx,table(dx$a),maxx=100)
usualperc=as.vector(quantile(dx$x,seq(0.01,0.99,0.01)))
plot(1:99,usualperc,type='l',xlab="Percentile",ylab="Value")
lines(1:99,percNN,lty=2)

CambridgeAssessmentResearch/KernEqWPS documentation built on Feb. 23, 2024, 9:34 p.m.