similar to: two level fraction factorial design: How to input 'defining relation'?

Hello! I have read somewhere (somehow, I can't seem to find it again, it's been a couple of months) that when analyzing factorial block design, the position where you put the block factor is important, even more when there are missing values. I understand that when using anova.lm, the order is sequential, so that if I want to check for a treatment effect, I should put my blocking factor

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 all, Does anybody know if it is possible to build a fractional factorial design in R? That is, suppose that we want do design an experiment with 3 factors with 2, 3 and 3 levels, respectivly. However we want to consider, let's say, only 6 from all possible level combinations. Does R design such experiment? Thanks in advance, Caio [[alternative HTML version deleted]]

Dear all, I studied in tank prey fish behavior. Using the design described below (and R code), I want to test the effects of both habitat and predator (and interaction) on prey fish's vertical distribution, which was recorded (with repeated measures) as a categorical variable. I found that package mlogit might fit to my need but I don't know how to specify my complex design in the

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

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

Dear R Gurus: How do you put together a 2^2 (or even 2^k) factorial problem, please? Since you have 2 levels for A and B, do you put in "A+" and "A-" as factors, please? Thanks, Edna Bell

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 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

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

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

Dear R Help - I am analyzing data from an ecological experiment and am having problems with the ANOVA functions I've tried thus far. The experiment consists of a blocked/split-plot design, with plant biomass as the response. The following is an overview of the treatments applied (nitrogen addition, phosphorus addition, and seeded/not seeded) and at what level (block, main-plot, and sub-plot):

Hi all, I have been trying to reproduce an analysis from Douglas Montgomery?s book on design and analysis of experiments. Table 6.10 of example 6.2 on page 246, gives a table as follows: > NPK <- expand.grid(A=mp,B=mp,C=mp,D=mp) > Rate <- c(45,71,48,65,68,60,80,65,43,100,45,104,75,86,70,96) > filtration <- cbind(NPK,Rate) > filtration A B C D Rate 1 - - - - 45 2

Hi, I'd like to apply a 2^k factorial design with k=10 parameters. Obviously this results in a quite long term for the model equation due to the high number of combinations of parameters. How can I specify the equation for the linear model (lm) without writing all combinations explicitly down by hand? Does a R command exist for this problematic? Thanks for your help in advance, Sven

> On Mar 5, 2018, at 8:52 AM, Ding, Yuan Chun <ycding at coh.org> wrote: > > Hi Bert, > > I am very sorry to bother you again. > > For the following question, as you suggested, I posted it in both Biostars website and stackexchange website, so far no reply. > > I really hope that you can do me a great favor to share your points about how to explain the

Hello, I am planning a study with the main point to evaluate the interaction of two treatments, but for ethical reasons one cell is empty, that with patients receaving no treatment at all Treatment B

This list provides help on R programming (see the posting guide linked below for details on what is/is not considered on topic), and generally avoids discussion of purely statistical issues, which is what your query appears to be. The simple answer is yes, you can fit the model as described, but you clearly need the off topic discussion as to what it does or does not mean. For that, you might try

Hi Bert, I am very sorry to bother you again. For the following question, as you suggested, I posted it in both Biostars website and stackexchange website, so far no reply. I really hope that you can do me a great favor to share your points about how to explain the coefficients for drug A and drug B if run anova model (response variable = drug A + drug B). is it different from running three

Dear R users, I need to analyze data generated from a partial two-by-two factorial design: two levels for drug A (yes, no), two levels for drug B (yes, no); however, data points are available only for three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes drugB. I think we can not investigate interaction between drug A and drug B,

> 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,