similar to: How set lm() to don't return NA in summary()?

Hi R users, I sent a message yesterday about NA in model estimates ( http://r.789695.n4.nabble.com/How-set-lm-to-don-t-return-NA-in-summary-td3722587.html). If I use aov() instead of lm() I get no NA in model estimates and I use gmodels::estimable() without problems. Ok! Now I'm performing a lot of contrasts and I need correcting for multiplicity. So, I can use multcomp::glht() for this.

Windows 2000 . 5.00.2195 with Service Pack 1. R 1.5.1 Output from my split-split plot aov "alerted" me that I have done something wrong. I designed an experiment with all combinations of all levels of each treatment, but lost a little data (3 out of 192 plots). With the following data, I run the following model: > collim[c(1:6,187:192),c(1,3:6,9)] plot Litter Fert

Dear R sages, I used the function rbind to combine a matrix (M) and a vector (Fert) to get a new matrix (A). This was fine. The issue is however, that the new matrix A has as its row names the name of the vector Fert, even though I set teh new vector A to have dimnames=NULL. See short code below fyi. *Fert<-c(0,1,5) * *M <- matrix(0, 2, 3) diag(M) <- c(0.3,0.5) * *A<-

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

Dear R community, I am trying to fit an nlme model where I want to estimate the fixed effects of two treatments on the parameters on the following equation Photo~(a*(1-exp(-c*PARi/a)))-b I was able to fit a simple model without covariates following the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error "

Hi R-Helpers: I?m trying to use the function GLMM from lme4 package, (R-1.8.1, Windows 98),and I get the following error: > pd5 = GLMM(nplant~sitio+ + fert+ + remo+ + sitio:fert+ + remo:sitio+ + remo:fert+ + remo:fert:sitio + data=datos, + family=binomial, +

Using R v. 1.7.0 on Windows 2000 I would like to plot the fitted values of a model as a function of a continuous covariate, augmented with data (e.g., augPred) grouping by combinations of fixed effects. I have not been able to use augPred effectively, and am wondering if it does not handle unbalanced data (3 out of 192 missing). I include below the model and an xyplot that almost does the

Hello, I am a Masters student and I am working on my thesis modelling smallholder farms using a program in R. I have modified the original code and I am having some issues with lists that I cannot figure out. Originally, I had list file defining lists such as: Param, Crop1, Crop1, Soil, etc. (ex. Param <- list() ). Their subsets were listed as Crop1$CContent for example and there was quite

Hi I have a file saved in R, named agrexp.Rdata, shown below > agrdata fert yield 1 25 84 2 50 80 3 75 90 4 100 154 5 125 148 If I double clicked on this file, the data is displayed without problem. However if I tried to import using: > agrdata<-read.table("agrexp.Rdata") or >

Dear all I'm not sure if I did the right analysis for my specific split splot design. We are studying biomass increase with different CO2 concentrations with four different functional plant groups (e.g. grasses, herbs, broad-leafed trees and conifers). Of each functional plant group we have four species. The design is orthogonal. The design is: Blocks: 2 (climate chambers, called

Hi Rneters, I'm running a GLM model with a full factorial design in blocks and binomial error distribution. I would like to have the F values for this model but I got a message that "using F test with a binomial family is inappropriate in: anova.glm(model, test = "F")". Should I not report F statistics on this kind of analysis? I would appreciate any comment on this.

Hi, I'm trying to fit a logit model to a set of data that was collected under a split plot scheme. The structure of the data is Whole plot factors: Watering Frequency (2 levels: Hi/Lo) and Fertilizer type (3 factors A/B/C) Subplot factor: slope type (2 factors up/down) Response: Proportion of infected leaves(Infected leaves/Total leaves) of the plant (2 plants recorded from each plot)

Colin, If your propose is to create a ternary plot with points and vectors, I think easier do this with graphics based plots instead of trellis based plots. Although, with little work you can do with trellis too. I gave you a reproducible code to put an arrow in a ternary plot. I use the function locator() to extract coordinates. The code is the following

Hi all, I'm adjusting a nonlinear regression model for data that has a categorigal variable present. So, I can use nls() to do this considering the categorical variable, like this #------------------------------------------------------------ da <- expand.grid(tr=gl(2,1,la=c("tr")), x=1:12) da$y <- 10*da$x/(3+da$x)+rnorm(da$x,0,0.1) plot(y~x, da) n0 <-

Hello list, I'm doing a table with scores and I want include colors to represent status of an individual. I'm using sweave <<results=tex>>= and xtable but I can't get a result I want. My attemps are #----------------------------------------------------------------------------- # code R da <- data.frame(id=letters[1:5], score=1:5*2) col <- function(x){

Hello list, I have plots with long strings in main=, ylab= or xlab=. So, in I my script I use break long lines to avoid lines hiden on my monitor and in sweave document pages. I use graphics like this plot(1, main="aaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbb") but I would like a plot result like this plot(1, main="aaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbb") I

Hello R users, I'm analyzing an experiment in a balanced incomplet block design (BIB). The effect of blocks are assumed to be random, so I'm using nlme::lme for this. I'm analysing another more complex experiments and I notice some diferences from doBy::popMeans() compared multcomp::glht() and contrast::contrast(). In my example, glht() and contrast() were equal I suspect popMeans()

Hello, I'm doing some graphics for a paper and a need customize such with filled region above the density curve. My attempts I get something very near what I need, but I don't solve the problem of use semitransparent filled. Below a minimal reproducible code. Someone has any idea? require(lattice) # toy data... dt <- expand.grid(A=1:2, B=1:3, y=1:50) dt$y <- rnorm(nrow(dt), dt$B,

Hello, I want to do with Lattice functions (qqmath, histogram) a figure like this below. n <- 1000 x <- rnorm(n) qqnorm(x); qqline(x) op <- par(fig=c(.02,.5,.5,.98), new=TRUE) hist(x, xlab="", ylab="", main="", axes=FALSE) box() par(op) Is possible? Thanks. Walmes Zeviani. _________________________________________________________________

Hi all, I am using barchart() to plot values above the bars. When using groups argument we get bars grouped arround a given x level. By placing values above this bars we need to know the respective x coordinates. How can I get it? require(lattice) da <- expand.grid(x=1:5, z=1:3, w=1:2) da$y <- rpois(da$x, lambda=23) barchart(y~x|w, groups=z, data=da, horizontal=FALSE,