similar to: LAWLESSNESS

Hello , I am writing because I would need some advice on the following question. I am working on paternity in a monogamous bird species and I am performing analyses to check whether the probability for a male to be cuckolded (binary variable) depends on his body size, the body size of his female, the degree of genetic relatedness to his female and nest density around his own nest (all continuous

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Dear R experts, Could you please tell me how to nest random effects in an lmer model? Right now, my function looks like this: Full<-lmer(Consortship~((1|Male)+(1|Female))+MaleRank+MaleAge+MMGroom+MMAggrRankControl+FemaleRank+FemaleAge+MFAggrMating+GroomBirth07+MatNoConsortGroomF, family=poisson) However, a) I'm not sure if placing the two random effects (Male and Female) in parentheses

I am trying to automate a report that my company does every couple of years for the state of Maine. In the past we have used SPSS to run the data and then used complicated Excel template to make the tables/graphics which we then imported into Word. Since there are 256 tables/graphics for this report, this work flow is a little painful. I would like to automate the process and I think I can do

I have been trying to produce a histogram that has two groups (male and female snakes) on the same graph (either superimposed or each frequency bar appears side by side). I found a couple of functions for superimposed histogram written by other people. The below is the codes I used for my data containing a column of svl (body size; snout-vent length) and another column of sex (male or female).

Hello, I'm trying to integrate example data in the shape of a tab delimited ASCII file into my package and therefore dropped it into the data subdirectory. The build works out just fine, but when I attempt to install I get: ** building package indices ... Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 1 did not have 500 elements Calls: <Anonymous>

Hi Folks, I have repeated measures for data on association time (under 2 acoustic condtions) in male and female frogs as they grow to adulthood (6 timepoints). Thus, two within-subject variables (Acoustic Condition: 2 levels, Timepoint: 6 levels) and one between-subject variable (Sex:male or female). I am pretty sure my distributions depart from normality but I would first like to simply run a

Hai, smbclient -L $(hostname -s) on CLI, wworks witout problem on ubuntu 18.04, with the newly builded samba 4.9.1 package from repo and the kodi 18 Beta4. I also noticed that kodi 18 has some setting now in /home/kodi/.kodi/userdata/guisettings.xml <setting id="smb.legacysecurity" default="true">false</setting> <setting

On Thu, Mar 07, 2024 at 05:07:49PM +1100, Darren Tucker wrote: > On Thu, 7 Mar 2024 at 14:54, The Doctor <doctor at doctor.nl2k.ab.ca> wrote: > [...] > > /usr/local/bin/openssl version -a > > ld-elf.so.1: /usr/lib/libssl.so.3: version OPENSSL_3.2.0 required by /usr/local/bin/openssl not found > > This is why configure (with the patch) didn't use that openssl

Hi, Im having trouble creating the following graph. Here is my code: library(plotrix) library(prettyR) female_improvement <-read.table("C://project/graphs/gender/breakdown/gender-improvement/female-improvement.csv", sep=",", header=TRUE) barp(rbind(rep(length(female_improvement$gender),2),freq(female_improvement$reason)[[1]]), ylab="22 Males participated in the

Dear list, I have following data set, which I want to plot the "Scale" variable on the x-axis and "Mean"´on the y-axis for each Ageclass and for each sex. The Mean value of each Ageclass for each sex would be connected by a line. Totally, there should be 6 lines, from which three present the Mean values of each Ageclass for respective sex. Are there any easy ways to do

Hi all, I have data like this: >sample <- read.csv(file="sample.csv",sep=",",header=TRUE) > sample stdate Domain sex age Login 1 01/11/09 xxx FeMale 25 2 2 01/11/09 xxx FeMale 35 4 3 01/11/09 xxx Male 18 30 4 01/11/09 xxx Male 31 3 5 02/11/09 xxx Male 32 11 6 02/11/09 xxx Male 31 1 7 02/11/09

I am trying to add confidence (error) bars to lattice barcharts (and dotplots, and xyplots). I found this helpful message from Deepayan Sarkar and based teh code below on it: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/50299.html However, I can't get it to work with groups, as illustrated. I am sure I am missing something elementary, but I am unsure what. Using R 2.1.1 on various

This dataset below is one sample answer the questioner from our customer. > testbank <- read.table("testbank.txt", header=T) > testbank age married income gender ownhouse class 1 20-30 no high female yes 1st 2 30-40 no high female yes 1st 3 40-50 no low female yes 1st 4 50-60 no high female yes 1st 5 60-70

Dear all, This relatively routine analysis has left me frustrated and in a rut. I have a dataset (data1), which I subset in order to remove rows where HabitatDensity="Med". This dataset looks correct when I call it up, however, when I create a table out of the new subset (data2), my table continues to show the "Med" information as 0. This is a problem because I need a

Hello R-users/experts, I am new to R- I have a simple question: Let say I have a data set as follows temp:[file attached] the data structure is a follows: sex age female 28 female 53 female 53 female 36 male 42 male 29 male 43 male 36 male 41 Here we are grouping all male value into male and all female value in to female

The data is in the attachment. What I wanna get is: , , Sex = Male Eye Hair Brown Blue Hazel Green Black 32 11 10 3 Brown 53 50 25 15 Red 10 10 7 7 Blond 3 30 5 8 , , Sex = Female Eye Hair Brown Blue Hazel Green Black 36 9 5 2 Brown 66 34 29 14 Red 16 7 7 7 Blond 4

Hi All, I have the data like this : >sample <- read.csv(file="sample.csv",sep=",",header=TRUE) > sample stdate Domain sex age Login 1 01/11/09 xxx FeMale 25 2 2 01/11/09 xxx FeMale 35 4 3 01/11/09 xxx Male 18 30 4 01/11/09 xxx Male 31 3 5 02/11/09 xxx Male 32 11 6 02/11/09 xxx Male 31 1 7 02/11/09

Hi I have a question regarding an output of a binomial lmer-model. The model is as follows: lmer(y~diet * day * female + (day|female),family=binomial) The corresponding output is: Generalized linear mixed model fit by the Laplace approximation Formula: y ~ diet * day * female + (day | female) AIC BIC logLik deviance 1084 1136 -531.1 1062 Random effects: Groups Name Variance

Hi, I have newbie question. Suppose I have the following data: temp <- data.frame(type1 = c("male", "female", "male", "female", "female"), type2 = c("low", "med", "high", "low", "med"), a = c(1,2,4, NA, 3), b = .... [TRUNCATED] temp type1 type2 a b c 1 male low 1 5 0 2 female