Nothing
checkglms = function() {
#
# Dec 4 -- so far, linear/gaussian model demonstrated
#
require(MASS)
data(anorexia)
myr = makeRegistry("abc", file.dir=tempfile())
chs = chunk(1:nrow(anorexia), n.chunks=18) # 4 recs/chunk
f = function(x) anorexia[x,]
batchMap(myr, f, chs)
submitJobs(myr) # now getResult(myr,1) gives back a data.frame
waitForJobs(myr) # simple dispersal
pp = parGLM( Postwt ~ Treat + Prewt, myr,
family=gaussian, binit = c(0,0,0,0), maxit=10, tol=.001 )
theLM <- lm(Postwt~Treat+Prewt, data=anorexia)
checkTrue(max(abs(pp$coefficients - coef(theLM))) < 1e-5 )
require(sandwich)
hc0 <- vcovHC(theLM, type="HC0")
checkTrue(max(abs(pp$robust.variance - hc0)) < 1e-5)
#
# logistic regression
#
library(datasets)
data(infert)
library(BatchJobs)
library(parglms)
myr = makeRegistry("abc", file.dir=tempfile())
chs = chunk(1:nrow(infert), n.chunks=31)
f = function(x) infert[x,]
batchMap(myr, f, chs)
submitJobs(myr)
waitForJobs(myr)
m1 = glm(case~education+age+parity, data=infert,
family=binomial)
pp = parGLM(case~education+age+parity, myr,
family=binomial, binit=rep(0, length(m1$coef)), tol=.001,
maxit=50)
checkTrue(max(abs(pp$coef-m1$coef)) < 1e-5)
se1 = summary(m1)$coef[,2]
sepp = sqrt(diag(pp$eff))
checkTrue(max(abs(se1-sepp)) < 1e-5)
TRUE
}
checkglms()
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