similar to: significance of random effects in poisson lmer

(To the list moderator: I just subscribed to the list. Apologies for not having done so longer before trying to post.) Hi all, I am currently using lmer to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other

I have created a list of tables with the same columns but different number of row. Example (actual list has ~200 elements): > temp1<- data.frame(ID=c("Herb","Shrub"),stat=c(4,5),pvalue=c(.03,.04)) > temp2<- data.frame(ID=c("Herb","Shrub", > "Tree"),stat=c(12,15,13),pvalue=c(.2,0.4,.3)) > L<-list(a=temp1,b=temp2) > L $a

I have a function (see below). This function has one object, ID. If I run the loops by themselves using a character value (ie,"VFFF1-7") instead of the function object, then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

I have a function (see below). This function has one object, ID. If I run the loops by itself using a character value (ie,"VFFF1-7"), then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop criteria. Any obvious problems that

Dear all, I am currently analyzing count data from a hierarchical design, and I?ve tried to follow the suggestions for a correct estimation of p-values as discusssed at R-Wiki (http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests&s=lme%20and%20aov). However, I have the problem that my model only consists of parameters with just 1 d.f. (intercepts, slopes), so that the

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Hi Helpers: I have a factor object that has 314k entries of 39 land cover types. (This object can be coerced to characters neatly should that be easier to work with.) > length(foo) [1] 314482 > foo[1:10] [1] Montane Chaparral Barren Red Fir Red Fir [5] Red Fir Red Fir Red Fir Red Fir [9] Red Fir Red Fir 39 Levels:

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, using Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below is

Dear All I have a problem with the height of the boxes in the key in the following. (The text is over 2 lines to accentuate the problem of no space between the rectangles.) Is there an easy way to put a space between the rectangles; size controls the width but there appears to be nothing for the height? xyplot(1~1, key = list(corner = c(0.8,0.8),

Hopefully the attachment will make it this time... Hi: I am practicing with the attached shapefile and was wondering if I can get some help. Haven't used 'rgdal' and 'maptools' much but it appears to be a great way bring map data into R. Please take a look at the comments and let me know if I need to explain better what I am trying to accomplish. library(rgdal)

I am basically trying to append a value(vector) to one dataframe using a relational value from another dataframe. Obviously, I can use a loop to accomplish this. However, is there a way to vectorize it? Example: > data <- data.frame(c(1,1,1,2,2,2,3,3,3),rep(2,9)); names(data) <- > c("Sample","Score") > meta <-

Dear R-helpers, I have a time series analysis problem in R: I want to analyse the output of my simulation model which is proportional cover of shrubs in a savanna plot for each of 500 successive years. I have run the model (which includes stochasticity, especially in the initial conditions) 17 times generating 17 time series of shrub cover. I am interested in a possible periodicity of shrub

Hello all, I have a data set where the response variable is the percent cover of a specific plant (represented in cover classes 0,1,2,3,4,5, or 6). This data set has a lot of zeros (plots where the plant was not present). I am trying to model cover class of the plant as a function of both total nitrogen and shrub cover. After quite a bit of research I have come across a conditional approach

Hello useRs, I have some problems when I try to plot a lattice. The lattice has 200 columns (X value) and 5 rows (Y value), with a total of 1000 squares. I want to plot this lattice with the squares having different colours according to a variable value. The variable I want to plot is cover value of a shrub, and it has values between 0 and 1. I want to plot the squares with value 1,

If I have a data frame where a species occupies several rows with different phases such as (both col's ar factors): species,phase Populus tremula,1 Populus tremula,2 Populus tremula,3 Calluna vulgaris,1 Calluna vulgaris,2 Betula alba,1 Betula alba,2 Betula alba,3 Primula veris,1 Primula veris,2 and another df where each species only have one row: species,growth_form Populus tremula,tree Acer

Hi all, I'm using spatstat to investigate the spatial structure of an arid shrub population. The first-order intensity of my data does not appear to be homogenous, so I would like to use inhomogeneous techniques. I realise there is a inhomogeneous K-function available in spatstat, but there doesn't not appear to be one for the pair-correlation function (O-ring statistic). As such I was

Hi: I am practicing with the attached shapefile and was wondering if I can get some help. Haven't used 'rgdal' and 'maptools' much but it appears to be a great way bring map data into R. Please take a look at the comments and let me know if I need to explain better what I am trying to accomplish. library(rgdal) library(maptools) library(ggplot2) dsn="C:/Documents and

Hi everybody, Well, I really hoped that until now i know how to do a selection ..... but obviously i don't. So risking your laughing at me - here you go. I have a table with lots of vegetation data, and one column specifies in which region the data is. There are only 2 regions, region 1 and region 3. I want to select all data that belong to region 1 and after do some xtabs on it. If i do

Hi, My name is Jos?? Mar??a G??mez, and I am pretty new in R. Thus, I apologize deeply if my questions are extremmely na??ve.I have checked several available books and URL's, without finding any answer. I'm trying to fit Generalized Linear Mixed Models via PQL. Below I provide the structure of my data set. Year and Plot are random variables. Fate is the binomial dependent. I have severe