similar to: mgcv: Plotting probabilities for binomial GAM with crossed random intercepts and factor by variable

Hello I have a table of this kind: function x1 x2 x3 2.232 1 1 1.00 2.242 1 1 1.01 2.732 1 1 1.02 2.770 1 2 1.00 1.932 1 2 1.01 2.132 1 2 1.02 3.222 1.2 1 1 ..... ... .. .. The table represents the values of a function(x1, x2, x3) for each combination x1, x2, x3. I'd like to generate a plot where each point has the coordinates x=x1, y=x2,

Hi R-masters I read the article: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. In this paper i proposed a bivariate mixed model and use SAS proc mixed to adjust the estimates. I thinks use R to make the same and try with this code: base<-read.csv("base.csv") adj<-.5 attach(base) sens<-(VP+adj)/(VP+FN+2*adj)

Concerns: R 1.5.0 gui version, Windows (downloaded binary) and Linux (installed from sources). # Load the data from the attached file: kk<-read.table("__filename__", header=1) # attach the data: attach(kk) Snr<-factor(Snr) # fool around with a call to anova.glm(): anova.glm( aov( nFD~Type+size+Modality+Error(Snr/(Type+size+Modality)) ) ) # Error: object nFD not found # Well, I

Hello, I think this might be a beginner question, but I couldn't find the answer in the manual... http://www.nabble.com/file/p14618947/at-modality.png I created this image with R by using the following code: modality <- read.table("results.table", header=TRUE, sep=",") color <-

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

Hi, This question is related to the bwplot issue I reported yesterday. I have a 3 factors (2x3x2) dataset that I collapsed into a 2 factors dataset (3x2 = sizexModality). For size==small, I have 2 observations per subject (Snr), for the other sizes only 1. I reckoned that aov (and underneath, lm) might handle this as it should, since the subjects are idendified, when I do > aov(

http://www.nabble.com/file/p14664173/at-modality.png I created the above image with R and I have one problem left: Some of the labels of the axes do not show up, probably because there's not enough space. I use the following code to create the plot: modality <- read.table("results.table", header=TRUE, sep=",") color <-

Dear advanced statisticians, *******Objectif******** I try to set up linear models with mean as intercept: Answer: y Variable: x, as factor of two modalities: x(1), x(2). I would like to have a model as: y = mean(y)+A(i)+residuals, with i in (1,2) and A(1) coefficient for x(1) and A(2) coefficient for x(2). *******Trials in R******* ## Firstly: I write in R: >Model<-lm(y~x,Data)

Hi all Well, first: happy new year... Second: I write a function in R that might interest some other people. On the other hand, I am closer to beginners than experts; I don't know how valuable my code is. I don't know how long it will take to me to create a library and I don't know if it's worth to. So before starting this long process, I would like some advices, both on the

Dear Sirs I can't try the package sets because I have a old mac system. Package sets needs R = 2.6. I need to group some variables into a set of new variables defined by a particular modality: Example: my.df = data.frame(var1=c(1,2,2,1,2,2,1,2), var2=c(2,2,1,1,2,2,1,1), var3=c(1,2,2,1,1,1,1,2), var4=c(1,1,1,1,2,1,2,1)) I'm looking for a way to compute set1 and set2? set1 = c(5,4) %

Hi, I have an experimental design with 3 factors (Type, size and Modality), where the stimulus for Type==B, size==small was identical to that for Type==A, size==small. Since I am in the end only interested in Type==A, I "relabeled" all Type==B,size==small responses as Type==A,size==small. This gives me an unbalanced design, with a certain number of missing combinations, and others that

Dear R friends A while a go I sent an email to the epi-list and later to the help-list and no answer could fully illuminate my question. So Im trying again with a more specific matter. Im trying to work on a script (function) to analyse data from a diagnostic test meta-analysis with random effects. This was first described by an author using SAS witn PROC NLMIXED. Im not an expert in R and much

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

Hi, I have obtained some results with factanal that seem to support a hypothesis I already had, and I'd like to verify that I can indeed conclude this from this new analysis. We had subjects reproduce perceived trajectories with a device that allowed us to measure spatial position (the path) and the device's orientation at any of those positions. From this, we calculated the rotation of

R provides a few ways of handling missing values, a.o. in the context of an anova (aov); 2 types of exclusion, and failure. In some situations, I personally like to have missing values replaced by the mean (or the median) for the given combination of factors. A routine that does that is something like the code included below. It works, but is (of course) rather slow. It would be much quicker

Since the fact that gfortran performance has improved over the major releases, I decided to benchmark the current releases on a MacPro with the Polyhedron 2005 benchmarks using -ffast-math -funroll-loops -msse3 -O3. The results are... gcc release gcc 4.2.4 gcc 4.3.3 gcc 4.4-pre gcc 4.3.3/ gcc 4.4-pre/

Hello. I would like to access the LLVM IR Instruction from which an SDNode (from SelectionDAG) originates. For this I have modified: - llvm/lib/CodeGen/SelectionDAGISel.cpp, SelectionDAGISel::SelectBasicBlock(), namely I put SDB->clear() at the beginning of the method in order to avoid clearing NodeMap after creating the initial SelectionDAG from LLVM IR, since I want to access

# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor

Dear R users, I've written the following functions to implement Silverman's modality test ("Using kernel density estimates to investigate multimodality", J.R. Stat. Soc. B, 43, (1981), 97-99.), and I tested them on the Chondrite data set (Good & Gaskins, J. Amer. Stat. Ass., 75, (1980), 42-56). Values for the critical window width seem OK, which is not the case for the