similar to: error message when using outer function

Esteemed useRs and developeRs, I need to create a '3D line plot' (proper name?) of which an excellent example can be viewed here: http://cococubed.asu.edu/images/87a/images/unknown_pleasures.jpg I have some experience using the rgl package to create 3D PCA plots, but have no idea where to start for an image like this. I'd really appreciate suggestions & help on how might

Hi, if I know the colnames x and y in the following example, I can easily to do lm. tmp <- data.frame(x=c(1,1.2),y=c(1,2)) lm(y ~ x, data=tmp) when the colnames are variable, what should I do? for example colnames(tmp)[1] <- paste("aa",1,sep="_") lm(y ~ paste("aa",1,sep="_"), data = tmp) it gives me error. [[alternative HTML version

Maybe embarassing, but I can't quickly find out how to do this: Mix expression() text and regular text __in a variable__ for plotting: I can easily do plot(1,type="n",main=expression(paste("Plot of ", x ^ alpha, "; ", alpha==2.3) However, if alpha is really just in a variable, I don't know what to do alpha <- 2.3 ch.a <- format(alpha) and now

Maybe embarassing, but I can't quickly find out how to do this: Mix expression() text and regular text __in a variable__ for plotting: I can easily do plot(1,type="n",main=expression(paste("Plot of ", x ^ alpha, "; ", alpha==2.3) However, if alpha is really just in a variable, I don't know what to do alpha <- 2.3 ch.a <- format(alpha) and now

Could somebody help me to understand the syntax of R's ppois function? I'm looking to calculate the cumulative probability density of an observed value (y) given the expected mean (mu) and the level of significance (alpha). I'm coming from using SAS to do this and don't recognize the descriptions of the arguments for ppois. The definitions of lambda and p as stated in the R manuals

Hi the list. I do not understand the philosophy behind numeric and integer. - 1 is numeric (which I find surprising) - 2 is numeric. - 1:2 is integer. Why is that ? Christophe

Hello all, if I have multiple imputed data sets, is there a command or function in R in any package you know of to combine those, I know one common MI approach is rubins rules, is there a way to do this using his rules or others? I know theres ways, like using Amelia from Gary King's website to create the imputed data sets, but how to make them into one or combine them for analysis.

Hi, A question. When I run gelman.diag and gelman.plot with mcmc lists obtained from MCMCregress, the results are following. > post.R <- MCMCregress(Size~Age+Status, data = data, burnin = 5000, mcmc = 100000, + thin = 10, verbose = FALSE, beta.start = NA, sigma2.start = NA, + b0 = 0, B0 = 0, nu = 0.001, delta = 0.001) > post1.R <- MCMCregress(Size~Age+Status, data

Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

Hi, all, I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the

Hello, I am using t-test to check if the difference between two poulations is significant. I have a large N=20,000, 10,000 in each population. I compare a few different poulations with eachother and though I get different t-scores, I get the same p-value of 10^-16 which seems like the limit for this function. Is this true and is so, is there a workarounf to get a more sensitive/accurate p-value?

The Hmisc and Design libraries have been updated respectively to versions 1.4-2 and 1.1-1. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r . Web sites for the libraries are http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html and http://hesweb1.med.virginia.edu/biostat/s/Design.html . Thanks to Xiao Gang Fan for porting the libraries

The Hmisc and Design libraries have been updated respectively to versions 1.4-2 and 1.1-1. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r . Web sites for the libraries are http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html and http://hesweb1.med.virginia.edu/biostat/s/Design.html . Thanks to Xiao Gang Fan for porting the libraries

What does missInfo compute and how is it computed? There is only 1 observation missing the ethnic3 variable. There is no other missing data. N=1409 > summary(MIcombine(mod1)) Multiple imputation results: with(rt.imp, glm(G1 ~ stdage + female + as.factor(ethnic3) + u, family = binomial())) MIcombine.default(mod1) results se (lower upper)

Hello R-people! I have a general statistical question about regressions. I just want to describe my case: I have got a dataset of around 150 observations and 1 dependent and 2 independent variables. The dependent variable is of metric nature (in my case meters in a range from around 0.5-10000 m). The first independent is also metric (in mm ranging from 50-700 mm) and it is assumed to be