similar to: row.names need reordering

Dear R-help, I am trying to impute missing data for the first time using R. The norm package seems to work for me, but the missing values that it returns seem odd at times -- for example it returns negative values for a variable that should only be positive. Does this matter in data analysis, and/or is there a way to limit the imputed values to be within the minimum and maximum of the actual

Hello R-help, Thanks for everyone's very helpful suggestions so far. I am now trying to use aregImpute for my missing data imputation. Here are the code and error messages. Any suggestions would be very much appreciated. Sincerely, Anders Corr ######################################## #Question for R-Help on aregImpute ######################################## #DOWNLOAD DATA (61Kb)

My attempt to test a model using ordglm code is running into problems, and I thought if you have a moment you might illucidate the situation. Here is the data: http://www.people.fas.harvard.edu/~corr/6.4.05.RData Here is the code: # I coerce tcn8 matrix data to a vector, because ordglm will not accept matrix data. y<-as.vector(tcn8[,62]) x<-as.vector(tcn8[,60])

Oops, I just erased all my data using this gizmo that I thought would replace -9 with NA. A) Can I get my tcn5 back? B) How do I do it right next time, I learned my lesson, I'll never do it again, I promise! Anders Corr > for(i in 1:dim(tcn5)[2]){ ##for the number of columns + for(n in 1:dim(tcn5)[1]){ ##for the number of rows + tcn5[is.na(tcn5[n,i]) | tcn5[n,i]

Dear list members I need a function that calculates the bivariate normal distribution for each observation. It is part of a likelihood function and I have 1000's of cases. As I understand it I cannot use packages like "mvtnorm" because it requres a covariance matrix of the same dimension as the number of observations. Basically what I need is a function that takes as arguments a

hi everyone, Suppose I have three variables a, b, and c each with 10 values. Now I construct a corr matrix for them. Now I want to give the names of columns of corr matrix as a, b, c, i.e. the first column of corr matrix will have name as ‘a’ second column with ‘b’ and so on. Can anyone give me any code by which I can automatically assign the names of columns of corr matrix which are

Hi. Does anyone know whether the following error is a result of a bug or a feature? I can eliminate the error by making ML=F, but I would like to see the values of the cut-points and their variance. tmp.vec<-c(0, 0, 0 , 0 ,0 , 1, 0, 2, 0 , 0, 5 ,5 ,3 ,1, 0 , 1, 5, 10, 27, 20, 9, 0, 1, 1, 12, 29, 57, 34, 0, 0, 1, 2, 11, 31, 32) tmp.mat<-matrix(tmp.vec, nrow=7)

Hi gang, I'm perplexed- I have some code that uses for() loops that works fine in R version 2.8 on my mac, worked fine in version 2.8 on my old windows machine, but doesn't work in version 2.10 on windows. The loop implements a function over a data frame (code is included below). In Mac (running version 2.8), the results of the loop are what I expect: > p_unadj [1] 0.034939481

Hi, I'm running gam (mgcv version 1.3-29) and glm (logit) (stats R 2.61) on the same models/data, and I got error messages for the gam() model and warnings for the glm() model. R-help suggested that the glm() warning messages are due to the model perfectly predicting binary output. Perhaps the model overfits the data? I inspected my data and it was not immediately obvious to me (though I

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

Hello all and thanks in advance for any advice. I am very new to R and have searched my question but have not come up with anything quite like what I would like to do. My problem is: I have a data set for individuals (rows) and values for behaviours (columns). I would like to know the proportion of shared behaviours for all possible pairs of individuals. The sum of shared behaviours divided by

Hi, I would like to obtain correlation parameters (e.g., coefficients, p-value) for multiple samples in regard to a reference. I have my data in a table with the reference as the second row (first row are headers) and then each sample in a row. What I do so far is load up the data, get the reference sample and use "apply" and "lm" to do the regression: x <-

Dear R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not

Hi, I am a newbie of R, and playing with the "ifelse" statement. I have the following codes: ## first, for(i in 1:3) { for(j in 2:4) { cor.temp <- cor(iris.allnum[,i], iris.allnum[,j]) if(i==1 & j==2) corr.iris <- cor.temp else corr.iris <- c(corr.iris, cor.temp) } } this code is working fine. I also tried to perform the same thing in another way with "ifelse":

Hi List I have a dataframe (~1,200,000 rows deep) and I'd like to conditionally reorder groups of rows in this dataframe. I would like to reorder any rows where the Chr.Strand column contains a '-' but reorder within subsets delineated by the Probe.Set.Name column. # toy example #### library(plyr) negStrandGene <- data.frame(Probe.Set.Name =

Hi all, I have a function rho.f which gives a list of estimators. I have the following problems. rho.f(0.3) gives me the right answer. However, if I use rho.f(corr[4]) give me a different answer, even though corr[4]==0.3. This prevents me from using a for loop. Can someone give me some help? Thank you very much in advance. Hannah >

OK, I solved it all ( so far :) ), mixing in some load-instructions and called on the result of that, which worked. Here is the skeleton-code: %kernel = type { int ()* } int puts_kernel(){...} ; main() %theKernel = malloc %kernel %puts_kernelPTR = getelementptr %kernel* %theKernel, long 1, ubyte 0 store int ()* %puts_kernel, int ()** %puts_kernelPTR %tmp.11 = load int ()** %puts_kernelPTR

I feel like a complete dolt, as I know this question has been asked by others on a fairly regular basis, but I'm going in circles trying to get the following to work: id.prob<-function (tt) { library(mvtnorm) #============================ Makeham<-function(tt) { a2=0.030386513 a3=0.006688287 b3=0.039047537 t<-tt-20 h.t<-a2+a3*exp(b3*t) S.t<-exp(-a2*t+a3/b3*(1-exp(b3*t)))

Hello R users, I am trying to calculate the stochastic lambda for a published matrix population model using the popbio package. Unfortunately, I have been unable to match the published results. Can anyone tell me whether this is due to slightly different methods being used, or have I gone wrong somewhere in my code? Could the answer be as simple as comparing deterministic lambdas to

Hi all, I'd like to use the Hmisc::summarize function, but it uses a function (FUN) of a single vector argument to create the statistical summaries. Consider an easy case: I'd like to compute the correlation between two variables in my dataframe, grouped according to other variables in the same dataframe. For exemple, consider the following dataframe D: V1 V2 V3 A 1 -1 A 1