search for: litterally

Hello, I'm actually trying to fit a gee model with 2 nesting levels since I expect a correlation between all members of a litter at a first level and between all individuals sharing a mother at a second superior level with an exchangeable matrix. I order my dataframe by both mother and litter I try several syntaxes: id= mother*litter which give the same correlation matrix as id=

Dear R Core Team I've a proposal to improve drop1(). The function should change the contrast from the default ("treatment") to "sum". If you fit a model with an interaction (which ist not signifikant) and you display the main effect with drop1( , scope = .~., test = "F") If you remove the interaction, then everything's okay. There is no way to fit a

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

Windows 2000 . 5.00.2195 with Service Pack 1. R 1.5.1 Output from my split-split plot aov "alerted" me that I have done something wrong. I designed an experiment with all combinations of all levels of each treatment, but lost a little data (3 out of 192 plots). With the following data, I run the following model: > collim[c(1:6,187:192),c(1,3:6,9)] plot Litter Fert

Hello, Why the correlation between the random effects is negative? library(coxme) rats1 <- coxme(Surv(time, status) ~ (1|litter), rats) random.effects(rats1)[[1]] #one value for each of the 50 litters print(rats1) rats2 <- lmekin(time ~ (1|litter), rats) fixed.effects(rats2) random.effects(rats2)[[1]] #one value for each of the 50 litters print(rats2)

Hello useRs, I`m running structural equation models with R, but for one of my models the below error message apears. I`m trying to change startvalues but without success. The manual for sem package did not help me. Does anyone knows how to change startvalues for iteration in sem package? Or it can be another problem with the model? Error in startvalues(S, ram, debug = debug, tol = start.tol)

Using R v. 1.7.0 on Windows 2000 I would like to plot the fitted values of a model as a function of a continuous covariate, augmented with data (e.g., augPred) grouping by combinations of fixed effects. I have not been able to use augPred effectively, and am wondering if it does not handle unbalanced data (3 out of 192 missing). I include below the model and an xyplot that almost does the

Hi, I'm wondering if i can use the function "TukeyHSD" to perform the all pairwise comparisons of a "aov()" model with one factor (e.g., GROUP) and one continuous covariate (e.g., AGE). I did for example: library(multcomp) data('litter', package = 'multcomp') litter.aov <- aov(weight ~ gesttime + dose, data = litter) TukeyHSD(litter.aov, which =

> > Dear all, > > I have been usig R for some time, but now I have a MAC instead of a > PC, am I am having problems in reading files... > > > I have tried: > Data<-read.table("Users/SaraMM/PhD/Analises-LitterBags/Dados- > Litter.txt",head=T) > > but it said: > Error in file(file, "r") : unable to open connection > In addition:

Hi I am performing an analysis of variance with two factors, each with two levels. I have differing numbers of observations in each of the four combinations, but all four combinations *are* present (2 of the factor combinations have 3 observations, 1 has 4 and 1 has 5) I have used both anova(aov(...)) and anova(lm(...)) in R and it gave the same result - as expected. I then plugged this into

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats

Dear all, I have encountered the following problem where coxme seems to allow model with only random effect in R 2.11.1 but not in R 2.13.0. Following is the error message using rat example data. Any comment on this is appreciated. In R2.13 > library(coxme) > rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats) > rat0 <- coxme(Surv(time, status) ~ (1|litter), rats)

Hi all. I have a situation that I wonder someone may have run into - or has a direction I should dig / develop in. Let's say I have a system with 1M "users". Their public keys are stored in a database, and I can access them via a web call. I have a few servers which should allow those users access. Some constraints to make it non-crazy. The users can only reverse tunnel. They

I guess I didn't want to litter the users table either - it just seems "wrong" to be actually adding things to the host when it is really so transient. It feels like it should be LDAP-ish. Just ask the server for the keys and do a one-off authentication. But I've seen even LDAP creates the user directories. I see that 2.6 kernels can have some 4B users, which should last me a

Dear friends - We have 25 rats, 14 of these subjected to partial removal of kidney tissue, 11 to sham operation, and then followed for 6 weeks. So far we have data on 26 urine metabolites measured by NMR 7 times during the observation. I have smoothed the measurements by b.splines in fda including a roughness penalty, and inspecting the mean curves for nephrectomized and sham animals indicate

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx :

On Mar 17, 2013, at 03:57, LRN wrote: > /me looks at chmod and utime wrappers: > Ah, i knew i've missed something! :) > Also, i didn't consider wildcards (i thought shell was supposed to > handle them...). I believe that shell does handle wildcards on all Unix variants, including OSX. Since Windows does not handle them, I suggest that the main flac code not be littered

Thanks Ivan and Calum I continue to appreciate your support. Calum, I entered the code snippet you provided, and it returns 'file missing'. Looking at this, while the object 'full_filename' exists, what is happening is that the path from getwd() is being appended to the title of the article, but without the '/' between the end of the path name (here 'TEST' and

help(read_docx) says that the function only imports one docx file. In > order to read multiple files, use a for loop or the lapply function. > I told you people will suggest better ways to loop!! > > docx_summary(read_docx("Now they want us to charge our electric cars > from litter bins.docx")) should work. > Ivan thanks for spotting my fail! Since the OP is new to