similar to: digit precision in p value of rcorr

Hello, I'd like to test a significance of two variables in their correlation using rcorr, which gave me an error of format incompatibility. Below are the lines that I typed in the R window and let me know if anyone knows how to resolve this. Var=read.csv("03apr10ab_corr_matrix_in_overlaps.csv",header=F) colnames(Var)=c("D Prime","T statistics") D Prime

Hi, I'm trying to calculate 95% confidence interval of C statistic of logistic regression model using rcorr.cens in rms package. I wrote a brief function for this purpose as the followings; CstatisticCI <- function(x) # x is object of rcorr.cens. { se <- x["S.D."]/sqrt(x["n"]) Low95 <- x["C Index"] - 1.96*se Upper95 <- x["C

Dear all ?Hmisc::rcorr states that it takes as main argument "a numeric matrix". But is it normal that it fails in such an ugly way on a data frame? (See below.) If the function didn't attempt any conversion to a matrix, I would have expected it to state that in the error message that it didn't accept 'data.frame' objects in its input. Also, I vaguely remember having used

Dear R-help, This is an example in the {Hmisc} manual under rcorr.cens function: > set.seed(1) > x <- round(rnorm(200)) > y <- rnorm(200) > round(rcorr.cens(x, y, outx=F),4) C Index Dxy S.D. n missing uncensored Relevant Pairs Concordant Uncertain 0.4831 -0.0338 0.0462 200.0000

Dear list, I get the following message when I use rcorr in library "Hmisc" ------------------------------------------------------ > rcorr(lskPox0t30, type=c("spearman")) Error in "[<-.data.frame"(*tmp*, is.na(x), value = 1e+30) : matrix subscripts not allowed in replacement ------------------------------------------------------ I do not understand

Hi, I try to export the outputs of rcorr into excel. but I got error message,"cannot coerce class "rcorr" into a data.frame". Actually i just need export part of results of this analysis,e.g. p-values or stat-values. Does anyone have sort of exprience before or you can help on how to export subset of results of analysis? Many Thanks! Xin

Dear r-helpers! I want to classify my vegetation data with hierachical cluster analysis. My Dataset consist of Abundance-Values (Braun-Blanquet ordinal scale; ranked) for each plant species and relev?. I found a lot of r-packages dealing with cluster analysis, but none of them is able to calculate a distance measure for ranked data. Podani recommends the use of Goodman and Kruskals' Gamma for

Hello, Also, the other file, NPA.csv, is not in tabular form. Can you please reformat it? Rui Barradas Citando ruipbarradas at sapo.pt: > Hello, > > Please keep this on the list, always cc r-help. > One of the files in your attachment is empty: > > y <- read.csv(file.choose("GT.csv")) > Error in read.table(file = file, header = header, sep = sep, quote =

Hello, Please keep this on the list, always cc r-help. One of the files in your attachment is empty: y <- read.csv(file.choose("GT.csv")) Error in read.table(file = file, header = header, sep = sep, quote = quote,? : ? no lines available in input Rui Barradas ? Citando Chaitanya Ganne <Chaitanya.Ganne at jefferson.edu>: > Thank you so much for your input. > > I am

Hi! All, To find co-expressed genes from a expression matrix of dimension (9275 X 569), I used rcorr function from library(Hmisc) to calculate pearson correlation coefficient (PCC) and their corresponding p-values. From the correlation matrix (9275 X 9275) and pvalue matrix (9275 X 9275) obtained using rcorr function, I wanted to select those pairs whose PCC's are above 0.8 cut-off and then

Dear List, I would like to calculate the Goodman-Kruskal gamma for the predicted classes obtained from an ordinal regression model using lrm in the Design package. I couldn't find a way to get gamma for predicted values in Design so have found previous positings suggesting to use : Rcorr.cens(x, S outx = TRUE) in the Hmisc package My question is, will this work for predicted vs

Dear R-helpers, I recently switched from SAS to R, in order to model the occurrence of rare events through logistic regression. Is there a package available in R to calculate the Goodman-Kruskal Gamma? After searching a bit I found a function "rcorr.cens" which should do the job, but it is not clear to me how to define the input vectors? Is "x" a vector with the fitted

Hi, I've just run an rcorr on some data in Spearman's mode and it's just produced the following values; [,1] [,2] [1,] 1.00 -0.55 [2,] -0.55 1.00 n= 46 P [,1] [,2] [1,] 0 [2,] 0 I presume this means the p-value is lower than 0.00005, but is there any way of increasing the number of significant figures used? How should I interpret this value? Cheers Jim

Hello R-User! I have a data.frame with 82 variables (columns) and 290 rows. The variables are set to classes factor, ordered factor and numeric. I used the following code Matrix.My.data<-as.matrix(Df.My.Data[2:82]) Matrix.My.data.rcorr<-rcorr(Matrix.My.data, type="spearman") and got the following message: Fehler in storage.mode(x) <- if (.R.) "double" else

I am using the rcorr.cens function from the Hmisc package and the time-dependent ROC curve obtained using tdrocc in the survcomp package. I understand that the C statistic from rcorr.cens has to be subtracted from 1 if high values of the risk variable lower survival. Given that I wonder what the connection is between that C statistic and the AUC from the tdrocc object. If they are substantially

Hello, I'm trying to install a package that requires a Fortran compiler (Hmisc) using R CMD INSTALL. I downloaded the package source onto my Desktop, unzipped it, and then typed: R CMD INSTALL /Users/brianbeckage/Desktop/Hmisc * Installing *source* package 'Hmisc' ... ** libs g77 -fno-common -g -O2 -c cidxcn.f -o cidxcn.o g77 -fno-common -g -O2 -c cidxcp.f -o cidxcp.o g77

I am running Ubuntu 7.10 and R-2.6.0, and I am having trouble updating packages. There appears to be a problem involving gfortran. For example, here is the output of an attempt to update the Hmisc package. * Installing *source* package 'Hmisc' ... ** libs gfortran -fpic -g -O2 -c cidxcn.f -o cidxcn.o gfortran -fpic -g -O2 -c cidxcp.f -o cidxcp.o gfortran -fpic -g -O2 -c

Frank Harrell suggested I re-post with information about the version of R Heres's the information: > > version > _ > platform powerpc-apple-darwin6.8 > arch powerpc > os darwin6.8 > system powerpc, darwin6.8 > status > major 2 > minor 0.1 > year 2004 > month 11 > day 15 > language R Here's what happens

Dear all, I am trying to compute a matrix of Pearson's `r' or Spearman's `rho' rank correlation coefficients using rcorr (Hmisc) the following way: > mx<-rcorr(x, type="spearman")[1] but then ... > is.matrix(mx) [1] FALSE Even if I use as.matrix the result is not better. What can I do? Thank you all Luca

Hi all, one of my packages uses the rcorr.cens function from the Hmisc package. Until now I have simply put the Hmisc package into Depends:, but prodded on by new CRAN requirements, I tried to moving it into Imports:. However, this fails because rcorr.cens calls the function is.Surv from survival, which does not seem to be on the search path when Hmisc is "imported from" rather then