similar to: please help ! label selected data points in huge number of data points potentially as high as 50, 000 !

Dear R users, Here is my problem: # example data name <- c(paste ("M", 1:1000, sep = "")) xvar <- seq(1, 10000, 10) set.seed(134) p <- rnorm(1000, 0.15,0.05) dataf <- data.frame(name,xvar, p) plot (dataf$xvar,p) abline(h=0.05) # I can know which observation number is less than 0.05 which (dataf$p < 0.05) [1] 12 20 80 269 272 338 366 368 397 403 432 453

Hi. I searched the list and didn't found nothing similar to this. I simplified my example like below: #I need calculate correlation (for example) between 2 columns classified by a third one at a data.frame, like below: #number of rows nr = 10 #the third column is to enforce that I need correlation on two variables only dataf =

Dear all, In order to ease the transition from SPSS to R for some of my colleagues, I am trying to create a function which would show the variables and their labels (if those exist), using function "label" in package Hmisc. A toy example would be this: my.data <- data.frame(age=c(24,35,28), gender=c("Male", "Female", "Male")) require(Hmisc)

Hi, I have a global data-frame in my R script. At some point in my script, I want to update certain columns of this data-frame by calling in an update function. The function looks like this: # get events data. This populates a global event data frame in the R-script events <- getEvents(con, eventsFilePath) # events has columns eventid, timeStamp, isSynchronized, timeDiff; with millions of

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called

Hello; I am having a problems with the interpretation of models using ordered or unordered predictors. I am running models in lmer but I will try to give a simplified example data set using lm. Both in the example and in my real data set I use a predictor variable referring to 3 consecutive days of an experiment. It is a factor, and I thought it would be more correct to consider it ordered. Below

Hello, I have two problems in automating multiple glm(s) operations. The data file is tab delimited file with headers and two columns. like "ABC" "EFG" 1 2 2 3 3 4 dat <- read.table("FILENAME", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dataf <- read.table("FILENAME", header=FALSE,

I am getting a difference in results when running some analysis using by and tapply compare to using a for loop. I've tried searching the web but had no luck with the keywords I used. I've attached a simple example below to illustrates my problem. I get a difference in the mean of yvar, diff and the p-value using tapply & by compared to a for loop. I cannot see what I am doing wrong.

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

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

I am running R version 2.15.1 in Windows XP I am having problems with a function I'm trying to create to: 1. subset a data.frame based on function arguments (colname & parmname) 2. rename the PARMVALUE column in the data.frame based on function argument (xvar) 3. generate charts plotvar <- function(parentdf,colname, parmname,xvar,yvar ){ subdf <-

I have the following code: ``` rm(list=ls()) N = 30000 xvar <- runif(N, -10, 10) e <- rnorm(N, mean=0, sd=1) yvar <- 1 + 2*xvar + e plot(xvar,yvar) lmMod <- lm(yvar~xvar) print(summary(lmMod)) domain <- seq(min(xvar), max(xvar))??? # define a vector of x values to feed into model lines(domain, predict(lmMod, newdata = data.frame(xvar=domain)))??? # add regression line, using

Hi, I have a large dataframe than I'm writing functions to explore, and to reduce cut and paste I'm trying to write a function that does a subset and then a plot. Firstly, I can write a wrapper around a plot: plotwithfits <- function(formula, data, xylabels=c('','')) { xyplot(formula, data, panel = function(x,y, ...) { panel.xyplot(x,y,

Hello All, I am trying to write a function which would operate on columns of a dataframe specified in parameters passed to that function. f = function(dataf, col1 = "column1", col2 = "column2") { dataf$col1 = dataf$col2 # just as an example } The above, of course, does not work as intended. In some languages one can force evaluation of a variable, and then

Dear all; Is there any way to make this to work?: .x<-rnorm(50,10,3) .y<-.x+rnorm(50,0,1) X<-data.frame(.x,.y) colnames(X)<-c("Xvar","Yvar") Ytext<-"Yvar" lm(Ytext~Xvar,data=X) # doesn't run lm(Yvar~Xvar,data=X) # does run The main idea is to use Ytext as input in a function, so you just type "Yvar" and the model should fit....

Hi, I like the formatting and the appearance of lattice plots. But I have not succeeded in gettting the right format in my plots with the lattice package in one of my applications. In?the code shown below, I start by constructing a general data frame and show my attempts with the lattice package commands. After that, I use the graphics package and show the kind of plot that I want to get. I would

Dear users, By running the script below, R crashes systematically at the last command, namely dev.off(), on Windows 7, but not on Windows XP. I therefore don't provide a reproducible example and do not really extract the relevant parts of the script because it has most likely nothing to do with the script itself. I can do it though if you think it might be relevant. R crashes on Windows

Hi, I am writing a function that writes to a csv file for every call. However, for the subsequent calls, I want to append the data to the existing csv file without appending the column names again. I tried searching in the previous posts, but I am stuck with different errors. Here is what I am doing (dataF is a data-frame):- outputFilePath <- paste(getwd(), "/",

In R, I'm imported a data frame of 2,321,123 by 4 called "dataF". I converted the data frame "dataF" to a matrix dataM <- as.matrix(dataF) Does R have an efficient routine to treat the special elements that contain "inf" in them. For example, can you separate the rows that have "inf" elements from the matrix into a separate matrix without