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Dear All, THis is my "data.char$excluded" object and I want to show its content in my output screen. > data.char$excluded $names [1] "Species" "Target" "Sex" [[2]] [1] "DG" [[3]] [1] "R" [[4]] [1] "M" > When I run cat() in S Plus, I got: > cat(data.char$excluded, "\n") c("Species",

Dear Experts, I have a data object named "data.char". When I use cbind(data.char) in SPlus, I got the following results: > cbind(data.char) data.char data matrix, 2700 excluded list, 3 cluster.var character, 2 strata list, 3 xlog CT link logit gpcorr 1 missing.row numeric, 0 zero.row numeric, 22 infile perc.csv attr(,

Dear Experts, I need to calculate the cdf of the standard normal distribution, i.e. H(x) = 1/sqrt(2*pi) integral(exp(-z^2/2) dz), where z is b/w -infi to infi. I know there should be a way to do it in R, but did not know to do it. I'd appreciate any help you could offer. Charlie Liu Graduate student intern at EPA/ECO

Dear Experts, In glim() of S Plus, one of the returned values is "var", the estimated variance matrix of coefficients. However, in glm() of R (there is no glim() in R), "var" is not one of the returned values. Anyone know what could I get the varience matrix of coefficients in glm() in R? As a novice in R and S+, I'd appreciate your help Sincerely, Charlie Liu

Dear R People, I'm working on a plot function to produce the graph shown below. One of the features my supervisor does not like is that the labels in the Y- axix are shown vertically. Is there a way to change that to horizontally ? This is the axix() function I used : axis(2,at=seq(nstrat),labels=snames) The labels (snames) are: SEV1:HU:C, SEV1:MU:C, SEV1:MU:L, SEV1:RT:C and SEV1:RT:L,

Dear Expects, I tried to install the SJava package to the Library subdirectory of my R1.5.0. However, when I tried it according to the instructions from http://www.omegahat.org/RSJava/, I got the following error messages: library(SJava) Error in .Defunct() : `.Alias' is defunct. See ?Defunct. >.JavaInit() Error: couldn't find function ".JavaInit" I downloaded SJavaWin

Hi, I just downloaded the latest version of R - 1040. I'm doing the project of converting CATREG - a statistical software writen in S Plus to R. However, when I copied my R codes to the 1040 bin directory and resource my codes, I got the following error message, this never happened in my 1030 or 1010 versions of R. Anyone one know what's wrong with 1040? THanks CHarlie Liu EPA/ECO

Dear Friends I have a technical question about conducting 2 way repeated measures ANOVA analysis using R. 1. Data set: repeated measurement of activity over night (2 hr. intervals) repeated (within subject)factor: Hours Between subject: Species, Sex Dependent variables: specimens Here is how the data arranged for the analysis: Subject Replicate Hour Sp. Specimens 1 1 1 a 2 1 1 a

Dear Gideon Unless you are willing to accept uncorrelated measures in time and homogeneity of variance I would go for a linear mixed model instead. You might be surprised on how diffrent can be your results. Try; >?lme I hope that this helps Francisco >From: GIDEON WASSERBERG <wasserberg at wisc.edu> >To: "R-help at lists.R-project.org" <R-help at

Hi, I have been having some trouble using aov to do an anova, probably because I'm not understanding how to use this function correctly. For some reason it always tells me that "Estimated effects may be unbalanced", though I'm not sure what this means. Is the formula I am using written incorrectly? Below is the code I am using along with the data: > my.data response

I'm getting an error from nlme that has me stymied. I have a data set ,'mydata', with variables: AChE, Dose, sex, set, and mrid; 'set' and 'mrid' indicate two levels of nesting, with 'set' nested within 'mrid'. I want to fit the model: mod <- nlme(AChE ~ Cexp(Dose, A, B, m), data=mydata, fixed = A+B+M~sex, random=A+B+m~sex | mrid/set,

Hi Sergio Doing those tests 30 times is going to give you a huge Type I error rate, even if there was a function that did that. There is a reason why TukeyHSD doesn't make it easy. In general, if there are useful comparisons among the species, you are better off setting up and testing contrasts than doing all-pairwise Tukey tests. Also, the PCA scores are ordered in terms of variance

Dear all, I'm testing the effect of species and sex in my sample by using the principal component scores of a PCA analysis. I have 30 PCs and I tried to see if there is any significant difference from males to females, given that there is a significant effect of phylogeny (factor with several species). I didi it like this: Y<-PCA$pc.scores[,1:30] fit <- manova(Y ~ sp*sex)

I am a true R novice aonly using it for this function ;) I am trying to use color2D.matplot to form a image of my data using the following conditions color2D.matplot(fi1, c(dr), c(dg), c(db), nslices=7, ylab='Species', xlab="gene", show.legend=TRUE) where fi1 is my matrix. I have a matrix with 36 columns and 130 rows. most entries are 1 or 0 and I am trying to get this

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

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

Hello, Is there an easy function for switching list to matrix. My list is of genetic distances between species pairs: A A 0 A B .5 A C .25 B C .5 and I want a distance matrix such as: A B C A 0 .5 .25 B .5 0 .5 C .25 .5 0 for use in a mantel test. Thank you for the help! cheers, charlie -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Charles G. Willis Department of Organismic

Dear list members, I have a question concerning multiple comparisons after using glm. My response variable is days until emergence of an insect species. The explanatory variables are sex (two levels), parasitoids added (two levels) and populations (34 levels). I would like to know now which populations are different in days until insect emergence. For this I used multiple comparisons as

Hello, I've got a small dataset on box turtle shell measurements that I would like to perform a detrended correspondence analysis on. I thought that it would be interesting to examine the morphometrics for each species in the area of overlap and in areas where neither species occurs.  I've taken a look at the dune and dune.env datasets in vegan. Using the str() command gives me  >

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >