similar to: Tiny help for tiny function

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())

Dear everybody! take a<-c(5,3,NA,6). if(a[1]!=NA){b<-7} if(a[3]!=5){b<-7} if(a[3]!=NA){b<-7} if(a[3]==NA){b<-7} will alltogeather return Fehler in if (a[1] != NA) { : Fehlender Wert, wo TRUE/FALSE n?tig ist (or simularly). Somehow this is logical. But how else should I get out, whether a certain vector-component has an existing value? Thank you in advance! Yours, Mag. Ferri

Hi everybody, the following alway gives me the error "Fehler in if (File$X.Frame.Number[a] + 1 == File$X.Frame.Number[a + 1]) (File$FishNr[a] <- File$FishNr[a - : Fehlender Wert, wo TRUE/FALSE n?tig ist". Maybe its stupid, but i'm not getting why... Maybe someone can help me. Thanks a lot! for (i in unique(BigFile$TrackAll)) { File <-

Hallo together, I am trying to use the nmkb() optimizer and I have problems using the function, as it causes the following error message Fehler (error)* in while (nf < maxfeval & restarts < restarts.max & dist > ftol & : Fehlender Wert (missing value)* , wo (where)* TRUE/FALSE n?tig ist (is required)* *translation Do I need to adjust the control ?

help Hello, I?m a statistic student in Austria and I have to do a survival analysis in R by using psplines as regressor. My problem is that I sometimes (I think it depends on the choose of the parameters) get a error message, but I do not know what it means. After that I tried the procedure with an example dataset R is providing. Although using the cancer dataset I also get this message. Input:

Hello list members I have a problem with modifying a data.frame. As an example given is a data.frame called ex : ex<-data.frame(id=c(1,2,3,4,5,6),obs=c(14,9,20,36,55,47),eff=c("A","A","B","C","C","C")) After that I would like to modify the object ex with the following short script: for (i in ex) { if(ex[i,3]=="A"||

Hi, I'am using the playwith library to write my own small GUI application. But I get the following error under Windows and Linux (Ubuntu): Error in if ((modeOK %in% c("Identify", "Brush")) && (actions$ident == : Fehlender Wert, wo TRUE/FALSE n?tig ist Under linux my R program simply continues, but under Windows it stops. Since it is not in my code, has anyone

I want to create a open/high/low/last plot of intraday data. I try to use the function plotOHLC from the tsteries package. I create my own multiple time series and then try to plot it. raw Data Format (file eurusd2.csv): "Date (GMT)" "Open" "High" "Low" "Last" 17-03-2008 00:00:00 1,5764 1,5766 1,5747 1,5750 17-03-2008 00:05:00 1,5749 1,5750 1,5741

dear R users, I have troubles performing Moran.I test as suggested on http://www.ats.ucla.edu/stat/r/faq/morans_i.htm my spatial data are longitude and lattitide of communities. The calculation of the inverse distance matrix according to the homepage (using my data) datAL <- read.csv2("C:\\Konvergenz AL.csv", header=T) ALdist <- as.matrix(dist(cbind(datAL$L?nge,

-------- Original-Nachricht -------- Betreff: Re: Package cwhmisc and problems Datum: Fri, 24 Aug 2012 07:34:14 +0100 Von: Prof Brian Ripley <ripley@stats.ox.ac.uk> An: Christian Hoffmann <c-w.hoffmann@sunrise.ch> Kopie (CC): CRAN@r-project.org This is the address for CRAN submissions. Please ask for help on R-help or R-devel. On 23/08/2012 20:16, Christian Hoffmann wrote:

Hi, I am currently working on fitting a mixture density to financial data. I have the following data: http://s000.tinyupload.com/?file_id=00083355432555420222 I want to fit a mixture density of two normal distributions. I have the formula: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) my R code is: normalmix<-normalmixEM(dat,k=2,fast=TRUE) pi<-normalmix$lambda[1] mu1<-normalmix$mu[1]

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

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

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

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

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.

Problem: Based on a n x 2 data matrix i want a kernel estimate of the bivariate density. However, i also wish to specify wich points the density should be calculated at. I can offcourse just write the full kernel density estimate as a R-code, but surely there must already exist some package for this operation? The package density(), seems to create a new matrix (depending on n), where the

Hi all, I appologise in advance if this is a little OT, but I am building a box that will serve as firewall and router for a small ''internet cafe / netcafe'' and am using CentOS... So here it is: What are the best tools to be used for keeping the potential script kiddies from ''harming the Internet'' :) ? I specifically want to be able to detect and prevent