similar to: confidence intervals for mean (GLM)

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

Dear users, I have a double for loop that does exactly what I want, but is quite slow. It is not so much with this simplified example, but IRL it is slow. Can anyone help me improve it? The data and code for foo_reg() are available at the end of the email; I preferred going directly into the problematic part. Here is the code (I tried to simplify it but I cannot do it too much or else it

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

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

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

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",

Can anyone tell me how I can supply more than one graph to plotCI (gplots) at once? Below is what I tried, also with rbind instead of cbind. What is the way to do this (in general, I think)? Problem is that lines of 1-st and 2-nd series are mixed, while they have nothing to do with each other. I also tried calling plotCI with argument add=TRUE, which didn't seem to work (that is actually

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

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

Dears useRs, I have 2 factors, (for the sake of explanation - A and B), with 4 levels each. I've already fitted a negative binomial generalized linear model to my data, and now I need to split the factors in two distinct analysis of deviance table:  - A within B1, A within B2, A within B3 and A within B4  - B within A1, B within A2, B within A3 and B within A4 Here is a code that illustrates

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.:

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 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

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

Assume a C program invoked by .Call, that returns a list. Near the top of the program we allocate space for all the list elements. (It is my habit to use "xyz2" for the name of the R object and "xyz" for the pointer to its contents.) PROTECT(means2 = allocVector(REALSXP, nvar)); means = REAL(means2); PROTECT(u2 = allocVector(REALSXP, nvar)); u =

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

Dear R users, I'm wanting to sample from the negative binomial distribution using the rnegbin function from the MASS library to create artificial samples for the purpose of doing some power calculations. However, I would like to work with samples that come from a negative binomial distribution that includes only values greater than or equal to 1 (a truncated negative binomial), and I