similar to: factorial block design with missing data

Hello R users, I am trying to minimize two functions with genoud. It is actually one function with two sets of data, each of them having two unknown variables (called Vcmax and gi) which have the same value in each of the function. They are called f.1 and f.2 in the code below. My objective to minimize the functions in order to get the two variables equal in each of the functions. Furthermore, I

I have a set of data that comprises genome numbers in single eggs from three different parasite clones - 3D7, HB3, and MIX. I can draw a boxplot of the genome numbers for each clonefed but how do I carry out a t test or ANOVA to compare if the means are signifcantly different? (Data is listed below) Many thanks, Georgina Humphreys clonefed genomes HB3 21.3 HB3 23.5 HB3 25.9 3D7 27.2 HB3 28.1

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 R wizards:? I am wrestling with reshape.? I have a long data set that I want to convert into a wide data set, in which rows are firms and columns are years. > summary(rin) firm fyear sim1 Min. :1004.00 Min. :1964.0 Min. : -1.00000 1st Qu.:1010.00 1st Qu.:1979.0 1st Qu.: -0.14334 Median :1016.00 Median :1986.0 Median : 0.00116 Mean

Dear All, I am looking an R version of the "Computer program for sample size and power calculations in the design of multi-arm and factorial clinical trials with survival endpoints". Best regards, Giovanni Parrinello P.S.: in the meantime I am preparing a summary for my preceeding question about time-varying covariates in the Cox model and I thank Frank Harrell, Chuck Cleland,

Dear All, I'm new to the list and relatively new to R (but not so to S-Plus) and I?ve been asked to help study some experimental data. In a factorial experiment (5 factors at 2 levels) the response-variable was measured at various times (0, 2, 5, 7, ..., 30 days after start). There's just one run for each factor combination! I intend to use the nlme library of Pinheiro and Bates in a

Hi R users: I want to know if there is any package that makes the specific computation 2^k factorial designs as in: George E. P. Box, J. Stuart Hunter and William G. Hunter. Statistics for Experimenters. Second Edition. 2005. John Wiley & Sons. They advice about the misuse of the ANOVA table in this kind of designs in page 188. Thank you for your help. Kenneth

Hi Rers, I have a silly question. I don't know how to express the loglikelihood function of 1/(x!) where x=x1,x2,....xn in R. Could anyone give me a hint? Thank you in advance. Chunhao Tu

Dear R friends, I was wondering if there were factorial and modulus functions out there that I've somehow overlooked? -P. -- Peter L. Hurd, Ph.D. phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd fax 512.471-3878 Section of Integrative Biology, University of Texas, Austin TX 78712 USA

The following R commands were written: >help.search("factorial") >data(npk) >npk >coef(npk.aov) In the output of coef command, please explain me the interpretation of coefficients of block1 to block 6 in this 2^3 confounded factorial experiment. Thanks. [[alternative HTML version deleted]]

Sorry for the stupid question, but is there the factorial function in R? I tried to find it using help.search('factorial') but got nothing appropriate. Many thanks, -Serge

Is there a native factorial function in R [my searches have been fruitless], or do I need to use gamma() function? ********************************************************************* Dr Peter Caley CSIRO Entomology GPO Box 1700, Canberra, ACT 2601 Email: peter.caley@csiro.au Ph: +61 (0)2 6246 4076 Fax: +61 (0)2 6246 4000 *********************************************************************

Thanks to Martin, Marc, Dennis, Rashid and Bill for comment. The short answer is there is no factorial function within the base package, so use the result that gamm(x + 1) = x!. This what the factorial() function in the package gregmisc does: factorial <- function (x) gamma(1 + x) Bill V. also writes: "Your best move is usually to use the lgamma() function (for log gamma) and do

Hello all, I´m trying to study a factorial design, but I can´t understand why did Df, Sum Sq and Mean Sq of residuals alter when I Split the interaction? I think that Split the interaction must not alter the residuals. Am I doing something wrong? Could anyone help me? My data and functions I tried are: Y<-c(196,213,183, 192,253,199, 251,331,276,

Dear list, perhpas this question is more suitable for R-dev but since I am not really a developer I post it here first. Apparently the following lines do not create any problem in R: library(combinat) r <- 20; b <- 2; sum( sapply(0:r,function(x) nCm(r,x)^(2*b)) ) > 2^64 while in C I obtain an overflow of data even using unsigned long long and with long double I incurr in precision

Hi All, recently I used Design Expert for some Design Of Experiment work. I was happy with the interface to select which effects I want to see in my experiment, and which not. For example: I can select of course my main effects, but also if I want to see interaction A:B, B:C, A:B:C,but not A:C. This was very interessting as you can end up with fewer runs, especially in cases of 10 factors with

Hi all, I'm working on microarray and currently analyzing the microrarray data using limmaGUI. Loop design has been applied in this experiment. This is a 2X2 factorial experiment where there are control and treatment at 2 different time points, week 6 and 9. The experimental design is almost the same as the limmaGUI work example: Weaver Data set. I would like to look at the effect of

Hi everyone, In the package POT, there is a function that computes the L-moments of a given sample (samlmu). However, to compute those L-moments, one needs to obtain the total number of combinations between two numbers, which, by the way, requires the use of a factorial. See, for example, Hosking (1990 , p. 113). How does the function "samlmu" in the package POT avoids overflows? I

Dear R-users: I am using the package MAANOVA to analyze microarray data and have encountered problems when trying to get interactions. I am a newbie in both, R and maanova, and I do not have good knowledge in statistical methods. I have four effects: Effects Levels Var 2 Ind 2 Trat 2 Time 4 Sample 3 <-- biological replicate Spot 4 <-- technical replicate I had

Dear R Gurus: I vaguely remember reading that if interaction was present in a factorial design, then the main effect results were suspect. However, I was reading a text which now uses the tests for main effects even if interaction is present. Which is correct, please? Thanks, Edna Bell