similar to: creating baseline variable from a longitudinal sequence

Hi, I am very new to R and I've been trying to work through the R book to gain a better idea of the code (which is also completely new to me). Initially I imputed my data from a text file and that seemed to work ok, but I'm trying to examine linear relationships between gdist and gair, gdist and gsub, m6dist and m6air, etc. This didn't work and I think it might have something to do

Dear friends, when i use glm() to fit my data, i use glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, *family = Gamma(link = inverse),* data =a,)) It shows: error in eval(expr, envir, enclos) : *gamma distribution don't allow negative value*. But i use result<-glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, family = poisson, data =a) #this

> str(data) 'data.frame': 250 obs. of 3 variables: $ student: chr "A" "B" "C" "D" ... $ data : num 20.2 20.4 22.5 22.1 23.3 ... $ param : Factor w/ 4 levels "AGE","SCHOOL",..: 1 1 1 1 1 1 1 1 1 1 Hi , i would like to split the dataframe so that each level of param is a column At the end it should look like

Dear R-help mailing list,   I have this problem, I have a joint  longitudinal and survival data of the form say   ID Time Failuretime Censoringind longitudinalmeasure 1  0     35                  0                123 1  10    35                 0                 120  1   25   35                 1                   123 2   0    23                  0                   100 2   10  23                 

Hi all, I asked this before the holiday, didn't get any response. So would like to resend the message, hope to get any fresh attention. Since this is not purely lme technical question, so I also cc-ed R general mailing list, hope to get some suggestions from there as well. I asked some questions on how to analyze longitudinal study with only 2 time points (baseline and a follow-up)

Dear R users,   Please assist me with the following problem. I have a dataset that looks like the following:   dat<-data.frame(   'id'=rep(c(1,2,3),each=3),   'time'=rep(c(1,2,3),3),   'y'= c(2,2,NA,2,NA,NA,2,5,7) )   I wish to create a variable for dropout time in dataframe 'dat' such that the dropout time is the time to drop out by the subject as follows:    

Estimada comunidad tengo un problema del que no encuentro datos que me ayuden mucho en la web. Estoy haciendo calculos por grupos con data,table. Tengo un archivo (zp.res) con tres columnas que clasifican los datos (sol, con, dia) y una columna de datos numericos (media), de la siguiente forma: sol con dia media 1: con 0 1 -22.6 2: con 0 1 -36.6 3: con 0 1 -35.6 y

Dear R users, I am reading data from a file where there are some missing that are represented by -9999.00. I am using the command below. =====My original data =========== PARM = TMPC;T12X;T12N;DWPC;PALT;SKNT;DRCT;P24M;CLCL STN YYMMDD/HHMM TMPC T12X T12N DWPC PALT SKNT DRCT P24M CLCL 820420 110429/1200 22.50 -9999.00

Hello R Users, I am new to R. I have two data sets i) TOMS aerosol optical depth(AOD) and ii) TOMS ozone(O3). > > AOD data is on 1x1 grid and O3 data is on 5x5 grid. > > First I want to read AOD and O3 as it is and then I want to regrid AOD on > 5x5 grid as O3. > > Reading is first problem. > > FIRST PROBLEM READING AOD: > > AOD data is in following format: >

Dear all- I have a dataset (see a sample below - but the whole dataset is June 2005 - June 2008). The "LST" format is "YYMMDDHHmm" and I would like to get the hourly average of the "mph" for the summer months (spanning all years). I have been trying to use "aggregate" but am not having much success at all! any thoughts would be greatly appreciated.

hello, I'd like know how to sort a data frame in R for example how I should do to sort by Catholic with swiss data frame like below thanks Fertility Agriculture Examination Education Catholic Infant.Mortality Courtelary 80.2 17.0 15 12 9.96 22.2 Delemont 83.1 45.1 6 9 84.84 22.2

# hi all, # I'm trying to plot temperatures by date in a trellis plot by their stations # I'm plotting the following data.frame library(lattice) h <- structure(list(station_name = structure(c(3L, 4L, 2L, 10L, 11L, 12L, 6L, 7L, 5L, 8L, 9L, 3L, 4L, 2L, 10L, 11L, 12L, 6L, 7L, 5L, 8L, 9L, 3L, 4L, 2L, 10L, 11L, 12L, 6L, 7L), .Label = c("Ashqelon", "Beer Sheva",

Hi folks, I am running a very simple regression using mylm <- lm(mass ~ tarsus, na.action=na.exclude) I would like the use the residuals from this analysis for more regression but I'm running into a snag when I try cbind(mylm$residuals, mydata) # where my data is the original data set The error tells me that it cannot use cbind because the length of mylm$residuals is

Dear R-helpers, thanks for yesterday's speeding-up tip. Here is my next query: I have lots of polygons (not necessarily convex ones, and they never have holes) given by x,y coordinates. I want to get the polygon that is the union of these polygons. This is my current method, but I am hoping there is a faster method (up to thousands of polygons, each with ca. 40 xy points). Example:

I came across a post by Karl Knoblick regarding the modeling of longitudinal data (see https://stat.ethz.ch/pipermail/r-help/2007-May/132137.html). I am often asked by physicians to perform what Karl refers to in his post as option 1: to perform paired t-tests against baseline at each follow up time point (30 days, 90 days, 6 months, etc.). Unlike Karl's example, however, many of the trials

R-helpers: I am experiencing some odd behavior with the 'matrix' function that I have not experienced before and was wondering if there is something that I was missing in my code. --------------------------------- > sessionInfo() R version 2.6.1 (2007-11-26) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United

Dear List, I am not sure how should i optimize a log-likelihood numerically: Here is a Text book example from Statistical Inference by George Casella, 2nd Edition Casella and Berger, Roger L. Berger (2002, pp. 355, ex. 7.4 # 7.2.b): data = x = c(20.0, 23.9, 20.9, 23.8, 25.0, 24.0, 21.7, 23.8, 22.8, 23.1, 23.1, 23.5, 23.0, 23.0) n <- length(x) # likelihood from a 2 parameter Gamma(alpha,

Dear all - We perform some measurements with a machine that needs to be recalibrated. The best calibration we get with polynomial regression. The data might look like follows: > true_y <- c(1:50)*.8 > # the real values > m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.2 > # the measured data > x <- c(1:50) > # and the x-axes > > # Now I do the following:

On Thu, Apr 04, 2019 at 11:52:46AM -0400, Michael S. Tsirkin wrote: > I simply love it that you have analysed the individual impact of > each patch! Great job! Thanks! I followed Stefan's suggestions! > > For comparison's sake, it could be IMHO benefitial to add a column > with virtio-net+vhost-net performance. > > This will both give us an idea about whether the

On Thu, Apr 04, 2019 at 11:52:46AM -0400, Michael S. Tsirkin wrote: > I simply love it that you have analysed the individual impact of > each patch! Great job! Thanks! I followed Stefan's suggestions! > > For comparison's sake, it could be IMHO benefitial to add a column > with virtio-net+vhost-net performance. > > This will both give us an idea about whether the