| iee.ord {weightedCL} | R Documentation |
Maximum Likelihood for Ordinal Model
Description
Maximum Likelihood for Ordinal Probit and Logit: Newton-Raphson minimization of negative log-likelihood.
Usage
iee.ord(x,y,link,iprint=0,maxiter=20,toler=1.e-6)
Arguments
x |
vector or matrix of explanatory variables. Each row corresponds to an observation and each column to a variable. The number of rows of x should equal the number of data values in y, and there should be fewer columns than rows. Missing values are not allowed. |
y |
numeric vector containing the ordinal response. The values must be in the range 1,2,..., number of categories. Missing values are not allowed. |
link |
The link function.Choices are “logit” for the logit link function, and “probit” for the probit link function. |
iprint |
logical indicator, default is FALSE, for whether the iterations for numerical maximum likelihood should be printed. |
maxiter |
maximum number of Newton-Raphson iterations, default = 20. |
toler |
tolerance for convergence in Newton-Raphson iterations, default = 1.e-6. |
Details
The ordinal probit model is similar to the ordinal logit model. The parameter estimate of ordinal logit are roughly 1.8 to 2 times those of ordinal probit.
Value
list of MLE of parameters and their associated standard errors, in the order cutpt1,...,cutpt(number of categ-1),b1,...b(number of covariates).
negloglik |
value of negative log-likelihood, evaluated at MLE |
gam |
MLE of ordered cutpoint parameters |
reg |
MLE of regression parameters |
cov |
estimated covariance matrix of the parameters |
References
Anderson, J.A. and Pemberton, J.D. (1985). The grouped continuous model for multivariate ordered categorical variables and covariate adjustment. Biometrics, 41, 875–885.
Examples
################################################################################
# Ordinal regression
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# read and set up data set
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data(sleep)
ydat=sleep$sleep
bydat=oydat=ydat
bydat[ydat==4]=0
bydat[ydat<4]=1
oydat[ydat==4]=1
oydat[ydat<4]=2
oydat[ydat==2]=3
oydat[ydat==3]=4
x1=sleep$heartrate
x2=sleep$temperature
z1=(x1-mean(x1))/sd(x1)
z2=(x2-mean(x2))/sd(x2)
xdat=cbind(z1,z2)
################################################################################
# select the link
################################################################################
link="probit"
i.est<-iee.ord(xdat,ydat,link)
cat("\niest: IEE estimates\n")
print(c(i.est$reg,i.est$gam))