similar to: How to show which variables include in plot of classification tree

Dear R-helpers, First I apologize if my question is quite simple I have a large datasets which more 100 variables. For a research I need to change all name of variables with add one or more letters on each variables. For example, > data(Pima.tr) > Pima.tr[1:5,] npreg glu bp skin bmi ped age type 1 5 86 68 28 30.2 0.364 24 No 2 7 195 70 33 25.1 0.163 55 Yes 3 5

Hi there, >library(faraway) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6 0.627 50 1 2 1 85 66 29 0 26.6 0.351 31 0 >data(pima) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6

Dear all, I was wondering if someone can help me. I am learning SVM for classification in my research with kernlab package. I want to know about classification performance using Area Under Curve (AUC). I know ROCR package can do this job but I found all example in ROCR package have include prediction, for example, ROCR.hiv {ROCR}. My problem is how to produce prediction in SVM and to find

Hi I'm running QDA on some data and calculating the discriminant function. qda.res <- qda(type ~ npreg + glu + bp + skin + bmi + ped + age) ind_yes <- c(1:N)[type == "Yes"] > ind_no <- c(1:N)[type == "No"] > cov_yes <- cov(table[ind_yes, 1:7] ) > cov_no <- cov(table[ind_no, 1:7] ) > covar<-list(cov_no, cov_yes) qdf<- function(x,

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

Hi All, I'd like to ask for a few clarifications. I am doing some calculations over some biggish datasets. One has ~ 23000 rows, and 6 columns, the other has ~620000 rows and 6 columns. I am using these datasets to perform a simulation of of haplotype coalescence over a pedigree (the datestes themselves are pedigree information). I created a new dataset (same number of rows as the pedigree

Get the following error on Sun Solaris 10, Sparc (Ultra-5) ./tools/wineinstall Wine Installer v1.0 ./tools/wineinstall: !: not found ./tools/wineinstall: : cannot execute ./tools/wineinstallgrep: RE error 41: No remembered search string. ./tools/wineinstall: test: ] missing : Name: not found ./tools/wineinstall: wine: not found Running configure... ./tools/wineinstall: !: not found We need to

Hello, I am building a R-package for Genetics analysis. The accepted data is in pedigree (.ped) file format. To load the data (say CAMP.ped) from "data" directory, I have a function "CAMP.R", which does the job. The package builds successfully in Linux (.tar.gz) and the data loads successfully by "data(CAMP)". However, when I build the package in WINDOWS, the data

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

Hello R experts, what follows is my reproducible example: mydata<-structure(list(ped.avg = c(335.9, 110.8, 645.7, 638.9, 1468.1, 126.4, 4811.1, 88.5, 868.5, 656.6, 723.6, 654, 2.8, 15, 14.2, 17.5, 15.4, 112.1, 424.7, 18.3, 19.9, 28.6, 25.6, 23.5, 15.4, 27, 62.1, 15.6, 74.6), ped.erst = c(96, 53.2, 615.2, 616.5, 512.9, 56.2, 1851.8, 57.1, 579.5, 613.2, 601.1, 613.6, 1.3, 6.3, 6.5, 6.1,

Dear R-experts Execuse me for an easy question, but I need help, sorry for that. >From days I have been working with a large dataset, where operations are needed within a component of dataset. Here is my question: I have big dataset where x1:.....x1000 or so. What I need to do is to work on 4 consequite variables to calculate a statistics and output. So far so good. There are more vector

I have a data frame containing the Id, Mother, Father and Sex from about 10,000 animals in our colony. I am interested in graphing simple family trees for a given subject or small number of subjects. The basic idea is: start with data frame from entire colony and list of index animals. I need to identify all immediate relatives of these index animals and plot the pedigree for them. We're

I am doing a reanalysis of results that have previously been published. My hope was to demonstrate the value of adoption of more modern regression methods in preference to the traditional approach of univariate stratification. I have encountered a puzzle regarding differences between I thought would be two equivalent analyses. Using a single factor, I compare poisson models with and without

Hola a todos, A partir de los siguientes datos: d <- list(`1` = structure(list(ped = c(1L, 1L, 1L, 1L, 1L, 1L, 1L), id = 1:7, father = c(2L, 0L, 0L, 2L, 2L, 2L, 2L), mother = c(3L, 0L, 0L, 3L, 3L, 3L, 3L), sex = c(2L, 1L, 2L, 2L, 2L, 1L, 2L), affected = c(1L, 2L, 1L, 1L, 2L, 2L, 2L)), row.names = c("1", "2", "3", "4", "5",

Dear Sir/Madam, Since a few day now I try to use the command "polygenic" from the GenAbel package. However, I keep bumping up against an error message: "Error in polygenic(Testo, kin = kinship, data = data1) : dimension of outcome and kinship.matrix do not match". My data exists of 1240 individuals with 74 markers. It mainly consists of small families (2 or more brothers,

Dear R experts. I might be missing something obvious. I have been trying to fix this problem for some weeks. Please help. #data ped <- c(rep(1, 4), rep(2, 3), rep(3, 3)) y <- rnorm(10, 8, 2) # variable set 1 M1a <- sample (c(1, 2,3), 10, replace= T) M1b <- sample (c(1, 2,3), 10, replace= T) M1aP1 <- sample (c(1, 2,3), 10, replace= T) M1bP2 <- sample (c(1, 2,3), 10, replace= T)

Hi I was wondering if there is a permutation test available in R for linear models with continuous dependent covariates. I want to do a test like the one shown here. bmi<-rnorm(100,25) x<-c(rep(0,75),rep(1,25)) y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x H0<-lm(y~1+x+bmi) H1<-lm(y~1+x+bmi+x*bmi) anova(H0,H1) summary(lm(y~1+x+bmi)) But I want to use permutation testing to

Im doing a project using Rcommander. I have a dataset with 5 variables, BMI being one of them. I would like to find the variance, standard deviation, range, median, etc of BMI. However, everytime I type a command into the script window, (such as var(BMI$BMI,na.rm=TRUE) )an error message pops up saying Object "BMI" not found. I can graph it, work it, do t-test, etc,. It's listed as

I am attempting to use the lars package with a sparse input feature matrix, but the following fails: library(Matrix) library(lars) data(diabetes) attach(diabetes) x = as(as.matrix(as.data.frame(x)), 'dgCMatrix') lars(x, y, intercept = FALSE) Error in scale.default(x, FALSE, normx) : > > length of 'scale' must equal the number of columns of 'x' > > More

Hello, Say I have 2 columns, bmi and gender, the first being all the values and the second being male or female. How would I subset this into males only and females only? I have searched these fora and read endlessly about select[] and split() functions but to no avail. Also the table is not ordered. bmi gender -> bmi gender + bmi gender 1 24.78 male