similar to: Merging two data sets

Hi im trying to add error bars to my barplots, there very basic, i have a few grapghs where the y variable is different but on all the X variable is Age (Adult and Juvenile) however this is split into two levels so i have males and females, so my graph basically has four bars on it. I know how to add eror bars for instance when there is only one level eg lookng at the diffrence between male and

Greetings I'm attempting to insert missing data on the smallest size class of cave cricket instars into a data frame. The data involve censusing photoplots (plots) of roosting cave crickets in which we discern in four instars or size classes. I need to insert data on size class one into a data frame that already has data on size classes two through four. The data can be merged by their

dear R users, the data frame (read in from a csv) looks like this: TreeTag Census Stage DBH 1 CW-W740 2001 juvenile 5.8 2 CW-W739 2001 juvenile 4.3 3 CW-W738 2001 juvenile 4.7 4 CW-W737 2001 juvenile 5.4 5 CW-W736 2001 juvenile 7.4 6 CW-W735 2001 juvenile 5.4 ... 1501 1.00E-20 2001 adult 32.5 i would like to

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, I successfully created 3 screens with the following: fig.mat<-c(0,.5,.5,.5,1,1,0,0,.5,1,.5,1) fig.mat<-matrix(fig.mat,nrow=3) fig.mat split.screen(fig.mat) I can plot three different plots on those 3 screens, but when I try the following: (Trying to create three graphs with a common x-axis but different y-axis on screen 1) screen(1) par(oma=c(3,3,0,0)) par(fig=c(0,1,0,0.33))

Dear expert friends, I'm pretty young of this world and my question at your eyes can be petty easy. I'll need to change the name of the levels inside a column of my data-frame levels(ind.davis$Ageclass) <- c("adult", "Juvanile", "sub-adult") names(ind.davis$Ageclass) <- c("Adult", "Juvenile", "Sub-adult") that is what I

Greetings Using the following command I've been trying to subset a dataframe of counts of an organism to compute the sizes of groups for use as a predictor: Hs.patches <- as.data.frame(with(Hs.long, table(Cave,Year,Month,Region,Plot,))) I am getting the following error message that I do not understand: ERROR: argument is missing, with no default Can anyone shed light on this

Leonardo-- R-help can be a very useful resource. Some suggestions to use it well: 1. use an informative subject line, not "help" 2. include a "minimal working example:" a *little* data, the code that, with those data, reproduces your problem, and the error message that resulted. As to your particular question, at this point I can only guess, but for starters it would

Hello. I am trying to write an R function to plot the survival function (and associated hazard and density) for a Siler competing hazards model. This model is similar to the Gompertz-Makeham, with the addition of a juvenile component that includes two parameters---one that describes the initial infant mortality rate, and a negative exponential that describes typical mortality decline over the

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

Hi few typos i've found ?.C Specifying 'ENCODING' overrrides any declared encodings (see 'link{Encoding}') \link{Encoding} -------------------- writing R extensions, pag 73 (pdf version), note 1, and we provided an emulation on Windows 2000): see ?dyn.oad ^ ^^ 1

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

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

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, 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:

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 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: