similar to: F values for glm with binomial distribution

Hi, I would like to print elements from sem (structural equation modeling) function (e.g., model-reproduced covariance matrix (C); estimated asymptotic covariance matrix of parameter estimates (cov)). How can I do this? Thanks, Andr?? -- Andr?? Tavares Corr??a Dias Laborat??rio de Ecologia Vegetal Universidade Federal do Rio de Janeiro CCS-IB-Departamento de Ecologia Caixa Postal 68020

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

Hi, I've data from an incomplete fatorial design. One level of a factor doesn't has the levels of the other. When I use lm(), the summary() return NA for that non estimable parameters. Ok, I understant it. But I use contrast::contrast(), gmodels::estimable(), multcomp::glht() and all these fail when model has NA estimates. This is becouse vcov() and coef() has different dimensions. Is

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 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 (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 "

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<-

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)

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, +

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little

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 R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

Dear R community, I am not experienced in writing functions and need your help to understand the strategy to face the following problem: I have a group of independent numerical variables, let's say a<-c(1,3,2,6,9,2,2,3,4,1) b<-c(2,3,3,4,5,6,2,1,1,6) c<-c(0,2,4,4,7,6,7,1,2,2) d<-c(0,0,0,1,0,2,1,3,1,2) e<-c(9,2,3,1,1,1,0,2,5,6) and a grouping variable,

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728