similar to: help: advice on the structuring of ReML models for analysing growth curves

Hello all, If someone could take a little time to help me then I would be very grateful. I studied piping plovers last summer. I watched each chick within a brood for 5 minutes and recorded behaviour, habitat use and foraging rate. There were two Sites, the first with 4 broods and the second with 3 broods. http://r.789695.n4.nabble.com/file/n4511178/Table_PP_Maslo_et_al.png As the data within a

Hi, In the examples from the ReShape package there is a simple example of using melt followed by cast that produces a smallish amount of output about the chicks database. Here's the code: library(reshape) names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) DietResults <- cast(chick_m, diet + chick ~ time) DietResults My challenge

Hello, I have been attempting to set up a lme and have looked at numerous posts including 'R's lmer cheat-sheet' as well as reading a number of papers and other resources including R help, but I am still a little confused on how to write my model (I thought I had it). I have asked a number of questions on different forums; most of which have been resolved. My main concern right now

Dear R-users, I did some more research and I'm still not sure how to set up an ANCOVA with nestedness. Specifically I'm not sure how to express chicks nested within boxes. I will be getting Pinheiro & Bates (Mixed Effects Models in S and S-Plus) but it will not arrive for another two weeks from our interlibrary loan. The goal is to determine if there are urbanization (purban)

Dear R-users, I set up an experiment where I put up bluebird boxes across an urbanization gradient. I monitored these boxes and at some point I pulled a feather from a chick and a friend used spectral properties (rtot, a continuous var) to index chick health. There is an effect of sex that I would like to include but how would I set up a regression and look at the effect of urbanization

Dear R-experts, Quite new to R on this end, but learning fast (I hope). I am running version 2.7.1 on Windows Vista. I have small dataset which consists of: # NestID: nest indicator for each chicken. Siblings sharing the same nest have the same nest indicator. # Chick: chick indicator consisting of a unique ID for each single chick. # Year: 1, 2. # ClutchSize: 1-, 2- , 3-eggs. # HO:

Dear R-experts, I am running R version 2.7.1 on Windows Vista. I have a small dataset which consists of ?chick ID?, ?year (0, 1)?, ?hatching order [HO, defined as first, second and third-hatched chick]?, and the binary outcome of interest ?death (0, 1)?. So a subset of my dataset looks like this on a txt file: y ID Yr HO 1 1 1 First 0 2 1 First 0 3 1 Second 0 4 1 First 1 5 1 First 0 6 1 Third

according to the documentation of the cast function in the reshape function, I would expect this bit of code from the examples to calculate marginal means over only the 'diet' variable. #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) cast(chick_m, diet + chick ~ time, mean, margins="diet") But,

Hi R experts, I have been using ReML as follows... model<-lmer(late.growth~mtf+year+treat+hatch.day+hatch.day:year+hatch.day:treat+ mtf:treat+ treat:year+ year:treat:mtf+(1|fybrood), data = A) then I wanted to plot the results of the three way interaction using lines() as follows... tmp<-as.vector(fixef(model)) graph1<-plot(mtf,fitted(f2), xlab=list("Brood Size"),

Hi all, I have been using princomp() recently, its very useful indeed, but I have a question about how to specify the rows of data you want it to choose. I have a set of variables relating to bird characteristics and I have been using princomp to produce PC scores from these. However since I have multiple duplicate entries per individual (each bird had a varying number of chicks), I only want

Hi All I have regression coefficients from an experiment and I want to plot them in lattice using panel curve but I have run into error messages. I want an 3 panel conditioned plot of 2 curves of Treatment 2 in each panel conditioned by Treatment1, the example curve expression is x+value*x^2 A rough toy example to give an idea of what I want is: Data: data = expand.grid(Treatment1 =

I just had a manuscript returned with the biggest problem being the analysis. Instead of using principal components in a regression I've been asked to analyze a few variables separately. So that's what I'm doing. I pulled a feather from young birds and we quantified certain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I

Trying to find a quick/slick/easily interpretable way to collapse a data set. Suppose I have a data set that looks like this: h <- structure(list(INDEX = structure(1:6, .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"), TICKS = c(0, 0, 0, 0, 0, 3 ), BROOD = structure(c(1L, 1L, 2L, 3L, 3L, 3L), .Label =

Dear R-help, I would like to compute the variance for the proportion of treatment effect by a surrogate in a survival model (Lin, Fleming, and De Gruttola 1997 in Statistics in Medicine). The paper mentioned that the covariance matrix matches that of the covariance matrix estimator for the marginal hazard modelling of multiple events data (Wei, Lin, and Weissfeld 1989 JASA), and is implemented

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

Hello, everyone. I'm hoping to prevent myself from doing a lot of pointing and clicking in Excel. I have a dataframe of bird nest check observations, in which I know the date of the first check, the date of the second check (both currently in Julian date format), the status of the nest at the second check (alive or failed), and the date that the nest hatched (i.e. changed from Incubation

hello everyone, I'm modelling in lmer an average chick weight defined as "Total.brood.mass ~ offset(chick.number), with three fixed and two random effect. Next, I want to use function dredge from MuMIn package for model averaging. Not sure why, but in consequence the offset variable is treated as a predictor, so I get a table that mixes models with and without that offset term (the first

Quick question, as I attempt to learn R. For post-hoc tests 1) Is there an easy function that will take, say the results of tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2, and 3, with 1 and 2 being statistically the same and 3 being different from both Group Treatment A 1 A 2 B 3 2) I've been stumbling over the proper syntax for simple effects for a tukeyHSD

Hi: I have the following dataset in R( thanks Gabor for your help) but now the problem is that all the dashes are converted to questions marks ("?") I?am trying to get the dashes back using: mydf <- data.frame(lapply(mydf,function(x) replace(mydf,"?"(x),"-"))) but isn't working. I also tried: mydf <- replace(mydf,mydf=="?","-)")

Hi: I?need?help with a dataframe(see?pic attached). is a mix of dates and text. I want to create a table either?using latex function from hmisc or xtable. I already know how to do this but the problem is getting the dataframe into R. I don't have a reproducible example?but I am hoping that the pic attachment will make it to you.?If someone is interested in helping with this task I could send