similar to: coercion of LHS to a list

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

Hi I'm sure I'm missing something very straighforward here :-( I have a data set 'sales' as follows: ========================================== # read in the sales data sales<-read.table("sales.dat",header=TRUE); #generate a serial field sales$serial=c(1:24) sales an l ml ne ni total serial 1 43 25 35 51 17 69 1 2 38 18 47 94 3 99 2 ....... 24 58

Two new packages have been added to CRAN. The first is a simple package which provides the standard functions for a triangle distribution (rtriangle, dtriangle, ptriangle, qtriangle). The second package generates and augments Latin Hypercube Samples. Functions for generating random and optimized designs are provided. I look forward hearing your feedback and improving the documentation!

Two new packages have been added to CRAN. The first is a simple package which provides the standard functions for a triangle distribution (rtriangle, dtriangle, ptriangle, qtriangle). The second package generates and augments Latin Hypercube Samples. Functions for generating random and optimized designs are provided. I look forward hearing your feedback and improving the documentation!

Hi All, I am a new member in the group, have been doing some experiments with LLVM IR. I am facing issue with de-compiling the following instruction once it is read, %var2 = call i8 @myfuncCall (i2 %var1) In after I parse the file (lets say using parseIRFiles ). I can see the instructions,( the function call instruction ), How can I get the %var2 from the instruction/callInstruction ? going

Hi Abhinav, > If i have an IR instruction of the form > %tmp10 = call sbyte* %malloc( uint 4 ) ; <sbyte*> [#uses=1] > %tmp10 = cast sbyte* %tmp10 to int* ; <int*> [#uses=1] > store int* %tmp10, int** %t > > which is nothin but a malloc call how can i get %tmp into maybe a variable > set. > > If i have a store instruction then it is

Full_Name: Douglas Bates Version: Version 0.64.0 Unstable (April 3, 1999) OS: Debian GNU/Linux 2.1 Submission from: (NULL) (128.105.5.97) R> fm2 <- lm( 1/Surv ~ Poison + Antidote + Poison:Antidote, data = BC ) R> anova( fm2 ) Analysis of Variance Table Response: / Response: 1 Response: Surv Df Sum Sq Mean Sq F value Pr(>F) Poison 2 34.9 17.4

Hi all! I'm trying to build a simple class, but I'm getting a weird error. E.g. if I do: ========== gregion = setClass("gregion", contains = "data.frame", representation(name = "character", poly.x = "numeric", poly.y = "numeric" )) gregion$name = "North America" ========== Then I get this message: Warning message: In

hello all, If i have an IR instruction of the form %tmp10 = call sbyte* %malloc( uint 4 ) ; <sbyte*> [#uses=1] %tmp10 = cast sbyte* %tmp10 to int* ; <int*> [#uses=1] store int* %tmp10, int** %t which is nothin but a malloc call how can i get %tmp into maybe a variable set. If i have a store instruction then it is pretty much simpler as the getOperand(1) can give

The model frame shows the response and predictors in a data frame with nicely labelled columns: fm <- lm(wt~qsec+log(hp)+sqrt(disp), data=mtcars) model.frame(fm) # ok When the left hand side consists of more than one response, those response variables still look good, inside a matrix: fm <- lm(cbind(qsec,hp,disp)~wt, data=mtcars) model.frame(fm)[[1]] # ok A problem arises when

On 7/23/07, abhi232 at cc.gatech.edu <abhi232 at cc.gatech.edu> wrote: > Hi Bill, > Thanks a lot for your response.But my problem still remains.The thing is i > am having a data type std::vector<Value*> as i am checking for the > variables in Store Instructions and Malloc Instructions.For store the case > is straightforward as discussed earlier.I want the same Value*

Hi Bill, Thanks a lot for your response.But my problem still remains.The thing is i am having a data type std::vector<Value*> as i am checking for the variables in Store Instructions and Malloc Instructions.For store the case is straightforward as discussed earlier.I want the same Value* variable for malloc inst as well. bcos i cannot have a different set for only instructions. can there be

Dear R Users, I did not get any reply on my question so I am re-asking. This time I am giving sample data: 1 60.3162 -13.5993 -0.4353 46.0938 0.1877 -0.194E-07 2 60.3713 -13.5992 -0.4423 46.1241 0.2057 -0.231E-06 3 60.3430 -13.5981 -1.6163 44.9048 0.2237 -0.270E-06 4 60.3227 -13.5970 -2.6258 43.8785 0.2213

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

Hi to all, I am working on design package using survival function. First using PSM and adopting a weibull specification for the baseline hazard , I have got the following results(since weibull has both PH and AFT propreties ,in addition I have used the PPHSm command): Value Std. Error z p (Intercept) 1.768 1.0007 1.77 7.73e-02 SIZE -0.707 0.0895 -7.90 2.80e-15

Hello, I?m using zero-inflated Poisson regression via the zeroinfl() function to analyze data on seed-set of plants, but for some reason, I don?t seem to be getting the output for all three levels of my two categorical predictors. More about my data and model: My response variable is the number of viable seeds (AVInt), and my two categorical predictors are elevation (Elev) and Treatment

Dear R Community, Below is a problem with a simple ggplot2 graph. The code returns the error message below. Error: stat_count() must not be used with a y aesthetic. My code is below and the data is attached as a ?text? file. # Graph of the probabilities library(digest) library(DT) datatable(probability) str(probability) probability$Fertilizer <- as.factor(probability$Fertilizer)

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

For some reason, half of the patches I posted on Friday for HVM SMBIOS support haven''t shown up yet. I''m going to try to post a version of them reworked for changeset 11076:f328519053f5a444af475ec10dc8089a0b176e3f in a few moments, so please ignore the previous posts. Thanks! Andrew _______________________________________________ Xen-devel mailing list

Greetings. I''ve been asked to work on a PXE solution for booting Xen domU''s from a network, and would like some feedback and any help anyone is willing to provide. I''ve been limiting my work to HVM domU''s at the moment, because I don''t think the NBI''s (as far as I know, these are bootloaders) that I have available work in paravirtualized