similar to: Anova and unbalanced designs

Dear List, I am trying to get R's terminal output to a file and to the terminal at the same time, so that I can walk through some tests and keep a log concurrently. The function 'sink' with the option split=TRUE seems to do just that. It works fine for most output but for objects of class htest, the terminal output is incomplete (the lines are there but empty). Here is an

Hello, I am trying to get a linear model of y ~ log(x). *> lm (y~log(x))* However, I always get an error report: /Error in model.frame.default(formula = y ~ log(x), drop.unused.levels = TRUE) : variable lengths differ (found for 'log(x)')/ *Here was my y:* > y [1] 0.4500000 0.0500000 0.5000000 0.4000000 0.0000000 0.5000000 0.4000000 [8] 0.0500000

Hi all, I am a bit mystified by this error message that I get when I try to apply lmer() to a simple dataset with one between factor (age) and one within factor (item): "$ operator is invalid for atomic vectors" I'll just provide the code, because I don't see where the problem is: library(lme4) options(contrasts=c("contr.helmert","contr.poly")) data =

Dear Mailing-List, I think this is a newbie question. However, i would like to integrate a loop in the function below. So that the script calculates for each variable within the dataframe df1 the connecting data in df2. Actually it takes only the first row. I hope that's clear. My goal is to apply the function for each data in df1. Many thanks in advance. An example is as follows: df1

Dear R users I wonder if there is a quick way to calculate the ratio/fraction of a list/data frame. For example, if I have a data frame with two fields: "Index" and "A". I would like to know the fractions of A's within the same "Index". That is, for Index =1, three fractions will be "1/(1+2+3)=0.17", "2/(1+2+3)=0.33", and

Hi, I sometimes play around with extreme parameters for distributions and found that qbeta is not always monotone as the following example shows. I don't know whether this is serious enough to submit a bug report (as this example is near to the limitations of floating point arithmetic). Josef > x <- qbeta((0:100)/100,0.01,5) > x [1] 0.000000e+00 1.253990e-201 1.589622e-171

Dear all, I am new to R and to it's programming philosophy. The following function is supposed to work on a vector, but I can't figure out how to do that without looping through every element of it. Is there a more elegant way? Note: I have shortened it, so it is NOT correct from the pipe hydraulics point of view # Calculate wall friction factor # reynolds: Reynolds number # dk:

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 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

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Hello R users, I am looking for R (or S) code related to group sequential or adaptive designs for clinical trials. (The most prominent examples are the designs of Pocock or O'Brien/Fleming, the alpha-spending function approach, or Fisher's combination test and the inverse normal method.) I am particularly interested in the calculation of the critical boundaries, the handling of spending

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

I am wondering if in a block experimental design (ex. triple square lattice), the block effect was not significant, is it all right not to include the block effect in an empirical model (even though the sampling was done from different blocks)? Or we are forced to control for the block effect in block designs anyway? Thanks, Julia

Hi, I want to do ANOVA for nested designs like following. I don't understand why the matrix (t(X) %*% X) is singular. Can somebody help me understand it? Regards, Peng > a=2 > b=3 > n=4 > A = as.vector(sapply(1:a,function(x){rep(x,b*n)})) > B = as.vector(sapply(1:(a*b), function(x){rep(x,n)})) > cbind(A,B) A B [1,] 1 1 [2,] 1 1 [3,] 1 1 [4,] 1 1 [5,] 1 2 [6,] 1 2 [7,]

Hi, I have a dataset from a client where the data is from a cross-over design. Basically, each subject in a survey was asked to rate two products, A and B. The subject sampled A first and then after an appropriate wash-out period he/she sampled B. The next subject did the same, but in a different order. How can I do an ANOVA analysis on a cross-over design with only two treatments. This

Dear Colleagues, John Mandel ( Chain block designs with two-way elimination of heterogeneity. Biometrics 10, 251-272 ,1954). extended the class of chain block designs (Youden & Conner (1953) to elimination of both row and column (blocks) effects. These experimental designs can be useful in engineering and other fields. I am having difficulty obtaining his adjusted treatment means in his

Hello, svyvar from the survey package computes variances (with standard errors) from survey design objects. Is there any way to compute standard deviations and their standard errors in a similar manner? Thanks a lot, Sebastian

Dear Colleagues, Could you recommend a package of combination of functions in R for analysis of 2x2 tables of various designs. Preferably it should include tests and confidence limits (both exact and approximate) for alternative designs, such as independent proportions (e.g. parallel group clinical trials) and correlated proportions (matched pairs and crossover studies). Many thanks MM

Dear R list, What is the proper way to specify a nested model so that the F values agree with the expected mean square errors? Specifically, suppose I have a design where "Heads" are nested within "Machines". I would like to model the following Y_ijk = Mu + Machine_i +Head_j(i) +Error_k(ij). Using the commands below, > summary(aov(Strain~Machine + Head%in%Machine ))