similar to: Random intercept model with time-dependent covariates, results different from SAS

Greetings listeRs - Given a data frame such as times time1 time2 time3 time4 1 70.408543 48.92378 7.399605 95.93050 2 17.231940 27.48530 82.962916 10.20619 3 20.279220 10.33575 66.209290 30.71846 4 NA 53.31993 12.398237 35.65782 5 9.295965 NA 48.929201 NA 6 63.966518 42.16304 1.777342 NA one can use "transform" to

Dear All,   #I have the following code   Dose<-1000 Tinf <-0.5 INTERVAL <-8 TIME8 <-matrix(c((0*INTERVAL):(1*INTERVAL))) TIME7 <-matrix(c((0*INTERVAL):(2*INTERVAL))) TIME6 <-matrix(c((0*INTERVAL):(3*INTERVAL))) TIME5 <-matrix(c((0*INTERVAL):(4*INTERVAL))) TIME4 <-matrix(c((0*INTERVAL):(5*INTERVAL))) TIME3 <-matrix(c((0*INTERVAL):(6*INTERVAL))) TIME2

Dear list, I´m calculating time differences between series of time stamps and I noticed something odd: If I do this... > time1=strptime("2009 05 31 22 57 00",format="%Y %m %d %H %M") > time2=strptime("2009 05 31 23 07 00",format="%Y %m %d %H %M") > > diff(c(time1,time2),units="mins") Time difference of 10 mins .. I get the correct

Dear R folk, First off I want to thank those of you who responded with comments for my R quick and dirty stats tutorial. They've been quite helpful, and I'm in the process of revising them. When it comes to repeated measures MANOVA, I'm in a bit of a bind, however. I'm beginning to see that all of the documentation is written for psychologists, who have a slightly

Dear R people, I wonder if you could give me a hand with some of my data. I have a very typical analysis in biology, however it is difficult for me to find the right way to analyse. I had a group of animals, I gave them a treatment, and I measure a variable along time -one??s per day- along 5 days,for example(fake data): Animals Time1 Time2 Time3 Time4 1 1 5 3

I am trying to plot where data points from a give subject are connected by a line. Each subject is represented by a single row of data. Each subject can have up to five observations. The first five columns of mydata give the time of observation, columns 6-10 give the values at each time point. Some subjects have all data, some are missing values. The code I wrote to draw the plot is listed below.

I have 100+ .csv files which have the basic format: > test X Substance1 Substance2 Substance3 Substance4 Substance5 1 Time1 10 0 0 0 0 2 Time2 9 5 0 0 0 3 Time3 8 10 1 0 0 4 Time4 7 20 2 1 0 5 Time5

Dear R Masters, I'm an anesthesiology resident trying to make his way through basic statistics. Recently I have been confronted with longitudinal data in a treatment vs. control analysis. My dataframe is in the form of: subj | group | baseline | time | outcome (long) or subj | group | baseline | time1 |...| time6 | (wide) The measured variable is a continuous one. The null hypothesis in

Hello, Below is the code for a basic bar graph. I was seeking advice regarding the following: (a) For each time period there are values from 16 people. How I can change the colour value so that each person has a different colour, which recurs across each of the three graphs/tie epriods? (b) I have seen much more sophisticated examples using lattice (e.g each person has a separate

Hi all I am running a 2-way repeated measure anova with 1 between-subjects factor (Group=treatment, control), and 1 within-subject factor (Time of measurement: time1, time2). I extract the results of the anova with: summary(aov(effect ~ Group*Time + Error=Subj/Time, data=mydata)) Now, this must be clearly a dumb question, but how can I quickly extract in R all the post-hoc t-tests for the

Hi! I try to explain the efffect of (1) forest where i took samples's soils (* Lugar*: categorical variable with three levels), (2) nitrogen addition treatments (*Tra*: categorical variable with two levels) on total carbon concentration's soil samples (*C: *continue* *variable) during four months of sampling (*Time:* categorical and ordered variable with four levels). I fitted the

Probably a stupidly simple question, but I wouldn't know how to google it: xyplot(neuro ~ time | UserID, data=data_sub) creates a proper plot. However, if I add type = "l" the lines do not go first through time1, then time2, then time3 etc but in about 50% of all subjects the lines go through points seemingly random (e.g. from 1 to 4 to 2 to 5 to 3). The lines always start at time

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

Dear All, I regularly want to "apply" some function to an array in a way that the arguments to the user function depend on the index on which the apply is working. A simple example is: A <- array( runif(160), dim=c(5,4,8) ) x <- matrix( runif(32), nrow=4, ncol=8 ) b <- runif(8) f1 <- function( A, x, b ) { sum( A %*% x ) + b } result <- rep(0.0,8) for (i in 1:8) {

Hello, I am trying to find a direct way to write a nested if of sorts to find data for a specific time range for a specific day (across a range of days) and have exhausted my abilities with the manuals I have at hand. I have a good deal of data of this approximate form: day time price 1 1am 5 1 2am 7 1 3am 9 1 4am 12 2 1am 5 2 2am 7 2

Hi, I need help in calculating Java method time-stamp in following fashion. Consider following method example. long method3(long stop) { try { Thread.sleep(1500); } catch (Exception e) { } //////////////////// real CPU intensive operation /////////////////////////// for (int i = 1; i < stop; i++) { stop = stop * stop * i; };

Dear list, [cross-posting from Stack Overflow where this question has remained unanswered for two weeks] I'd like to perform a numerical integration in one dimension, I = int_a^b f(x) dx where the integrand f: x in IR -> f(x) in IR^p is vector-valued. integrate() only allows scalar integrands, thus I would need to call it many (p=200 typically) times, which sounds suboptimal. The

Hi r users, I have two data sets (X1, X2). For example, time1<-c( 0, 8, 15, 22, 43, 64, 85, 106, 127, 148, 169, 190 ,211 ) outpue1<-c(171 ,164 ,150 ,141 ,109 , 73 , 47 ,26 ,15 ,12 ,6 ,2 ,1 ) X1<-cbind(time1,outpue1) time2<-c( 0 ,8 ,15 , 22 ,43 , 64 ,85 ,106 ,148) output2<-c( 5 ,5 ,4 ,5 ,5 ,4 ,1 ,2 , 1 ) X2<-cbind(time2,output2) I want to

Hi, I would like to merge two data frames. It is just that I want the merging to be done with some kind of a fuzzy criterion. Let me explain. My first data frame looks like this : ID1 time1 dt 1 2008-01-02 13:11 10 2 2008-01-02 14:20 20 3

Hello all, I've 2 strings that representing the start and end values of a date and time. For example, time1 <- c("21/04/2005","23/05/2005","11/04/2005") time2 <- c("15/07/2009", "03/06/2008", "15/10/2005") as.difftime(time1,time2) Time differences in secs [1] NA NA NA attr(,"tzone") [1] "" How can i