similar to: no. at risk in survfit()

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Dear all R-users, I wanted to fit a Garch(1,1) model to a dataset by: >garch1 = garch(na.omit(dat)) But I got a warning message while executing, which is: >Warning message: >NaNs produced in: sqrt(pred$e) The garch parameters that I got are: > garch1 Call: garch(x = na.omit(dat)) Coefficient(s): a0 a1 b1 1.212e-04 1.001e+00 1.111e-14 Can any one

Hi R-users,   I would like to search for the values of seq that match my rand values.  In excel I will use =VLOOKUP(G2,$E$2:$F$32,2).  For example, for rand=.262 it will give me approximately seq=120 and rand=0.964293344, seq=460 and etc.   E           F     G cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60   

HI, Dear R community, I am using the following codes to plot, however, the lines code works. But the line was not drawn on the previous plot and did not shown up. How comes? # specify the data for missense simulation x <- seq(0,10, by=1) y <- c(0.952, 0.947, 0.943, 0.941, 0.933, 0.932, 0.939, 0.932, 0.924, 0.918, 0.920) # missense z <- c(0.068, 0.082, 0.080, 0.099, 0.108, 0.107,

Hi all, I can't find the error in the binomial GLM I have done. I want to use that because there are more than one explanatory variables (all categorical) and a binary response variable. This is how my data set looks like: > str(data) 'data.frame': 1004 obs. of 5 variables: $ site : int 0 0 0 0 0 0 0 0 0 0 ... $ sex : Factor w/ 2 levels "0","1": NA NA NA

Hi there, This problem has been dogging me for a bit, and I'm trying to figure out why. When running the the subsets function in the leaps library, R is giving me the following error message > lvodsub <- subsets(pred, resp$LVOD) Warning message: XHAUST returned error code -999 in: leaps.exhaustive(a, really.big = really.big) but this still happens if I add the really.big option:

Dear R members, I am new in using frailty models in survival analyses and am getting some contrasting results when I compare the Wald and likelihood ratio tests provided by the r output. I am testing the survivorship of different sunflower interspecific crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen as fixed effects, and sub-block as a random effect. I stratified

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

HI, Dear R community, I have used the following codes this morning, but this afternoon, I got the following errors: > x <- seq(0,10, by=1) > y <- c(0.952, 0.947, 0.943, 0.941, 0.933, 0.932, 0.939, 0.932, 0.924, 0.918, 0.920) # missense > z <- c(0.068, 0.082, 0.080, 0.099, 0.108, 0.107, 0.101, 0.105, 0.118, 0.130, 0.132) # missense False Negative > p <- c(0.035, 0.036,

Hi, I found a discrepancy between results in R and Stata for a factor analysis with a promax rotation. For Stata: . *rotate, factor(2) promax* (promax rotation) Rotated Factor Loadings Variable | 1 2 Uniqueness -------------+-------------------------------- pfq_amanag~y | -0.17802 0.64161 0.70698 pfq_bwalk_~ø | 0.72569 0.05570

Hi, I'm using metafor to perform a mixed-effects meta-analysis. I'd like to test whether the effect is different for animals and plants/whether "group" (animal/plant) influences the effect size, but am having trouble interpreting the results I get. I've read previous posts about QM in metafor, but I'm still a bit confused. I've dummy-coded the factors:

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Hi R help, Hi R help, Which is the easiest (most elegant) way to force "aov" to treat numerical variables as categorical ? Sincerely, Andrea Bernasconi DG PROBLEM EXAMPLE I consider the latin squares example described at page 157 of the book: Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter. This example use

Hello I am new in R. I am trying to implement Biomod 2 package. However, I have a doubt. I want to calculate the mean and sd of "Testing.data" (ROC and TSS) > # let's print the ROC scores of all selected models > myBiomodModelEval_55["ROC","Testing.data",,,] RUN1 RUN2 RUN3 RUN4 RUN5 RUN6 RUN7 RUN8 RUN9 RUN10 0.938 0.938 0.926 0.931 0.939

On Mon, Dec 16, 2013 at 04:16:29PM -0800, Michael Dalton wrote: > Commit 2613af0ed18a ("virtio_net: migrate mergeable rx buffers to page frag > allocators") changed the mergeable receive buffer size from PAGE_SIZE to > MTU-size, introducing a single-stream regression for benchmarks with large > average packet size. There is no single optimal buffer size for all >

On Mon, Dec 16, 2013 at 04:16:29PM -0800, Michael Dalton wrote: > Commit 2613af0ed18a ("virtio_net: migrate mergeable rx buffers to page frag > allocators") changed the mergeable receive buffer size from PAGE_SIZE to > MTU-size, introducing a single-stream regression for benchmarks with large > average packet size. There is no single optimal buffer size for all >

Hi all, As a molecular biologist by training, I'm fairly new to R (and statistics!), and was hoping for some advice. First of all, I'd like to apologise if my question is more methodological rather than relating to a specific R function. I've done my best to search both in the forum and elsewhere but can't seem to find an answer which works in practice. I am carrying out

Hi, According to daisy function from cluster documentation, it can compute dissimilarity when NA (missing) value(s) is present. http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/daisy.html But why when I tried this code library(cluster) x <- c(1.115,NA,NA,0.971,NA) y <- c(NA,1.006,NA,NA,0.645) df <- as.data.frame(rbind(x,y)) daisy(df,metric="gower") It gave this

I would like to know if it is possible to perform negative binomial regression with rate data (incidence density) using the glm.nb (in MASS) function. I used the poisson regression glm call to assess the count of injuries across census tracts. The glm request was adjusted to handle the data as rates using the offset parameter since the population of census tracts can vary by a factor of