hisemi (version 1.1-0)

hisemi-package: Hierarchical semiparametric regression model to a large number of parametric test statistics

Description

The package includes functions for fitting hierarchical semiparametric regression model to a large number of parametric test statistics

Details

Package:	hisemi
Type:	Package
Version:	1.1-0
Date:	2017-07-09
License:	GPL version 2 or newer
LazyLoad:	yes

• penLik.EMNewton is the major interface function to be called.

• coef.hisemit extracts the estimated parameters.

• confint.hisemit returns the confidence intervals.

• directSum computes the direct sum of matrices.

• EMupdate is the EM algorithm used in penLik.EMNewton.

• fitted.hisemit extracts the fitted values.

• logLik.hisemit returns the log likelihood.

• NRupdate is the Newton-Raphson algorithm used in penLik.EMNewton.

• plot.hisemit plots the fitted model.

• print.hisemit print summary information.

• residuals.hisemit returns the residuals.

• scaledTMix.null fits the null model with common pi0.

• scaledTMix.psat fits the partially saturated model with free pi0 and common scale factor.

• scaledTMix.sat fits the completely saturated model with free pi0 and free scale factor.

• vcov.hisemit returns the sandwich variance-covariance matrix.

References

Long Qu, Dan Nettleton, Jack Dekkers (2012). A hierarchical semiparametric model for incorporating inter-gene relationship information for analysis of genomic data. Biometrics, 68(4):1168-1177

See Also

pi0-package

Examples

# NOT RUN {
## simulate some fake data
G=100  ## for demonstration only. Normally, G should be much larger
sdncp=1.3
n1=n2=5
df=n1+n2-2
set.seed(54457704)
x=runif(G,1,G)
f=function(x)sin(x*pi/1000)+1
Pi.i=1/(1+exp(f(x)))
Z.i=rbinom(G,1,1-Pi.i)
t0.i=rt(G,df)
ncp.i=rnorm(G,0,sdncp)
t1.i=rt(G,df, ncp.i)
t.i=ifelse(Z.i==0,t0.i,t1.i)

## fit model
(plfit=penLik.EMNewton(t.i, x, df, spar=10^seq(0,8,length=30),plotit=FALSE))
(plfit0=scaledTMix.null(t.i, df))

# }
# NOT RUN {
plot(plfit)
plot(t.i, plfit$lfdr, pch='.')
lines(sort(t.i), plfit0$lfdr[order(t.i)], col=2, lwd=3)
# }