similar to: Nested Designs

Olá pessoal, estou realizando uma ANOVA com medidas repetidas e estou utilizando a função "Anova" do pacote "car". Medi o biovolume de algas a cada dois dias durante 10 dias (no banco de dados abaixo só coloquei até o 4° dia). Tenho 2 tratamentos ("c","t") e o experimento foi realizado em tréplicas ("A","B","C"). > Pa2

The pretty picture that I saw at: http://chartsgraphs.wordpress.com/2009/02/09/r-panel-chart-beats-excel-chart/#more-1096 inspired me to try something similar. The code that I wrote is: ------snipsnip--------------------------------------------------------------------- M <- structure(list(date = structure(c(13634, 13665, 13695, 13726, 13757, 13787, 13818, 13848, 13879, 13910, 13939, 13970,

Hi, I'm looking for a most efficient way to call an R function from C++ in a package. I know there are two functions (`R_forceAndCall` and `Rf_eval`) that can do the "call" part, but both are slow compared to calling the same function in R. I also try to use Rcpp and it is the worse one. Here is my test code: C++ code: ``` // [[Rcpp::export]] SEXP C_test1(SEXP f, SEXP x) { SEXP

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Dear R help, I have some readings in three dimensions (x, y, z) and an amplitude for each. I'd like to visualize the data using ggplot, using tile plots, as I have some additional point data I would like to eventually overlay on the tile plots. I would like to subset the data by sections, slices if you will, in the z dimension, and plot the data for that slice. I can do all of this, but am

Dear R community, The next release of package ff is available on CRAN. With kind help of Brian Ripley it now supports the Win64 and Sun versions of R. It has three major functional enhancements: a) new fast in-memory sorting and ordering functions (single-threaded) b) ff now supports on-disk sorting and ordering of ff vectors and ffdf dataframes c) ff integer vectors now can be used as

Dear R community, The next release of package ff is available on CRAN. With kind help of Brian Ripley it now supports the Win64 and Sun versions of R. It has three major functional enhancements: a) new fast in-memory sorting and ordering functions (single-threaded) b) ff now supports on-disk sorting and ordering of ff vectors and ffdf dataframes c) ff integer vectors now can be used as

I have been tweaking and researching for a while now and can't seem to get "good" performance out of Gluster. I'm using Gluster to replace an NFS server (c1.xlarge) that serves files to an array of web servers, all in EC2. In my tests Gluster is significantly slower than NFS on average. I'm using a distributed replicated volume on two (m1.large) bricks: Volume Name: ebs

Are folks in the Centos community succesfully using device-mapper-multipath? I am looking to deploy it for error handling on our iSCSI setup but there seems to be little traffic about this package on the Centos forums, as far as I can tell, and there seems to be a number of small issues based on my reading the dm-multipath developer lists and related resources. -geoff Geoff Galitz Blankenheim

The mle2 function (bbmle library) gives an example something like the following in its help page. How do I access the coefficients, standard errors, etc in the summary of "a"? > x <- 0:10 > y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) > LL <- function(ymax=15, xhalf=6) + -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) > a <- mle2(LL,

Hello, the only thing I found to fit a sin/cos is s.th like lm(a~cos+sin) But this is not what I want. I have a magnitude which is sinusoidal with offset and it doesn't start at phi=0. The data is: angle<-c(0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180) Voltage<-c(-45.07, -45.24, -43.61, -40.78, -36.48, -30.92, -23.83, -15.95, -7.53, 1.06, 11.24,19.85, 27.00,

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