similar to: Drawing boxplots in pairs function

Dear R-help, I am trying to rename the variables in a dataframe, called 'T1A' here. Seems renaming was successful, but when I call one of the variable I got error message and I wanted to know why. The data frame contains 365 rows and 49 columns. I would like to name the first column `DATE` and the others T0.5, T1, T1.5,...,T24 (as this is a set of data collected every half hour for a

I'd like to model the relationship between m responses Y1, ..., Ym and a single set of predictor variables X1, ..., Xr. Each response is assumed to follow its own regression model, and the error terms in each model can be correlated. My understanding is that although lm() handles vector Y's on the left-hand side of the model formula, it really just fits m separate lm models. What should

Hi Everyone, I have some data that predicts both a nominal and ordinal response variable. I was wondering what packages in R would help me analyze the data? I was also curious if anyone could recomend me some textbooks that would help with the analysis of such data? I have the 5th edition of "Applied Multivariate Statistical Analysis" by Richard A. Johnson and Dean W. Wichern

Dear helpeRs, the following data set comes from Johnson/Wichern: Applied Multivariate Statistical Analysis, 6th ed, pp. 304-306. /X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3, 2))) Y <- structure(c(0, 2, 4, 0), .Dim = as.integer(c(2, 2))) Z <- structure(c(3, 1, 2, 8, 9, 7), .Dim = as.integer(c(3, 2)))/ I would like to compute Wilk's Lambda in R, which I know is 0.0385.

On Nov 12, 2008, at 3:37 PM, David Greene wrote: >>> Unfortunately, calling FunctionType::refineAbstractType(opaque, void >>> (...)) >>> doesn't work because RefineAbstractType doesn't recurse down into >>> the >>> pointee types. >> >> What types are you starting out with? If you had the equivalent of: >> >> %t1 = type

SUPERMIN_KERNEL and SUPERMIN_MODULES don't work to guestfish. Since guestfish sets --if-newer parameter to supermin, so the environment variables are not used under the following conditions. - the output directory exists and, - the dates of both input files and package database are older than the output To solve that, rebuild the output it when SUPERMIN_KERNEL or SUPERMIN_MODULES are

On Wednesday 12 November 2008 17:29, Chris Lattner wrote: > In fact, I strongly suspect that RefineAbstractType doesn't work if > you give it something that isn't Opaque. I'd recommend only using it > on Opaque, not random abstract types. Ok. > Sure, LLVM is more general than C. In LLVM, you can have a pointer > that points to itself, for example: > > %t =

On Wednesday 12 November 2008 18:58, Chris Lattner wrote: > On Nov 12, 2008, at 3:37 PM, David Greene wrote: > >>> Unfortunately, calling FunctionType::refineAbstractType(opaque, void > >>> (...)) > >>> doesn't work because RefineAbstractType doesn't recurse down into > >>> the > >>> pointee types. > >> > >>

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

I would like to test the hypothesis that the difference between pairs, for several variables, is zero. This is easily done separately for each variable with: lm(Y ~ rep(0, nrow(Y))) where Y is a matrix whose columns are the differences for each variable between pair members. However, I would like to get an overall probability across all variables from a Wilks or Pillai-Bartlett statistic as in

Dear community I have two raster files (here r and r2: y-axis) and an equal x-axis (here s, 4 classes). Instead of plotting two boxplots I would like to plot paired boxplots: for each class the boxplots from r and r2 paired). I tried to adapt the code, but I am struggling around with the error: > names(rs) <- c('r', 's', 'r2') Error in `names<-`(`*tmp*`,

? Fri, 23 Aug 2024 10:15:55 +0200 <sibylle.stoeckli at gmx.ch> ?????: > > s<-sf$Unterregio > > r<-allrasters_pres[[1]] > > > > > > rs <- stack(r, s) > > names(rs) <- c('r', 's') > Error in `names<-`(`*tmp*`, value = c("r", "s")) : > incorrect number of layer names It looks like at least one

Dear Ivan Dear community Quite nice book recommendation. Yes indeed my raster "s" (the shape file for the boxplot classes) has several layers. That's way I tried to select a layer by " s<-sf$Unterregio". > sf <- read_sf("C:/Users/....._BiogeoRegion.shp") > names(sf) > names(sf) [1] "RegionNumm" "RegionName"

Dear Ivan Many thanks. Using my own dataset, my "s" is a layer shape file. Somewhere I need to define : snew<-s$Unterregio I tried it to do it before stack(), but it seems to be the wrong way. Do you have any suggestion? Code: > #first import all files in a single folder as a list > rastlist_pres <- list.files(path ="C:/Users/....._bee_presence",

? Thu, 22 Aug 2024 08:46:03 +0200 SIBYLLE ST?CKLI via R-help <r-help at r-project.org> ?????: > rr2s <- stack(r, r2,s) > > names(rs) <- c('r', 's', 'r2') > > Error in `names<-`(`*tmp*`, value = c("r", "s", "r2")) : > > incorrect number of layer names The error must be happening because the variable

I use 1.7.0 version under Windows XP. Problem: When graphics device history recording function turned on, suppose I source a file containing lines: plot(graph1) plot(graph2) I see the graph2 in the graphic device, assuming graph2 is the last plot in the file. Now using "Previous" under history menu, I get graph1 as expected. But after that, using "Next" does not show graph2.

In MDI mode of Windows, is it possible to save window size and other parameter settings of a graphics device when it is activated, much like Rconsole file? By default the bottom part of a graphic device (windows()) is too big such that it is hidden under the horizontal scrolling and status bar of Rgui. (Of course I can use the vertical scrolling bar of Rgui to see the hidden part.) So I select

I'd like to sample multiple response values from a multivariate regression fit. For example, suppose I have m=2 responses (y1,y2) and a single set of predictor variables (z1,z2). Each response is assumed to follow its own regression model, and the error terms in each model can be correlated (as in example 7.10 of section 7.7 of Johnson/Wichern): > ex7.10 <- + data.frame(y1 =

Hi, we''re playing around with XEN (kernel 2.6.32) and REMUS. For us, sees this combination of very interesting and we would like to use the productive. However, the configuration is a great effort and often leads to problems. Until the first test position ran, it took several tries. As the further development of REMUS is planned? I''ve read that it will soon be over

Hello there, I have two data sets with 14 variables each, and wish to do the test for equality of their covariance matrices and mean vectors. Normally these tests should be done by chi square test (box provided) and Hotelling's T square test respectively. Which R functions could do this kind of test? I just find some functions could do for one dimension, but no for multidimension. Some one