similar to: lmer: mixed effects models: predictors as random slopes but not found in the fixed effects?

Dear list I have a matrix composed of islandID as rows and speciesID as columns. IslandID: Island A, B, C….O (15 islands in total) SpeciesID: D0001, D0002, D0003….D0100 (100 species in total) The cell of the matrix describes presence (1) or absence (0) of the species in an island. Now I would like to search the species with absence (0) in all the islands (Island A to Island O.)

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

Hi I want to extract the random slopes from a lmer (I am doing a random regression), but are the answers obtained from ranef or coef? My model is: mod1<-lmer(B~ A +(A|bird), family=quasibinomial) And I want to obtain a slope for each individual bird but am not sure which output I need and can't find the answer anywhere. Thanks Sam Dr Samantha Patrick EU INTERREG Post Doc Davy 618

I have data from a design in which items are completely nested within subjects. Subject is the only second-level factor, but I have multiple first-level factors (IVs). Say there are 2 such independent variables that I am interested in. What is the proper syntax to fit a mixed-effects model with a by-subject random intercept, and by-subject random slopes for both the 2 IVs? I can

Hello, does any one know how to interpret this output in R? > Classification with logitboost > fit <- logitboost(xlearn, ylearn, xtest, presel=50, mfinal=20) > summarize(fit, ytest) Minimal mcr: 0 achieved after 6 boosting step(s) Fixed mcr: 0 achieved after 20 boosting step(s) What is "mcr" mean? Thanks [[alternative HTML version deleted]]

Hi all; Happy new year. I have got the following error rror in if (nrow(object$splits) > 0) { : argument is of length zero when I am running the following codes. train <- c(sample(1:27,18), sample(28:54, 18), sample(55:81, 8)) a2011.adaboost <- boosting(median_kod ~ ., data = b[train, ], boos=TRUE, mfinal = 10, control = rpart.control(minsplit = 0)) Regards, Greg [[alternative

Hello, I performed a boosting analisis with adabag package to obtain a classification tree with the following set of commands: Tesis.boost <- adaboost.M1(Captura~., data=Tesis2, mfinal=2) > arb<-Tesis.boost$tree[[1]] > post(arb, file ="") > post(arb, file ="",title= "Arbol 1") I would like to know the meanning of the numbers that appeared in the

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

Hi All, I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value < 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? thanks -- Eugenio Larios PhD Student University

Can anyone show me how to test for significant simple slopes of a 3-way interaction, with covariates. my equation tmod<-(glm(PCL~ rank.f + gender.f + MONTHS + CEXPOSE.M + bf.m + MONTHS*CEXPOSE.M*bf.m, data=mhatv, family=gaussian ,na.action=na.omit)) Thank you Mike [[alternative HTML version deleted]]

Hi I have (8 measures * 96 groups) = 768 datasets for which I did linear regressions using lm(). Now I want to compare the slopes for each of the 8 measures in each of the 96 groups. As I understand , I can not use > anova(lm1, ..., lm8) as the lm1 ... lm8 are based on different datasets. I also read in previous discussions in this list, that I can see if the slope +- stddev(slope)

Hi, all, I am thinking to compare a group of slopes from regression lines to see if they are different overall, and then make specific comparisons between groups. How can I achieve that in R? I searched the archives and there are only discussions about comparing two lines a time. Thanks. A sample data set is like the following. I would like to compare the regression slopes between the five

Hi all: I perform the linear mixed model for 300 persons, y is CD4 count,x is time. I randomized slope and intercept,so I can get 300 slopes and 300 intercepts.Now I wanna test wheter the variance of 300 slopes and 300 intercepts differs from zero. If the variance of 300 slopes(or intercepts) differs from zero at 0.05 significant level,I should randomize the slope(or intercept), and if not,I

Dear all I have the following data (a shortened extract shown; some replictates of time deleted) to which I fitted the linear model given below: time group mass 11 control 0.019 11 control 0.014 14 control 0.0306 14 control 0.0289 14 control 0.0236 17 control 0.0469 17 control 0.0709 11 five 0.0077 11 five

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Dear all, I am trying to estimate the prediction from a fixed effects model and their confidence intervals as well. Though I do not want to include in the prediction and at the confidence intervals the intercept. For that reason I used the argument incl.non.slopes=FALSE. But either if it is TRUE or FALSE it does not have any difference and also the system does not provide any warning. I really

Hi everyone, I have a data frame (d), wich has the results of mosquitoes trapping in three different places. I suspect that one of these places (Local=='Palm') is biased by low numbers and will yield slower slopes in the variance-mean regression over the areas. I wonder if these slopes are diferents. I've looked trought the support list for methods for comparing slopes and found the

Hello Instead of testing against 0 i would like to test regression slopes against -1. Any idea if there's an R script (package?) available. Thanks for any hint. Cheers Lukas ??? Lukas Indermaur, PhD student eawag / Swiss Federal Institute of Aquatic Science and Technology ECO - Department of Aquatic Ecology ?berlandstrasse 133 CH-8600 D?bendorf Switzerland Phone: +41 (0) 71 220

Hi, I read almost all I found in prior R-Help list about How to compare two regression line slopes. So, I made a didactic example to illustrate a solution cited by Ben Bolker: =============================================== Subject: Re: [R] How to compare two regression line slopes From: Ben Bolker (bol... at ufl.edu) Date: Jan 27, 2009 1:52:20 pm List: org.r-project.r-help

Hi everyone, I'm a bit new here (and new to R), and I was trying to do an OLR, and testing the parallel slope assumption seems be very important. I browsed through past postings, and didn't find much to help me in this area. I was wondering if anyone knew how I could go about doing this. Thank you. -- View this message in context: