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Dear all, I would like to fit a linear regression with replication (on each year, observation is replicated, e.g 4 times). The independent variable ranges for instance 1-5 year, so I expect to have a linear fit of 5 points. For that purpose I do these (with dummy variables x and y): x<-rep(seq(1:5),4) y<-rnorm(20) linreg<-lm(y~x) fitted.values(linreg) # why produce 20 points of

I have a simple table of data: Result Var1 Var2 Var3 1 0.10 0.78 0.12 0.38 2 0.20 0.66 0.39 0.12 3 0.10 0.83 0.09 0.52 4 0.15 0.41 0.63 0.95 5 0.60 0.88 0.91 0.86 6 -0.02 0.14 0.69 0.94 I am trying to achieve two things: 1) Manipulate this data so that I have the "Result" data unchanged, and all the other data

Hi Group, I performed the following commands to build my package in R 2.0 under Windows XP I got all my tools from Dr. Duncan Mudroch's website. I did a RCMD build dnal and it built a tar file for me. I did a RCMD INSTALL dnal and it installed well. When i do RCMD check dnal i get the following 2 WARNINGS with no Errors. checking package directories..WARNING Subdirectory 'src'

Trying to do a meta-analysis of correlations in R using the meta package; have tried several things and keep getting a similar error. Can anyone help explain the error? > cor<-c(-0.3018, 0.667, -3.8002, -0.607, -0.4885, -3.8002, -0.0701, 0.1348, -0.9505, -0.5709, -0.6127, -1.2419, -0.1511, -0.1054)> n<-c(3,4,3,3,3,3,16,36,30,9,3,3,30,4)> library(meta)> metacor(cor, n, data=NULL,

Hi, I wonder if it is possible to create an DLL or EXE file performing R procedures. Instead of running R, reading data and calling some procedures, I would like to use R functions in the following way: "C:\linearRegression.exe data.txt" which would produce let's say file "output.txt" with the results. Is there some way how to do it? Thanks a lot. Pavel Vanecek

I have been trying to run a logistic regression using a number of studies. Below is the syntax, error message & data. Any advice regarding what I am doing wrong or solutions are appreciated, regards Bob Green > logreg <- read.csv("c:\\logregtest.csv",header=T) > attach(logreg) > names(logreg) [1] "medyear" "where" "who"

Hi there, I've looked through the very helpful advice about adding fitted lines to plots in the r-help archive, and can't find a post where someone has offered a solution for my specific problem. I need to plot logistic regression fits from three differently-sized data subsets on a plot of the entire dataset. A description and code are below: I have an unbalanced dataset

I really like these Trellis graphics but how do I get this code to skip the missing? logreg<-read.csv("logreg.csv", header=TRUE, sep=",", na.string=" ") attach(logreg) bwplot(yesno~bc_pcb_tot |varlist, data=logreg, main="Box Cox PCB transformation", auto.key=TRUE, fontfamily = "HersheySans" ) Dean Sonneborn M.S. Public Health Sciences *

Dear R-users: How does one calculate in R the odds ratios for a CATEGORICAL predictor variable that has 4 levels. I see r-help inquiries regarding odds ratios for what looked like a continuous predictor variable. I was wondering how to get the pairwise odds ratios for comparisons of levels of a categorical predictor variable. I can't seem to get the correct output using: >

Hi, I was trying to plot the logistic regression from a regression "logreg" I just ran. I downloaded the "car" package from the R website and went to Packages -> install package from local zip file I checked in my library file and the package is there. I restarted R. I then ran the command: reg.line(logreg,col=palette()[2], lwd=2, lty=1) And I get the error: Error: could

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Hi there: recently i try to use LogicReg package for a tree model(logistics fit ) . i list my code and error below: > dim(model.dat) [1] 48000 745 > fit1 <- logreg(resp = model.dat[,745], bin=model.dat[, 9:700], type = 3, select = 3, ntrees = c(1,2), nleaves=c(1,7), ) Insufficient declaration LGCn1MAX in logreg() is 20000 LGCn1MAX should be at least 48000 Please fix and

Hello. I have run the command imp<-mice(mydata, im=c("","pmm","logreg","logreg"),m=5)  for a variable with no missing data, a numeric one and two variables with binary data. I got the following message: There were 37 warnings (use warnings() to see them) > warnings() Warning messages: 1: In any(predictorMatrix[j, ]) ... : coercing argument of

I want to get some "simple" logic regression examples to work before exploring a hard problem. I can get results, but I'm having some problems using "cat" to save the logic regression equation to a disk file. Consider this: # Simple Logic Regression Example # efg, 23 Feb 2005 library(LogicReg) # Create simulated data with known logic equation: # "noise"

Hello, dear R community. The thing is that I am not in the least a developer, neither do I want to create a package of my own. But recently I have found a package LogicForest, which is in the base written in Fortran I think. And well, in its manual it is written that there are several parameters there that had had to be "hard coded", but which in essence actually have no restrictions.

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

I'm in the process of converting some S3 methods to S4 methods. I have this function : setGeneric("enrichmentCalc", function(rs, organism, seqLen, ...){standardGeneric("enrichmentCalc")}) setMethod("enrichmentCalc", c("GenomeDataList", "BSgenome"), function(rs, organism, seqLen, ...) { ... ... ... })

Dear list, I tried to apply the logistic regression to different response variables from a dataframe and would like to store the results using the function logistic.display from the "epicalc" package in a list, but got an error message "Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1". All the response variables have value of 0 or 1. It worked

sorry to bother everyone. i have question to get the coefficient and confidence interval in Logic Regression with Logistic model. below i list the R code X <- matrix(as.numeric(runif(400) < 0.5), 50,8) colnames(X) <- paste("X", 1:ncol(X), sep="") rownames(X) <- paste("case", 1:nrow(X), sep="") # Define expected result: Y = (NOT X1) AND X5 Y

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