similar to: Importing data with different delimters

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 Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

##I want to remove the rows where the row sums are zero and this is as far as I have gotten ffg <- (structure(list(CD = c(0, 0, 0, 0, 3.125, 0, 0, 0, 0, 1.6, 3.125, 0, 0, 6.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.125, 0, 0, 0, 0, 0, 0, 0, 0,

I've got a list of 5 matrices that are each 5 x 6. I'd like to end up with a 5 x 6 matrix that contains the mean value of the 5 original matrices. I can do this by brute force, but there must be a better way than making each matrix into a vector and then remaking a matrix thanks very much for any help david freedman ll=list(structure(c(9.7, 17.6, 20.8, 24.1, 33.8, 14.5, 25.7, 29.8,

I'm running R 2.13 on Ubuntu 10.10 I have a data set which is comprised of character strings. site = readLines('http://www.census.gov/tiger/tms/gazetteer/zips.txt') dat <- c("01, 35004, AL, ACMAR, 86.51557, 33.584132, 6055, 0.001499") dat I want to loop through the data and construct a data frame with the zip code, state abbreviation, and city name in seperate columns.

Dear R experts, For quite some time I have been trying to solve a mistery of reading a seemingly trouble-free text file. The data is temperature reconstruction arranged as a huge grid, preceded by seven "header lines" (which you see better if file is opened in Firefox or Chrome). This is the data (gridded temperature reconstruction)

Dear All, 1.I have been trying to aggregate my data but I have not been getting it correctly. In the data I want to convert hourly data into daily averages. Here is a sample of the data: Neph_no Date Time Temp_C Pressure_kPa RH 9 2014/03/28 10:00:00 38.4 95.9 29.7 9 2014/03/28 11:00:00 37.8 95.8 29.2 9 2014/03/28 12:00:00 36.7 95.8 35.1 9 2014/03/28 13:00:00 35.4 95.8 38.9 9

Hi, Suppose I have the following vector (data points): > x [1] 36.0 57.3 73.3 92.0 300.4 80.9 19.8 31.4 85.8 44.9 24.6 48.0 [13] 28.0 38.3 85.2 103.6 154.4 128.5 38.3 72.4 122.7 123.1 41.8 21.7 [25] 143.6 120.2 46.6 29.2 44.8 25.0 57.3 96.4 29.4 62.9 66.4 30.0 [37] 24.1 14.8 56.6 102.4 117.5 90.4 37.2 79.6 27.8 17.1 26.6 16.3 [49] 41.4 48.9 24.1

I am hoping for some advie regarding the following scenario. I have data from 26 studies that I wanted to analyse using logistic regression. Given the data was from studies and not individuals I was unsure how I would perform this in R. When analysed in SPSS, weighting was used so that each study was included twice. Where "use" occurred, a value of 1 was assigned and was weighted

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, I would like to plot time against rainfall data (data is at the end) using xyplot. The basic code looks like this: xyplot(rainfall~time, type="a") When I do this, the graph looks ok except that the x-axis has too many values. I would just like to display the years and not the months on the x-axis. I've been fiddling around with 'scales', and read previous posts about

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

Hi Every one, I have a dataframe "class" with "name", "sex", "age", "height", "Weight". if i caluclate summary statistics with the below code numSummary(class[,c("Height", "Weight")], groups=class$Name, statistics=c("mean", "sd", "quantiles"), quantiles=c(0, .25,.5,.75,1)) iam getting

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Hi, How can I convert the matrices to list. For example I have this snippet: samples<-mymatrix[1,] print(samples) which prints: V1 V2 V3 V4 V5 V6 1 103.9 88.5 242.9 206.6 175.7 164.4 How can I convert the object "samples" such that it prints: [1] 103.9 88.5 242.9 206.6 175.7 164.4 The reason I ask this because I can't use the former "samples"

Dieter Menne pointed out that the (small) xml attachment didn't make it. Here is an in-line version (see end of message). Let's hope it works this time. I'm struggling with interpreting XML files created by ADODB as data.frames and I'm looking for advice. Note: This xlm contains a result set which comes from a rectangular data array. I've been trying to play with

I asked this before and I am going to try again in more applied terms. I am trying to use R to extract variance components for a two-factor random effects model with both factors crossed. It would also be nice to generate some confidence intervals as well. For example, a data set using SAS Proc Mixed is below followed by the four variance component estimates and the respective confidence

I am using R-2.10.1 binary from CRAN on a WinXP Pro system. I also use SAS v9.2 on the same box. I just started using the SAS ODBC driver that comes with version 9 of SAS. I have been able to set up an ODBC source for SAS datasets using the driver, and then with RODBC I am able to read a sample SAS dataset. > library(RODBC) > ch <- odbcConnect('sasodbc', believeNRows=FALSE)

I apologize that I've asked a similar question before, but being new to R I don't think I did a very good job of formating the question. I've included a text file since the date set is somewhat large. What I have is a huge string of numbers in a text file. The numbers are all separated by comma's and the groups are separated by a semicolon. What I would like to do is read each

I'm running nut 2.0.1 with an MGE pulsar evolution 3000 with the mge-utalk (serial) driver on NetBSD 3.1 . I had a very strange occurance in my basement datacenter this early AM. the following was logged by upslog: 20080219 023452 100.0 123.0 049.0 [OL CHRG] NA 60.00 20080219 023522 100.0 122.8 049.0 [OL CHRG] NA 60.00 20080219 023552 098.0 123.0 049.0 [OL DISCHRG] NA 60.00 20080219 023622