search for: litter

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= litter*mother and id=litter with a matrix s...

...st. Another possibility is to produce a warning when you use drop1 with the scope argument to get main effects in the presence of an interaction (even not signifikant) with "treatment" contrast. An example: library(MASS) ##- Data "genotype" names(genotype) ##- > [1] "Litter" "Mother" "Wt" ##- to be sure the contrasts are "treatment" options(contrasts = c("contr.treatment", "contr.poly" )) ##- model with interaction gen.int <- aov(Wt ~ Litter*Mother, data = genotype) drop1(gen.int, scope = .~., test = "F&...

...tive? Partial output follows. Thanks for the insight. Hank > anova(fm0) numDF denDF F-value p-value (Intercept) 1 69 6331.902 <.0001 Fert 3 44 42.176 <.0001 Seed 1 44 0.488 0.4886 Litter 1 69 1.830 0.1805 Density 1 69 68.714 <.0001 Fert:Seed 3 44 0.061 0.9799 Fert:Litter 3 69 0.294 0.8294 Fert:Density 3 69 0.381 0.7667 Seed:Litter 1...

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 = Surv(time, status) ~ rx + cluster(litter), data = rats) coef exp(coef) se(coef) robust se z p rx 0.905 2.47 0.318 0.303 2.99 0.0028 Likelihoo...

...ov "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 Seed S density X192.4 192 R 16 C 7 12 X192.3 192 C 16 C 14 26 X192.2 192 C 16 A 8 3 X192.1 192 R 16 A 9 12 X191.4 191 R 32 A 3 0 X191.3 191 C 32 A 10 11 ..... X146.2 146 R 32 C 7...

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) cor(random.effects(rats1)[[1]],random.effects(rats2)[[1]]) Thanks...

...'reg','folha','solo')) model.com=matrix(c( 'ridos>dendos', 'a1', NA, 'ridos>reg', 'a2', NA, 'ridos>folha', 'a3', NA, 'ridos>solo', 'a4', NA, 'ridos>raiz', 'a5', NA, 'dendos>litter', 'a6', NA, 'dendos>reg', 'a7', NA, 'litter>reg', 'a8', NA, 'folha>reg', 'a9', NA, 'solo>reg', 'a10', NA, 'raiz>reg', 'a11', NA, 'raiz>solo', 'a12', NA, 'folha>solo...

...ectively, 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 job. I would happily send anyone the data if they would be willing to help. Many Thanks in advance. Hank The model: fm <- lme( log(S,2) ~ Fert*Litter*Seed*Density, data = collimd, random = ~1|block/plot/Seed ) # The following is close, but fits lines within each panel rather than giving me the fitted values generated by the model. xyplot( log(S,2) ~ log10(Nper0.5m) | Fert*Seed, data = collimd, groups=Litter, scales = list( alte...

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 = 'dose') and i get a warning message like this: Warning message: In replications(paste("~", xx), data = mf): non-factor ignored: gesttime Is this process...

> > 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: Warning message: > cannot open file 'Users/SaraMM/PhD/Analises-LitterBags/Dados- > Litter.txt', reason 'No such file or directory...

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

...output for two models constructed with the rats data set. >library(survival) >data(rats) > str(rats) 'data.frame': 150 obs. of 4 variables: $ time : int 101 104 104 77 89 88 104 96 82 70 ... $ tumor : int 0 0 0 0 0 1 1 1 0 1 ... $ trt : int 1 1 1 1 1 1 1 1 1 1 ... $ litter: int 1 2 3 4 5 6 7 8 9 10 ... >m1<- coxme(Surv(rats$time, rats$tumor) ~ rats$trt + (1|rats$litter)) >m1 Cox mixed-effects model fit by maximum likelihood Data: rats events, n = 40, 150 Iterations= 10 54 NULL Integrated Penalized Log-likelihood -185.6822 -180...

...countered 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) Error in coxme.fit(X, Y, strats, offset, init, control, weights = weights, : No starting estimate was successful In R 2.11.1 > library(coxme) > rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats) &g...

...se forwarding). I was planning on just having the AuthorizedKeysCommand take the username, look up the keys from the webservice, and return them. Easy! The trouble is that the user isn't created on the machine beforehand. But I actually don't want the user created, b/c I don't want to litter all these servers with little user directories. Users may be transient as well - so littering the directories of these machines with tons of data just causes many other problems (running out of inodes, disk-space, etc). Any ideas? Thanks! Cary

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 se...

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

...ble?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx : 2*pnorm(-0.239/0.0816)=0.0034 ? Any help/hints are appreciated. Steen and R 2.2.1, survival 2.21 on win XP. library(survival) data(rats) marginal.model <- survreg(Surv(time, status) ~ rx + cluster(litter), rats ) summary(marginal.model) > library(survival) > data(rats) > marginal.model <- survreg(Surv(time, status) ~ rx + cluster(litter), rats ) > summary(marginal.model) Call: survreg(formula = Surv(time, status) ~ rx + cluster(litter), data = rats) Value Std. Er...

..., 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 with code that's not necessary on the primary platforms. Aren't Windows users accustomed to this feature being missing anyway? Brian

...ning 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 the name of the article. In other words, full_filename is reading "~/TESTNow they want us to charge our electric cars from litter bins.docx", so logically, this file doesn't exist. To work, the '/' needs to be inserted to differentiate between the end of the path name and the start of the article name. I've tried both paste0, as you suggested, and paste but neither do the trick. Is this a result of me...

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 all this I'm going to suggest a little tweak to this code which we can then build into a for loop: filepath <- getwd() #you will want to change this later. You are doing something with tcl to pick...