View source: R/discrete.smooth.R
discrete.smooth | R Documentation |
This function fits discrete kernels to score data and provides estimates of the (vector of) score probabilities.
discrete.smooth(scores,kert,h,x)
scores |
Data. |
kert |
kernel type. |
h |
bandwidth. |
x |
The points of the grid at which the density is to be estimated. |
This function fits discrete kernels as described in XXXX, and XXXXX.
Particular cases of this general equation for each of the equating designs can be found in Von Davier et al (2004) (e.g., Equations (7.1) and (7.2) for the "EG" design, Equation (8.1) for the "SG" design, Equations (9,1) and (9.2) for the "CB" design).
prob.est |
The estimated score probabilities |
Jorge Gonzalez jorge.gonzalez@mat.uc.cl
Gonzalez, J. (2014). SNSequate: Standard and Nonstandard Statistical Models and Methods for Test Equating. Journal of Statistical Software, 59(7), 1-30.
Holland, P. and Thayer, D. (1987). Notes on the use of loglinear models for fitting discrete probability distributions. Research Report 87-31, Princeton NJ: Educational Testing Service.
Von Davier, A., Holland, P., and Thayer, D. (2004). The Kernel Method of Test Equating. New York, NY: Springer-Verlag.
[1] Moses, T. "Paper SA06_05 Using PROC GENMOD for Loglinear Smoothing Tim Moses and Alina A. von Davier, Educational Testing Service, Princeton, NJ".
glm
, ker.eq
data("SEPA", package = "SNSequate")
# create score frequency distributions using freqtab from package equate
library(equate)
SEPAx<-freqtab(x=SEPA$xscores,scales=0:50)
SEPAy<-freqtab(x=SEPA$yscores,scales=0:50)
psxB<-discrete.smooth(scores=rep(0:50,SEPAx),kert="bino",h=0.25,x=0:50)
psxT<-discrete.smooth(scores=rep(0:50,SEPAx),kert="triang",h=0.25,x=0:50)
psxD<-discrete.smooth(scores=rep(0:50,SEPAx),kert="dirDU",h=0.0,x=0:50)
plot(0:50,as.matrix(SEPAx)/sum(as.matrix(SEPAx)),lwd=2.0,xlab="Scores",
ylab="Relative Frequency",type="h")
points(0:50,psxB$prob.est,type="b",pch=0)
points(0:50,psxT$prob.est,type="b",pch=1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.