search for: treating

I was wondering if somebody could explain why I get different results here: >treats[,2]<-as.factor(treats[,2]) >treats[,5]<-as.factor(treats[,5]) >treats[,4]<-as.factor(treats[,4]) #there are 'c' on more days than I have 'h2o2', where treats[,4] is the day. I only want 'c' that correspond to the same days that I have a 'h2o2' also.

Hi, I am trying to run ANOVA with an interaction term on 2 factors (treat has 7 levels, group has 2 levels). I found the coefficient for the last interaction term is always 0, see attached dataset and the code below: > test<-read.table("test.txt",sep='\t',header=T,row.names=NULL) > lm(y~factor(treat)*factor(group),test) Call: lm(formula = y ~ factor(treat) *

Dear all, I'm trying to rerun some data linkage exercises in R (they are designed to be done in SPSS, SAS or STATA) The exercise in question is to relabel the column "treat" to "1", if "yearsep" is smaller than 1988 and columns "proc1"-"proc3" contain the values 56.36 or 59.81. My pathetic solution to do this in R currently looks like

Dear list, I tried to use the nparcomp to run some post hoc non-parametric comparison and got and error. Error in uniroot(pfct, interval = interval) : f() values at end points not of opposite sign Appreciate any comments. the command line: >nparcomp(Ulceration~Group,data=test,type='Dunnett',control='Non-treated') Jun

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Hi all I am trying to do a simple recode which I am stumbling on. I figure there must be any easy way but haven't come across it. Given data of A","B","C","D","E","A" it would be nice to recode this into say three categories ie A and B becomes "Treat1", C becomes "Treat 2" and E becomes "Treat 3". I tried

Hi all, Sent the following question yesterday, but haven't got any suggestions yet. So just trying again, can anyone comment on the problem that I have? Thank you! ------------- Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using

Dear list, I cannot figure out why, after sub-setting my data, that particular item which I don't want to plot is still in the newly created subset (please see example below). R somehow remembers what was in the original data set. A work around is exporting and importing the new subset. Then it's all fine; but I don't like this idea and was wondering what am I missing here? Thanks!

Hi Bert, Thanks for the suggestions. I tried lme with different control parameters, and also tried using "ML", instaed of "REML", but still got the same answers. Yes, I hope some gurus on this list could give me some hints. Thanks --- "Gunter, Bert" <bert_gunter at merck.com> wrote: > But they are close. This is almost certainly a > numeric issue --

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat :

Dear all, I have some growth curve data from an experiment that I try to fit using lm and lmer. The curves describe the growth of classification accuracy with the amount of training data t, so basically y ~ 0 + t (there is no intercept because y=0 at t0) Since the growth is somewhat nonlinear *and* in order to estimate the treatment effect on the growth curve, the final model is y ~ 0 + t +

Hi, I'm trying to analyse a hierachical design and am running into some trouble. Clearly I don't fully understand "Error" and I was hoping someone could set me straight. We measure percentage algal cover in each of 5 quadrats from each of 16 patches where 4 treatments are randomly allocated to a patch. First suppose patches are coded 1 to 16. then the following gives the

R-helpers, Thanks in advance for your help. I am an R newbie and I am having trouble figuring out the easiest/most efficient way to assign classes to columns in a newly created data frame. R seems to want to convert everything to a factor when I use the cbind function to compile vectors into a data frame. ID<-seq(1,10,1) TREAT<-c(rep("B",5),rep("G",5))

I have a vague recollection of seeing reference, fairly recently, to a function that forms a single factor which ``codes'' the interaction between two (or more?) factors. Do I recollect correctly? It would be easy enough to roll one's own, but if there is an existing function it probably does a much cleverer job than I would do. cheers, Rolf Turner rolf at math.unb.ca

Hi all, I'm trying my best in changing the names of my experiments in the spectral map. My experiments are from six different time points, two as control and two treated experiments. I set have for each time point a different color . Now I want to change the symbols so that I have for the treated experiments one and for the controls a different one. But I want only two different symbols

Hi, I have a frame "treat" and want to find matched pairs in the data frame "control". In the matched (combined) data frame there should be two variables (0/1),indicating the "source" of the data (treat or control), so that it is possibe to set a "filter" (extraxt/select data). #Here are the dataframes (my real data frames have many variables) treat <-

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an

Hello all! I have a problem that calls for a better understanding, than mine, of how lme() uses the random part of the call. The dataset consists of eleven field trials (Trial) with three replicates (Block) and four fertiliser treatments (Treat). Analysing for example yield with lme() is easy: m1 <- lme(Yield ~ Treat, data=data, random =~1| Trial/Block) giving estimates of