similar to: Linear Models with mean as Intercept.

Hello and sorry to disturb. I'm trying to plot the confidence intervals for the fixed effects of a lme. I want to obtain graphically, if it is possible, a bar with Estimate, upper and lower CI for each level of the factors. I know how to do for a lm model but for a lme one, I tried with plot(intervals(...)) and plot(ci(...)) from the gmodels package but it doesn't work well. Thanks

Good afternoon, I REALLY try to answer to my question as an autonomous student searching in the huge pile of papers on my desk and on the Internet but I can't find out the solution. Would you mind giving me some help? Please. ######################################### I'm trying to use glm with factors: > Pyr.1.glm<-glm(Pyrale~Trait,DataRav,family=poisson) If I have correctly

Dear r-list, I've got a data base: > HData[1:10,] NumTree Site Species Date Age DBH H IdentTree 1 1 Queige Spruce 2002 184 49 33.5 Queige 1 2 2 Queige Fir 2002 NA 5 4.6 Queige 2 3 3 Queige Fir 2002 25 8 6.6 Queige 3 4 4 Queige Spruce 2002 198 47 32.5 Queige 4 5 5 Queige Fir 2002 200 59 35.3 Queige 5 6 6 Queige

Good morning, I used in R contr.sum for the contrast in a lme model: > options(contrasts=c("contr.sum","contr.poly")) > Septo5.lme<-lme(Septo~Variete+DateSemi,Data4.Iso,random=~1|LieuDit) > intervals(Septo5.lme)$fixed lower est. upper (Intercept) 17.0644033 23.106110 29.147816 Variete1 9.5819873 17.335324 25.088661 Variete2 -3.3794907 6.816101 17.011692 Variete3

Dear statisticians, I would like to save results for a "for loop" in a vector previously created. My result would be of class "matrix". I tried the following script: Script: #Creation of a previous vector n.Tree<-2 VectorX<-rep(1,n.Tree) #loop for (i in 1:2) { Ti<-MatOccurTree[Tree[i],1] #number of observation for Tree i Xi<-matrix(data=1,nrow=Ti,ncol=2)

Dear R users, I am looking for an easy (i.e., direct) way of obtaining the F and p values from the intercept in anovas with within-subject designs. My data are from a psychophysics experiment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject. I would like to know not only whether there is an effect of modality, but also whether the main effect is

Bonjour, Je cherche à installer le package odesolve du logiciel de statistique R sous Ubuntu Linux. C'est un package qui contient des fonctions appelant du code en Fortran. A l'installation sous R via le shell, j'obtiens l'erreur suivante: Hi, I tried to install odesolve package of R under Ubuntu Linux. But I got the following error: ghislain@ghislain-laptop:~$ sudo R [sudo]

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

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 <-

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 <-

See if this does what you want: > dat <- data.frame(f=factor(sample(letters[1:10], 100, replace=TRUE)), x=runif(100)) > str(dat) `data.frame': 100 obs. of 2 variables: $ f: Factor w/ 10 levels "a","b","c","d",..: 2 5 10 9 10 3 9 8 3 1 ... $ x: num 0.9162 0.0481 0.3048 0.0938 0.8599 ... > g <- names(which(table(dat$f) > 11)) >

Dear R users,I am looking for an easy (i.e., direct) way in obtaining the F and p values from the intercept in anovas with within-subject designs. Data are from a psychophysics expeirment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject.I would like to know not only whether there is an effect of modality, but also wether the main effect is

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

> On Jun 23, 2017, at 5:53 AM, Jay Zola <jayjay.1988 at hotmail.nl> wrote: > > Dear sir/madame, > > > I am currently writing a meta-analysis on the complication and reoperation rates of 5 different treatment modalities after a distal radius fracture. I was able to pool the rates of the 5 different rates using R. Now I have to compare the pooled rates of the 4 treatment

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

Hello. Quentin, retrieving the LLVM IR instruction from which an SDNode originates is useful during the instruction selection phase. For example, I need to recover the LLVM IR variable which is used to fill an entire vector with the ISD::BUILD_VECTOR target-independent SDNode . From the recovered LLVM IR variable I can walk on the use-def-chains in order to get the most complete

mgcv: Constructing probabilities for binomial GAM with crossed random intercepts and factor by variable Hello, (I'm sorry if this has been discussed elsewhere; I may not have been looking in the right places.) I ran a binomial GAM in which "Correct" is modelled in terms of the participant's age and the modality in which the stimulus is presented (written vs spoken).

Dear sir/madame, I am currently writing a meta-analysis on the complication and reoperation rates of 5 different treatment modalities after a distal radius fracture. I was able to pool the rates of the 5 different rates using R. Now I have to compare the pooled rates of the 4 treatment modalities with the golden standard separately. I though the chi squared test would be the best method. How do

Hi, I have two different imaging modalities (for the identification of areas of infarcted myocardium) that I need to compare regarding agreement and consistency. However, I don't think that methods like Cohen's Kappa, PCC, Bland-Altmann or ICC are sufficient here as there is not just a pairwise but also a spatial relationship between measured data points. For example if the results of the