gen_test_score {lazy.tools} | R Documentation |
This function generates a frequency distribution table of n discrete test scores in [minscore,maxscore].
gen_test_score(n, minscore = 0, maxscore = 100, normal = c(50, 15), beta = NULL, dump = 0, print = 0, plot = 0, title = "Score Distribution")
n |
# of observations. |
minscore |
The minimum score. |
maxscore |
The maximum score. |
normal |
Vector consisting of mean and std for normal distribution. |
beta |
Vector of alpha and beta parameters for beta distribution in [minscore,maxscore]. |
dump |
= 1 to output the discrete random numbers. |
print |
= 1 to print the result. |
plot |
= 1 to plot the result. |
title |
Title string to be used in plot. |
This function generates a frequency distribution table of n discrete test scores in [minscore,maxscore].
Continuous random numbers, either normal or generalized beta, are first generated and then they are discretized by cut function. Those values outside of [minscore,maxscore] will be set to minscore or maxscore, resp.
beta parameter has priority over nomal parameter.
If dump == 0, a matrix consisting of (score, frequency).
otherwise, a list of rv, random numbers, and freqdist matrix.
seed <- 1701 set.seed(seed) minscore <- 0; maxscore <- 50 n <- 500 scoredist1 <- gen_test_score( n, minscore, maxscore, beta=c(4,8), plot=1 ) scoredist2 <- gen_test_score( n, minscore, maxscore, normal=c(30,5), plot=1) # mixture scoredist12 <- merge_tables( scoredist1,scoredist2 ) barplot(height=scoredist12[,2], names.arg=as.character(scoredist12[,1]) , main="Score Dist", cex.names=.65, space=0, las=2)