similar to: Sperman Correlation with rcorr (Hmisc)

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

I try to use ?randomForest to find variables that are the most important to divide my dataset (continuous, categorical variables) in two given groups. But when I plot the outliers: plot(outlier(FemMalSex_NAavoid88.rf33, cls=FemMalSex_NAavoid88$Sex), type="h",col=c("red","green")[as.numeric(FemMalSex_NAavoid88$Sex)]) it seems to me that all my values appear as

Hello R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

I just started to write tiny functions and therefore I appologise in advance if I am asking stupid question. I wrote a tiny function to give me back from the original matrix, a matrix showing only the values smaller -0.8 and bigger 0.8. y<-c(0.1,0.2,0.3,-0.8,-0.4,0.9) x<-c(0.5,0.3,0.9,-0.9,-0.7,0.3) XY<-rbind(x,y) extract.values<-function (x) { if(x>=0.8|x<=-0.8)x

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

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 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 all, Sorry about posting a really novice question. I was able to run rcorr after converting the list to a matrix by your help. I'm though wondering if there is any way to find out an exact p value as the output only gave me 0 for P value as shown below. I've added options(digits=10), which doesn't seem to help at all. Any help would be appreciated. P D Prime T

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

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

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, 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

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

i'm trying to test the homogeneity of variance of 92 samples each one contains 3 observations. to use bartlett.test function i have created a (3,92) matrix (named xx): >bartlett.test(xx) this message appears: >Erreur dans bartlett.test.default(xx) : l'argument "g" est manquant, avec aucune valeur par d?faut when i checked the help i have understood that in g i should

I tried to use ctree but am not sure about the meaning of the plot. My.data.ct<-ctree(Resp~., data=My.data) plot(My.data.ct) My data.frame contains 88 explanatory variables (continous,ordered/unordered multistate,count data) and one response with two groups. In the plot are only two variables shown (2 internal nodes) and 3 final nodes. Does it mean that only these two variables show a