similar to: Computation for specific 2^k factorial designs

Hi R users: How can I obtain the same "anova" table for the effects for a 2^k experiment design that MINITAB shows (and authors recommends Box, Hunter, & Hunter). http://www.stat.psu.edu/online/development/stat503/06_2k/04_2k_unreplicate.html Here is the code that I use for this case: D<-C<-B<-A<-c("-","+") design<-expand.grid(A=A,B=B,C=C,D=D)

Hi R HELP, I consider the 2^3 factorial experiment described at page 177 of the book Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter (BHH2). This example use the following data in file BHH2-Data/tab0502.dat at ftp://ftp.wiley.com/ in /sci_tech_med/statistics_experimenters/BHH2-Data.zip run T C K y 1 1 -1 -1 -1 60 2

Does anyone know of a way of dealing with unbalanced mixed effects (fixed and random factors) for fully factorial designs. An example of such data is given below; The response variable is SQRTRECRUITS SEASON is a random factor DENSITY is a fixed factor Thus DENSITY:SEASON is a fixed factor. Therefore, whereas the effects of SEASON and DENSITY:SEASON should be tested against the overall

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

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

Yesterday I had the opportunity to attend a seminar by George Box where he discussed some of the ideas that will be incorporated in the second edition of Box, Hunter, and Hunter "Statistics for Experimenters" due out in a few months. At the end of the presentation he distributed a list of quotes from the book and I felt that many of these would be appealing to members of this

Hi R users: I will like to know if somebody works on insurance statistics (actuarial problems) and had use TRICAST, and can tell me if with all the R tools it can be build a solution like TRICAST or similar. In a word: Do you think that R has all the statistical tools (I mean modeling tools) to make a job similar to TRICAST? Does TRICAST has modeling tools that are not implemented on R yet?

Dear all, I would like to run in R the anova showed in the following pamphlet. http://www.for.gov.bc.ca/hre/biopamph/pamp14.pdf For A = 0 and B =0 I have de control group. Best regards. A B replication response 0 0 1 24 0 0 2 27 0 0 3 36 0 0 4 28 0

Hi Everyone, I found the doe.base package and the FrF2 package to do nice experimental planning and I'm very happy about this tool I was looking for such a long time. But I still try to find out how to add center points to a full factorial design. The FrF2-package has a center point option but is just supporting fractional designs? Is there a possibility to have center points within the

Dear all, in R, how can I run a Factorial designs with a separate control? Best regards. #A = 0 and B = 0 == then control A B rep response 0 0 1 24 0 0 2 27 0 0 3 36 0 0 4 28 0 0 5 32 1 1 1

Is names(model1$coef) what you're looking for? -----Original Message----- From: Kenneth Cabrera [mailto:krcabrer at epm.net.co] Sent: 29 November 2002 10:36 Cc: R-help at stat.math.ethz.ch Subject: [R] Obtaining the variable names of a glm object Hi, R users! Suppose I make a model like this:

Hello All: I am trying to replicate the results of an example found in Alan Agresti's "Categorical Data Analysis" on pages 267-269. The example is one of a 2 x 2 cross-classification table of ordinal counts: job satisfaction and income. I am able to get Agresti's results for the independence model (G^2 = 12.03 with df = 9) assuming as he does that the data is nominal, but

Hi R users: I will like to have a legend with math symbols and also with the value of a variable. But I cannot obtain both at the same time (symbol + value of a variable): Here is a reproducible example: m1<-5 m2<-12 plot(1:5,1:5,type="n") legend("topleft",legend=c(paste(expression(mu),"=",m1),expression(paste(mu,"=",m2))),lty=1:2) Thank you for

Hi R users: I will like to have a legend with math symbols and also with the value of a variable. But I cannot obtain both at the same time (symbol + value of a variable): Here is a reproducible example: m1<-5 m2<-12 plot(1:5,1:5,type="n") legend("topleft",legend=c(paste(expression(mu),"=",m1),expression(paste(mu,"=",m2))),lty=1:2) Thank you for

I am still a little unclear in the difference between randomized block design and two-way factorial design after consulting a few books, including John Rice Mathematical Statistics and Data Analysis. Both put observations in cells corresponding to two factors of many levels. Both use the same computer program to analyze data. It seems that randomized block design can have only one observation

Hi, i need to create a fractional factorial design sufficient to estimate the main effects. The factors may have any number of levels, let's say any number from 2 to 6. I've tried to use the library conf.design , but i cannot figure out how to write the code. For example, what is the code for a design with 5 factors (2x3x3x5x2) and only main effects not confounded? thanks in advance!

Hi R users: I got an error that "l1" is not found. This is a sample code: f1<-function(i,l1){ print(l1[[i]]) l1[[i+1]]<<-i return(i) } z1<-list(x=100,NULL,a=c(1,1)) lapply(1:3,f1,l1=z1) My final goal is to update the value of each list component to use it in the following step. I see that print function founds l1[[1]], but the assign instruction not. Thank you

> On Mar 5, 2018, at 2:27 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > David: > > I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected: >> three groups, no drugA/no drugB, yes drugA/no drugB,

Hi R users: I got this code to generate a graphic for each member of a lists. list1<-list(A=data.frame(x=c(1,2),y=c(5,6)),B=data.frame(x=c(8,9),y=c(12,6))) names1<-names(list1) sapply(1:length(list1),function(i) with(list1[[i]],plot(x,y,type="l",main=paste("Graphic of",names1[i])))) Is there a more elegant solution for not to use two separate lists? I would like to

I've looked through the documentation with R-0.64.1 and have been unable to find a high-level function for evaluation of factorials (i.e., n!, not factorial designs). Is there such a function? It is trivial to code, so everyone could write their own, but it also would be worthwhile as a standard tool. I'm guessing I am just overlooking it.