similar to: Systematic treatment of missing values

Hello, R experts, Sorry for asking this question again again since I really want a help! I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below:

Dear all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) >

Hello all, (Apologies in advance if my terminology is incorrect, I'm relatively new to R and statistics). I have data from a factorial design with two treatments (CRF-23), and I'm trying to compute treatment-contrast interactions through analysis of variance. I can't figure out how to do contrasts properly, despite reading the help for "C" and "contrasts"

Hi R people: I am new to R. I am writing a function to (1) produce a sparse stochastic Gaussian 2D field and (2) perform a systematic transect sampling on this field, this carried out many times in a simulation framework. My function does a good job at producing the random field (a matrix of zeros and some manifestations of the stochastic process, depending on a parameter of the function

Hi Folks, I'm a bit puzzled by the following (example): N<-factor(sample(c(1,2,3),1000,replace=TRUE)) unique(N) # [1] 3 2 1 # Levels: 1 2 3 So far so good. Now: contrasts(N)<-contr.treatment(3, base=1, contrasts=FALSE) contrasts(N) # 1 2 # 1 1 0 # 2 0 1 # 3 0 0 whereas: contr.treatment(3, base=1, contrasts=FALSE) # 1 2 3 # 1 1 0 0 # 2 0 1 0 # 3 0 0 1 contr.treatment(3, base=1,

Dear all, Here is a little coding problem. It falls in the category of "how can I do this efficiently?" rather than "how can I do this?" (I know how to do it inefficiently). So, if you want to take the challenge, keep reading, otherwise just skip to the next post - I won't be offended by that ;-) I am coding a particle filter and I want to use systematic resampling to

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

Dear Help-Rs,   I've been dealing with this problem for some time, using a work-around to deal with it. It's time for me to come clean with my ineptitude and seek a what has got to be a more streamlined solution from the Help-Rverse.   I regularly import delimited text data that contains numerics enclosed in quotes (e.g., "00765288071").  Thing is, for some of these data, I need

We want to sum many vectors and numbers together as a vector if there is at least one vector in the arguments. For example, 1 + c(2,3) = c(3,4). Since we are not sure arguments to sum, we are using sum function: sum(v1,v2,...,n1,n2,..). The problem is that sum returns the sum of all the values present in its arguments: sum(1,c(2,3))=6 sum(1,2,3)=6 We do not want to turn sum(v1,v2,...,n1,n2,..) to

Hi there, I have a vector and would like to create a data frame, which contains all unique combination of two elements, regardless of order. myVec <- c(1,2,3) what expand.grid does: 1,1 1,2 1,3 2,1 2,2 2,3 3,1 3,2 3,3 what I would like to have 1,1 1,2 1,3 2,2 2,3 3,3 Can anybody help?

Hi I am using R version 2.7.2. on a windows XP OS and have a question concerning an analysis of covariance with count data I am trying to do, I will give details of a scaled down version of the analysis (as I have more covariates and need to take account of over-dispersion etc etc) but as I am sure it is only a simple problem but I just can't see how to fix it. I have a data set with count

Dear Colleagues, I am looking for a package or previous implemented R to subgroup and exaustively divide a vector of squence into 2 groups. For example: 1, 2, 3, 4 I want to have a group of 1, (2,3,4) (1,2), (3,4) (1,3), (2,4) (1,4), (2,3) (1,2,3), 4 (2,3), (1,4) ... Can someone help me as how to implement this? I get some imaginary problem when the sequence becomes large. Thanks much in

Hi, I am quite new to R and in need of some advice. I am trying to conduct a meta regression over a some studies with about 7 mod variables which I have to dummy encode. I have found the following piece of code in the manual for the metafor library: ### manual dummy coding of the allocation factor alloc.random <- ifelse(dat$alloc == "random", 1, 0) alloc.alternate <-

Hi, Given a test matrix, test <- matrix(c(1,2,3,NA,2,3,NA,NA,2), 3,3) A) How to compute the counts of each column (excluding the NA) i.e., 3, 2, 1 B) How to compute the counts of each column (excluding the NA) that are greater than the column means ? i.e., 1, 1, 0 I could write a for loop, but hope to use better alternative. [[alternative HTML version deleted]]

I was wondering why the following doesn't work: > a=c(1,2) > names(a)=c("one","two") > a one two 1 2 > > names(a[2]) [1] "two" > > names(a[2])="too" > names(a) [1] "one" "two" > a one two 1 2 I must not be understanding some basic concept here. Why doesn't the 2nd name change to

I was wondering why the following doesn't work: > a=c(1,2) > names(a)=c("one","two") > a one two 1 2 > > names(a[2]) [1] "two" > > names(a[2])="too" > names(a) [1] "one" "two" > a one two 1 2 I must not be understanding some basic concept here. Why doesn't the 2nd name change to

Dear R experts. I might be missing something obvious. I have been trying to fix this problem for some weeks. Please help. #data ped <- c(rep(1, 4), rep(2, 3), rep(3, 3)) y <- rnorm(10, 8, 2) # variable set 1 M1a <- sample (c(1, 2,3), 10, replace= T) M1b <- sample (c(1, 2,3), 10, replace= T) M1aP1 <- sample (c(1, 2,3), 10, replace= T) M1bP2 <- sample (c(1, 2,3), 10, replace= T)

I would like to store and manipulate large sets of marker genotypes compactly using "raw" data arrays. This works fine for vectors or matrices, but I run into the error shown in the example below as soon as I try to use 3 dimensional arrays (eg. animal x marker x allele). > a <- array(as.raw(1:6),c(2,3)) > a [,1] [,2] [,3] [1,] 01 03 05 [2,] 02 04 06 >

Hello, is there a R package that provides a log rank trend test for survival data in >=3 treatment groups? Or are there any comparable trend tests for survival data in R? Thanks a lot Markus -- Dipl. Inf. Markus Kreuz Universitaet Leipzig Institut fuer medizinische Informatik, Statistik und Epidemiologie (IMISE) Haertelstr. 16-18 D-04107 Leipzig Tel. +49 341 97 16 276 Fax. +49 341 97 16

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----