similar to: Confidence interval for the coefficient of variation

Dear all, I have been looking for possibilities to read and write Excel 2007-files in and from R. The 'reading' part is ok through odbcConnectExcel2007 and sqlFetch(RODBC). For 'writing' I thought of using sqlSave in the same package, but it does not work (I think because this function starts from an existing file). Is there a workaround for this, or are there other plausible

Dear R-users, I'm encoutering some problems with the qcc()-function. When there are more than 25 replicates per "sample" the estimate for the standard deviation becomes NA, implying that nothing is plotted (error message: "Error in plot.window(...) : need finite 'ylim' values") On the forum I found a way to work around the error message, but this does not

Dear All, I am working on a large matrix of dimension 20000x700 say 'mat'. I have calculated pearson correlation for the rows of the matrix and their p-values using rcorr function in library Hmisc. Now I wish to filter out those pairs who's PCC value is above 0.8 cut off and p-value is less than 0.05. >library(Hmisc) >mat_cor=rcorr(t(mat),type="pearson")

Dear community, I'm doing a lm. In the independent variables I've got a categorical one. Here is its histogram: http://r.789695.n4.nabble.com/file/n3333638/altitude.png I did this regression: lmeo2.52f <- lm(dat82$IncAltuDom ~ dat82$hdom2+log(dat82$CV)+ dat82$CA+ dat82$FCC+ factor(dat82$IdAltitud)) I obtain: Coefficients: Estimate Std. Error t value

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

Dear all, I am a new member to the list - and to the analysis that I am attempting. I have the following case A group of us have been monitoring (over a period of a few years) a number of paired plots that were flooded and / or burnt. The plots are located in two topographical settings, some were burnt, some were flooded, some were burnt & flooded and some were not affected at all. At

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662