search for: proband

..., control = rpart.control(xval = 0)) for (i in 1:length(trees)) { trees[[i]] <- update(mod, weights = bootsamples[, i]) } } #bring in data [snip] #Recursive partitioning: library("rpart") # get a 2/3 random sample of ids ids <- sample(probands$id, size = 2*(length(probands$id))/3, replace = FALSE) # now I want a logical vector telling me which sample to put the # subjects in: #training_true <- probands$id %in% ids training <- subset(probands, probands$id %in% ids) testing <- subset(probands...

Dear R listers, I have a data file looks like the following: Testing marker: s_1 --------------------------------------------- Allele df(0) -LnLk(0) df(T) -LnLk(T) ChiSq p 3 7995 29320.30 7994 29311.85 16.90 4e-05 (2229/8000 probands) Testing marker: s_2 --------------------------------------------- Allele df(0) -LnLk(0) df(T) -LnLk(T) ChiSq p 3 7995 29339.25 7994 29338.31 1.88 0.1702 ( 121/8000 probands) Testing marker: s_3 -----------------------------...

I am involved in a study where, as in most of life, men demonstrate themselves to be recalcitrant. So while we have many probands and most of their mothers we only have about 50% of the trios being complete. I have been running tdt and trio.types. It appears as if it is ignoring the duos. Sometimes a duo can be informative. For instance Father ..missing Mother 1/2 Proband 1/1 This duo shows that for allele 2, this was clear...

...Thanks Dave Hi R user, I am using the gamm() function of the mgcv-package. Now I would like to decide on the random effects to include in the model. Within a GAMM framework, is it allowed to compare the following two models inv_1<-gamm(y~te(sat,inv),data=daten_final, random=list(proband=~1)) inv_2<-gamm(y~te(sat,inv),data=daten_final, random=list(proband=~sat)) with a likelihood ratio test for a traditional GLMM, like this: anova(inv_1$lme, inv_2$lme) The output is as follows: Model df AIC BIC logLik Test L.Ratio p-value inv_2$lme 1...

...function directly for loglikelihood and try to optimise using optim it gives warnings as well as error and if i use NLMINB then convergence is false. Can someone save my soul..... pl!!!!!!!!z below is my program script .............. #ovarian cancer data and parity among 769 probands and their mothers #cancer in proband y1<-c(1,1,1,1,1,1,1,rep(1,29),rep(1,36),rep(1,41),r! ep(1,85),rep(1,105),rep(1,84), 1,rep(0,22),rep(0,33),rep(0,38),rep(0,50),rep(0,103),rep(0,135)) #cancer in mother y2<-c(1,1,1,1,1,1,1,rep(0,29),rep(0,36),rep(0,41),rep(0,85),rep(0...

...ubject], ylim=c(400,1500)) dev.off() datalist <- data.frame(data) names(datalist) <- subject print(datalist) png("/SCR/STATISTIK/datalist.png", width=1024, height=768) plot(datalist) dev.off()}} #png("/SCR/all-show.png") #boxplot(datalist, main="Proband - ", col= "blue", ylab= "reactiontime in milliseconds", xlab= "name") #dev.off() #dev.set() #Datenmatrix <- cbind(datalist) #print(Datenmatrix) #dev.off() #plot(datalist) #dev.off() #einlesen <- datalist #print(einlesen) #Monate,storage...

...produced by hacking the survival package, running R CMD check, and then loading the package from the test directory that created: library("survival", lib.loc="/home/ross/src/survival.Rcheck") The clogit command seems to run fine. One of the variables in the data.frame is name proband, which I notice gets highlighted by ESS.

I tried to reproduce a result from a former colleague which he got with S-plus bootstrap method. I don't have S-plus at hand. In R, there are 2 packages related to bootstrap method, bootstrap and boot. The former has a function called 'bootstrap' but this does not seem to conform either to the function used in S-plus nor to that described in MASS, 3d ed., p.144. The latter seems to be

Bottom Line Up Front: How does one reshape genetic data from long to wide? I currently have a lot of data. About 180 individuals (some probands/patients, some parents, rare siblings) and SNP data from 6000 loci on each. The standard formats seem to be something along the lines of Famid, pid, fatid, motid, affected, sex, locus1Allele1, locus1Allele2, locus2Allele1, locus2Allele2, etc In other words one human, one row. If there were multip...