similar to: Nested ANOVA models in R

Hi. I'm analysing some fish biological traits with a gam in mgcv. After several tries, I got this model log(tle) = sexcolor + s(doy, bs = "cc", by = sexcolor) +log(tl) sexcolor is a factor with 4 levels doy is "day of year", which is modeled as a smoother tl is "total length of the fish" The summary of this models is (only parametric coefficientes): Parametric

Dear R users aim Barplot of insect trap catches (y variable trapcatch) at one specific station (variable FiBL_Hecke) from week 1-52 ( x variable week). It works well using the function tapply (sum trapcatch per week, males and females not separated), however, I intend to separate the y variable trapcatch in males and females (variable m_w: m and w) problem I used the function "add" to

Dear all,This is both an R and a statistics question. I want to test whether males and females of a given species tend to co-occur in a given sampling unit more frequently than expected by chance. I'm thinking about using a binomial distribution with p as the sex ratio of the entire population. So, even though the population sex ratio is close to 50:50, each sampling unit would have

I'm not sure how to best explain what I am after but here goes. I have a data frame with 2 geographical factors. One is the major region the other is the component regions. I am trying to process all the regions at the same time without using "for". So I need (think, I do) a list of matrices each structured according to the number of subregions within each region. So is there a

Hi all - R2.0.1, OS X Perhaps while there is some discussion of lme going on..... I am trying to execute a glmm using glmmPQL from the MASS libray, using the example data set from McCullagh and Nelder's (1989, p442) table 14.4 (it happens to be the glmm example for GENSTAT as well). The data are binary, representing mating success (1,0) for crosses between males and females from two

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is

Hello All, I would like to report the following bug or maybe you can explain if I am wrong. I am sampling from two different populations with weights. The two populations have the same age groups and I want to distinguish where I am sampling from. That is why I am using a matrix such as: matrix age.group Male Females Weight.Males Weight.Females 1 1.1

Hi, I'm analysing some anthropometric data on fifty odd skull bases. We know the gender of each skull, and we are trying to develop a predictor to identify the sex of unknown skulls. Rpart with cross-validation produces two models - one of which predicts gender for Males well, and Females poorly, and the other does the opposite (Females well, and Males poorly). In both cases the error

Hi, Sibylle, since you write '"mathematically" add', does barplot(rbind(m$trapcatch, w$trapcatch)) do what you want (modulo layout details)? Hth -- Gerrit --------------------------------------------------------------------- Dr. Gerrit Eichner Mathematical Institute, Room 212 gerrit.eichner at math.uni-giessen.de Justus-Liebig-University Giessen Tel:

Hi, I have found quite a few posts on normality checking of response variables, but I am still in doubt about that. As it is easy to understand I'm not a statistician so be patient please. I want to estimate the possible effects of some predictors on my response variable that is nº of males and nº of females (cbind(males,females)), so, it would be:

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Greetings. I'm getting started learning R, and I'm trying to reproduce some models I've done previously in SAS. I'm trying to fit simple Poisson regressions, and I keep getting impossible results: the models predict negative numbers of cases for many observations. The code for the models are: Female.model <- glm(Observed ~ Black + Other, family = poisson(link=log),

Hello to all R users, I use R for a year now and am dealing with geometric morphometrics of deer skulls. Yes, I am a biologist and my math skills are just beginning to brush up. To cut to the chase... I have two groups in my data (males and females) and my data is in a simple vector form. Now I need a bootstrap test for this value szc1 <- ((mean(maleCent)-mean(femaCent))^

Hi folks Can anyone direct me to a guide to analysing mixed models in R that is understandable by mere mortals (or worse, mere biologists)? I have read MASS and also Mick Crawley's book on the subject, have thrashed my way through the various help files from the NLME package and I'm afraid I remain baffled :-(. Particularly confusing is exactly what groupedData does, how I should use

Hi, I'm new at this, I'm very confused, and I think I'm missing something important here. In our pet example we have this: > fm <- lme(Orthodont) > plot(Orthodont) > plot(augPred(fm, level = 0:1)) which gives us a trellis plot with the females above the males, starting with "F03", "F04", "F11", "F06", etc. I thought the point of

Hi, I am sampling two random columns from females and two random columns from males to produce tetraploid offspring. For every female I am sampling a random male. In the end I want to write out a a matrix with all the offspring, but that does not work. I get always only the offspring from the last females. There must be a mistake in my script: moms<-read.delim("females.txt",

Please note is with replacement From: taby gathoni <tabieg@yahoo.com> To: R help <r-help@r-project.org> Sent: Thursday, March 10, 2011 11:53 AM Subject: [R] random sampling steps in R Dear all, Could someone assist me in random sampling steps/code in R? I have a main sample of 42 males and 165 females and I want to come up with about 1000 samples of 20 males and 20 females from

Dear Gerrit Thanks a lot. "rbind" seems to be the right function. Unfortunately there is a shift in the x-axis (see pdf). There are 52 trapcatch values each, m and w, but m$trapcatch and w$trapcatch are shifted up to x-value 60. The follow-up lines for temp and humidity are fine. Thanks Sibylle setwd("~/Desktop/DatenLogger2017") # am Mac sks trap =

Dear all, Could someone assist me in random sampling steps/code in R? I have a main sample of 42 males and 165 females and I want to come up with about 1000 samples of 20 males and 20 females from this main sample. While at it, i would also like to come up Accuracy Ratios (ARs) with corresponding confidence intervals. Please assist. Thanks so much, Taby [[alternative HTML version

this is the problem: load this R data frame over the internet and save it to your hard drive. http://rss.acs.unt.edu/Rdoc/library/twang/data/raceproling.RData please show how to save a dataset of males only (the variable male=1) to a new dataframe. Then do the same thing for females (male=0). Then show how to recombine the two datasets to belike the original one except that the female