similar to: quick question about binary data

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,

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

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

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 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

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

Hi, I'm trying to work through a Nested ANOVA for the following scenario: 20 males were used to fertilize eggs of 4 females per male, so that female is nested within male (80 females used total). Spine length was measured on 11 offspring per family, resulting in 880 measurements on 80 families. I used the following two commands: summary(aov(Spinelength ~ Male*Female)) and

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, 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",

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

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:

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),

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:

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))^

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

Hi everyone! I am a biologist from Argentina and have to solve this problem. I have an insect population obtained from 10 different nests and need to know its sex ratio. But as I cannot ensure insects independence I need to run a model where I can include the variable “nest” as with a random effect. The response variable has a binomial distribution (males or females). I’ve been reading for a while

I'm creating a stacked bar chart using the likert command in the HH package. My data are in a data frame, with two numeric variables and a categorical variable, I can't get likert to use the column containing the categorical variable as a my y axis label. Here is a quick example: library(HH) #my data are:

I've noticed that R has a number of very useful functions for obtaining descriptive statistics on groups of variables, including summary {stats}, describe {Hmisc}, and describe {psych}, but none that I have found is able to provided weighted descriptives of subsets of a data set (ex. descriptives for both males and females for age, where accurate results require use of sampling

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

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