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Dear list, I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate

Dear all, I have a question regarding the output of the coxph function. What does the 'max possible' exactly mean in the output below? Many thanks. coef exp(coef) se(coef) robust se z Pr(>|z|) smocc_zyban -0.4384 0.6451 0.8667 0.9473 -0.463 0.644 self 1.1857 3.2728 0.1405 0.1443 8.216 2.22e-16 *** smocc_zyban:self

Dear All, I have two factors: GROUP and PATIENT, where PATIENT is nested within GROUP. >levels(example$GROUP) [1] "0" "1" "2" "3" "4" > levels(example$PATIENT) [1] "1" "2" "3" There are three observations at each combination of these factors. However, there are no observations for PATIENT = 3 and GROUP

Hi, I'm running GEE using geepack. I set corstr = "ar1" as below: > m.ar <- geeglm(L ~ O + A, + data = firstgrouptxt, id = id, + family = binomial, corstr = "ar1") > summary(m.ar) Call: geeglm(formula = L ~ O + A, family = binomial, data = firstgrouptxt, id = id, corstr = "ar1") Coefficients:

Dear R-Helpers, I've looked high and low for a function that provides frequencies, proportions and cumulative proportions side-by-side. Below is the table I need. Is there a function that already does it? Thanks, Bob > # Generate some test scores > myValues <- c(70:95) > Score <- ( sample( myValues, size=1000, replace=TRUE) ) > head(Score) [1] 77 71 81 88 83 93 > >

Hi,. I've installed RSPerl on my Linux box. It runs all of the test scripts that I've been able to find. I've learned that I can pass in vectors of data as array-refs, and can use named parameters with R::callWithNames and a hash of names and values. The one problem that I'm left with is how to call a function from an add-on package ('ctest' in this case). I know that

I don't understand which is the problem Could you help me please? Thanks in advance Marta > str(data_indiciN2) 'data.frame': 200 obs. of 36 variables: $ gruppo: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ I001N2: num 19.32 8.22 28.35 7.24 14.7 ... $ I002N2: num 2.92 2.54 0.11 1.6 7.12 ... $ I003N2: num -22.362 -0.222 -19.291

Dear list members, I try to simulate an incomplete block design in which every participants receives 3 out of 4 possible treatment. The outcome in binary. Assigning a binary outcome to the BIB or PBIB dataset of the package SASmixed gives the appropriate output. With the code below, fixed treatment estimates are not given for each of the 4 possible treatments, instead a kind of summary

I have the manual for S+ 6 and I'm trying to use R for the Principle Component Analysis example and I'm getting a few interesting answers... The log is as follows: R : Copyright 2001, The R Development Core Team Version 1.3.0 (2001-06-22) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type `license()' or

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

Dear Thomas, The head of my dataset > head(wsuv) parcel sp time censo treatment species 1 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 2 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 3 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 4 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Dear list, I am currently working with a rather large data set on body temperature regulation in wintering birds. My original model contains quite a few dependent variables, but I do not (of course) wish to keep them all in my final model. I've fitted the following model to the data: >

I have used the greedy.wilks to stepwise discriminant analysis, but it doesn't work with my dataset. I don't understand which is the problem Could you help me please? Thanks in advance Marta > str(data_indiciN2) 'data.frame': 200 obs. of 36 variables: $ gruppo: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ I001N2: num 19.32 8.22 28.35 7.24

On 2019/7/20 0:13, Michael S. Tsirkin wrote: > On Fri, Jul 19, 2019 at 03:31:29PM +0000, ? jiang wrote: >> On 2019/7/19 22:29, Jiang wrote: >>> On 2019/7/19 10:36, Jason Wang wrote: >>>> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: >>>>> On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: >>>>>> On Thu, Jul 18, 2019

On 2019/7/20 0:13, Michael S. Tsirkin wrote: > On Fri, Jul 19, 2019 at 03:31:29PM +0000, ? jiang wrote: >> On 2019/7/19 22:29, Jiang wrote: >>> On 2019/7/19 10:36, Jason Wang wrote: >>>> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: >>>>> On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: >>>>>> On Thu, Jul 18, 2019

On 2019/7/19 22:29, Jiang wrote: > > On 2019/7/19 10:36, Jason Wang wrote: >> >> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: >>> On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: >>>> On Thu, Jul 18, 2019 at 10:01:05PM +0800, Jason Wang wrote: >>>>> On 2019/7/18 ??9:04, Michael S. Tsirkin wrote: >>>>>> On

Hello, So I am afraid I am having a recurring problem that I just can't figure out. I am using the plm package to conduct a panel analysis - although I am not sure if the problem is arising as a result of the plm package or something more general. I am trying to run a fixed effects model with effects over time and individual. The model has various lags, and the problem is that these lags do

On Fri, Jul 19, 2019 at 03:31:29PM +0000, ? jiang wrote: > > On 2019/7/19 22:29, Jiang wrote: > > > > On 2019/7/19 10:36, Jason Wang wrote: > >> > >> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: > >>> On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: > >>>> On Thu, Jul 18, 2019 at 10:01:05PM +0800, Jason Wang

On Tue, Jul 23, 2019 at 12:05:03PM +0000, ? jiang wrote: > > On 2019/7/20 0:13, Michael S. Tsirkin wrote: > > On Fri, Jul 19, 2019 at 03:31:29PM +0000, ? jiang wrote: > >> On 2019/7/19 22:29, Jiang wrote: > >>> On 2019/7/19 10:36, Jason Wang wrote: > >>>> On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: > >>>>> On Thu, Jul 18, 2019