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Dear R experts, I am trying to simulate a list containing data matrices. Unfortunately, I don't manage to get it to work. A small example: n=5 nbootstrap=2 subsets<-list() for (i in 1:n){ subsets[[i]] <- rnorm(5, mean=80, sd=1) for (j in 1:nbootstrap){ test<-list() test[[j]]<-subsets[[i]] } } How can I get test to be 2 simulation rounds with each 5 matrices. Kind regards, Tobias [[alternative HT...

Dear all, Let's say I have the following: # Loading the boot package # install.packages(boot) library(boot) # Generating data set.seed(123) x <- rnorm(100) # Bootstrap for the sample mean bmean <- boot(x, function(x,d) mean(x[d]), R=1000) bmean # #ORDINARY NONPARAMETRIC BOOTSTRAP # # #Call: #boot(data = x, statistic = function(x, d) mean(x[d]), R = 1000) # # #Bootstrap Statistics : #

...s.factor(MajorArea)4 -0.2413011 0.08161707 -2.956503 3.111496e-03 # # $fit$refvar # [1] 0.01855022 # # $fit$goodness # loglike AIC BIC KIC AICc AICb1 AICb2 # 12.677478 -15.354956 -6.548956 -10.354956 NA NA NA # KICc KICb1 KICb2 nBootstrap # NA NA NA 0.000000 #Bug 16975 report fitting of FH model proposed by Maciej Beresewicz FH<-lme(yi ~ as.factor(MajorArea),random=~1|as.factor(SmallArea), data=milk,weights=varFixed(~var), control=lmeControl(sigma = 1,tolerance = 1e-4)) FH # Linear mixed-effects model fit b...

hello, i need to calculate ci's for each of 4 groups within a dataset, to be able to infere about differences in the variable "similarity". the problem is that data within groups is dependent, as assigned by the blocking-factor "site". my guess was to use a block bootstrap but samples within in these blocks / sites are not of same length. i was not able to find a method to