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...t with coxph having robust and cluster option fit3 <- coxph(Surv(start,stop,event)~transplant + cluster(id), data=heart, weights = iptw) fit3 # fit with coxph having cluster option fit4 <- coxph(Surv(start,stop,event)~transplant, data=heart, weights = iptw) fit4 # fit with coxph # coxreg fit5 <- coxreg(Surv(start,stop,event)~transplant + cluster(id), data=heart, weights = iptw) fit5 # fit with coxreg from eha having cluster option fit6 <- coxreg(Surv(start,stop,event)~transplant, data=heart, weights = iptw) fit6 # fit with coxreg from eha ############################ > exp...

...n=6) within subject (n=4) within smallgroups (=gru) (n = 28), i.e. n = 4 * 28 = 112 persons and 112 * 6 = 672 data points library(nlme) fitlme7 <- lme(nachw ~ I(zeitn-3.5) + I((zeitn-3.5)^2) + I((zeitn-3.5)^3) + I((zeitn-3.5)^4)*gru, random = list(subgr = ~ 1, subject = ~ zeitn), data = hlm3) fit5 <- lme(nachw ~ ordered(I(zeitn-3.5))*gru, random = list(subgr = ~ 1, subject = ~ zeitn), data = hlm3) anova( update(fit5, method="ML"), update(fitlme7, method="ML") ) > anova( update(fit5, method="ML"), update(fitlme7, method="ML") )...

...a.frame(model) modeldf[2:13<-lapply(modeldf[2:13],factor) colms<-(modeldf)[4:13] ## 10 markers only in this file se<-c(1:1000) for(f in colms) { print("Marker") { for( i in 1:1000) { print("perm no.") print(se[i]) { peg.no.prm<-sample(peg.no, length(peg.no)) try(fit5<-lmer(data=modeldf, peg.no.prm~1 + (1|family/f))) print(summary(fit5)) capture.output(fit5, file="testperm5.txt", append=T) }}} } The data files are at: >> >> >> <http://www.4shared.com/file/131980362/460bdafe/Testvcomp10.ht >> ml> (excel) >> http:...

...+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc) fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit5<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) *output of tau=0.15*fit1 Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 in...

I'm trying to figure out how to carry out a Poisson regression fit to longitudinal data with a gamma distribution with unknown shape and scale parameters. I've tried the 'lmer4' package's glmer() function, which fits the Poisson regression using: library('lme4') fit5<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) + (1|id), data=pdata, nAGQ=1, family=poisson) #note: can't use nAGQ>1, not yet implemented summary(fit5) Here 'seizures' is a count and 'id' is the subject number. This fit works, but uses the Poisso...

...apply(modeldf[2:13],factor) colms<-(modeldf)[4:13] ## ten marker columns se<-c(1:10000) peg.no<-(modeldf)[,14] library(lme4) for(f in colms) { print("Marker") { for( i in 1:10000) { print("perm no.") print(se[i]) { peg.no.prm<-sample(peg.no, length(peg.no)) try(fit5<-lmer(data=modeldf, peg.no.prm~1 + (1|family/f))) print(summary(fit5)) capture.output(fit5, file="testperm5.txt", append=T) }}} } The data files are at: <http://www.4shared.com/file/131980362/460bdafe/Testvcomp10.ht ml> (excel) http://www.4shared.com/file/131980512/dc7308b/Te...

SSweibull() :  problems with step factor and singular gradient Hello I am working with growth data of ~4000 tree seedlings and trying to fit non-linear Weibull growth curves through the data of each plant. Since they differ a lot in their shape, initial parameters cannot be set for all plants. That’s why I use the self-starting function SSweibull(). However, I often got two error messages:

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

...writing separate lines of code for each? -Ben Osborne > fit1<-lm(dBA.spp16$sp2.dBA.ha~dBA.spp16$sp1.dBA.ha) > fit2<-lm(dBA.spp16$sp3.dBA.ha~dBA.spp16$sp1.dBA.ha) > fit3<-lm(dBA.spp16$sp3.dBA.ha~dBA.spp16$sp2.dBA.ha) > fit4<-lm(dBA.spp16$sp5.dBA.ha~dBA.spp16$sp4.dBA.ha) > fit5<-lm(dBA.spp16$sp6.dBA.ha~dBA.spp16$sp4.dBA.ha) > fit6<-lm(dBA.spp16$sp5.dBA.ha~dBA.spp16$sp6.dBA.ha) > fit7<-lm(dBA.spp16$sp1.dBA.ha~dBA.spp16$sp4.dBA.ha) > fit8<-lm(dBA.spp16$sp1.dBA.ha~dBA.spp16$sp5.dBA.ha) > > dBA.spp16.fits<-matrix(NA, nrow=8, ncol=5) > colnames...

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1