similar to: speed up process

Dear Jim, Here is again exactly what I did and with the output of Rprof (with this reduced dataset and with a simpler function, it is here much faster than in real life). Thanks you again for your help! ## CODE ## mydata1<- structure(list(species = structure(1:8, .Label = c("alsen","gogor", "loalb", "mafas", "pacyn", "patro",

R helpers I am getting to grips with R but came across a small problem today that I could not fix by myself. I have 3 text files, each with a single column of data. I read them in using: myData1<-scan("C:/Program Files/R/myData1.txt") myData2<-scan("C:/Program Files/R/myData2.txt") myData3<-scan("C:/Program Files/R/myData3.txt") I wanted to produce a

I can run one-sample t-test on an array, for example a matrix myData1, with the following apply(myData1, 2, t.test) Is there a similar fashion using apply() or something else to run 2-sample t-test with datasets from two groups, myData1 and myData2, without looping? TIA, Gang

Hello I have dataframes. mydata1 <-data.frame(value=c(15,20,25,30,45,50),dates=c("2005-05-25 07:00:00 ","2005-05-25 19:00:00","2005-06-25 07:00:00","2005-06-25 19:00:00 ","2005-07-25 07:00:00","2005-8-25 19:00:00")) or mydata2 <-data.frame(value=c(15,20,25,30,45,50),dates=c("2005-05-25 00:00:00 ","2005-05-25

Hi I am really new using R, so this is really a beginner stuff! I created a very small data set on excel and then converted it to .csv file. I am able to open the data on R using the command "read.table ("mydata1.csv", sep=",", header=T)" and it just works fine. But when I want to work on the data (e.g. calculate the mean of variable "X") R says

Hello, I am attempting to train a random forest model using the randomForest package on 500,000 rows and 8 columns (7 predictors, 1 response). The data set is the first block of data from the UCI Machine Learning Repo dataset "Record Linkage Comparison Patterns" with the slight modification that I dropped two columns with lots of NA's and I used knn imputation to fill in other gaps.

Hi, I am trying to plot the original data with the line of the model using the predict function. I want to add SE to the graph, but not sure how to get them out as the predict function for gls does not appear to allow for SE=TRUE argument. Here is my code so far: f1<-formula(MaxNASC40_50~hu3+flcmax+TidalFlag) vf1Exp<-varExp(form=~hu3) B1D<-gls(f1,correlation=corGaus(form=Lat~Lon,

hi, I met some problems in R, plz help me. 1. How to do a intersect operation among several groups in one list, without a loop statement? (I think It may be a list) create data: myData <- data.frame(product = c(1,2,3,1,2,3,1,2,2), year=c(2009,2009,2009,2010,2010,2010,2011,2011,2011),value=c(1104,608,606,1504,508,1312,900,1100,800)) mySplit<- split(myData,myData$year)

Greetings to you all, I am performing a semi parametric bootstrap in R on a Gamma Distributed data and a Binomial distributed data. The main challenge am facing is the fact that the residual variance depends on the mean (if I am correct). I strongly feel that the script below may be wrong due to mean-variance relationship #####R code####### fit1s

Hi, It works for me to import spss datasets via library(foreign) with read.spss or via library Hmisc by (spss.get). But no matter which way I do import the data, user-defined missings from Spss are always lost. (it makes no difference if there are a single value, a range, or any combination of them. They are always ignored). Is there any way in R to find out if any value was user-defined missing

Dear useRs, How could I obtain the confidence intervals for the means of my treatments, when my data was fitted to a GLM? I need the CI's for the Poisson and Negative Binomial distributions. Here's what I have: mydata1 <- data.frame('treatments'=gl(4,20), 'value'=rpois(80, 1)) model1 <- glm(value ~ treatments, data=mydata1, family=poisson) means1 <-

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

Hi All, The for-loop below stopped when error("Cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance") occurred. I assigned a row of NA values to the data frame "m1" manually and reset "j" in the for-loop every time error returned. I’m wondering if there is a function that can detect error or failure, so the

I'm using ks.test (mydata, dnorm) on my data. I know some of my different variable samples (mydata1, mydata2, etc) must be normally distributed but the p value is always < 2.0^-16 (the 2.0 can change but not the exponent). I want to test mydata against a normal distribution. What could I be doing wrong? I tried instead using rnorm to create a normal distribution: y = rnorm

Hi all, I am trying to understand Writing R Extension... Section 1.1.5, data: I include two datasets in a package, one using 'save', the other using 'write.table': --- 8< ---- myData1 <- data.frame(x=1:10) write.table(myData1,file="myData1.txt") myData2 <- data.frame(x=2:10) save(myData2,file="myData2.Rdata") --- 8< ---- Then R CMD check aks me to

Hello, I have some data file, say, mydata 1,2,3,4,5,6,7 3,3,4,4,w,w,1 w,3,6,5,7,8,9 4,4,w,5,3,3,0 i want to replace some percentages of "mydata" file in to NAs for those values that are NOT w's. I know how to apply the percentage thing here but don't know how to select those values that are not "w"s. So far, i was able to do it but the result replaces the w's

Ok, I have a new, multipart problem that I need help figuring out. Part 1. I have a three dimensional array (species, sites, repeat counts within sites). Sampling effort per site varies so the array should be ragged. Maximum number of visits at any site = 22 Number of species = 161 Number of sites = 56 I generated the array first by;

Hi, I am trying to discretize a numeric attribute of a data.frame using chiMerge mydata2<-chiMerge(mydata1, c(7), alpha = 0.05) but this command never returns, and I have to forcefully STOP the operation. Is this a bug or am I missing somthing? Can anybody help me. please? Thanks in advance.

I am new to R and use nnetTs - calls. If a time series of let's say 80000 Datapoints and did call nnetTs I want make a new net for the old ponts plus the next 1000 points (81000 datapoints total) what would again cost much calculation time. So I want to pre-init the new net with the former wonnen net to reduce the necessary iteration numbers. Is thee a possibility to do that and how? i.e.:

Dear Members, Greetings! I have come across a discrepancy shown by R and SAS results on same data for logistics regression.. When I processed the above csv file(1000.csv) for predicting the Action (i/c) by Age Group(1-7,Na) and Gender(M,F,Na) with GLM of R I get: R result Call: glm(formula = Action ~ Gender + AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance