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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

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 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

Sorry if this post should be long but I tried to give you a piece of my data to reproduce my error message using hetcor: Fehler in result$rho : $ operator is invalid for atomic vectors Zus?tzlich: Warning messages: 1: In polychor(x, y, ML = ML, std.err = std.err) : 1 row with zero marginal removed 2: In polychor(x, y, ML = ML, std.err = std.err) : the table has fewer than 2 rows Error in

Hello R-User I have a table as tab-delimited textfile (291 rows, 83 columns). The first row are labels and the first line the variable names. I used the following code several times with different similar tables and it always worked. But now: setClass("of") setAs("character", "of", function(from) as.ordered(from)) Classe82<-cclasses <-

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 try tu use mob() with my data.frame ('data.frame': 288 obs. of 81 variables; factors, numerics and ordered factors) My response is a binary variable and I should use for modelling a logistic regression (family=binomial). I read in the "MOB" Vignette that I could use a formula like this if I would like to have only partitioning variables apart from the response.

Hi all, I have a 4 by 4 matrix, and I want to switch row 2 and row 3 first, then switch column 2 and column 3. Is there an easy way to do it? The following is a tedious way to get what I want. But I wonder if there is a way to simplify this. > a=matrix(rnorm(16),4,4) > a [,1] [,2] [,3] [,4] [1,] 0.33833811 -0.9422273 -0.06181611 -1.8346134 [2,] -0.68167996

R 2.7.2 PPC Mac OS X 10.4.11 library mice 1.13.1 I try to use mice for multivariate data imputation. My variables are numeric, factors, count data, ordered factors. First I created a vector for the methods to use with each variable ImpMethMice<-c(rep("logreg", 62), rep("polyreg",1), rep("norm",12), rep("polyreg",12)) next step was

Hello RUser! I try to use ace for an ancestral state reconstruction but got back an error message. ace(FacVar,Tree, type="discrete") Warning messages: 1: In nlm(function(p) dev(p), p = rep(ip, length.out = np), hessian = TRUE) : NA/Inf durch gr??te positive Zahl ersetzt (NA/Inf replaced by positive number) 2: In nlm(function(p) dev(p), p = rep(ip, length.out = np), hessian

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