similar to: Mapping actual to expected columns for princomp object

[Re-directing to R-devel, as I think this needs changes to the code.] Can I suggest a modification to stats:predict.princomp so that it will check for column (variable) names? In src/library/stats/R/princomp-add.R, insert the following after line 4: if (!is.null(cn <- names(object$center))) newdata <- newdata[, cn] Now Dana's example looks like: > predict(pca1, frz) Error in

Has the algorithm used by batchSOM for hexagonal topology changed in some way between R 1.9.1 and 2.0.1? In comparing between the two versions, I get identical results for rectangular topology, but very different results for hexagonal topology. (I tried to find release notes for the VR bundle that might explain the changes, but was unable to locate these anywhere.) Here's the script I

Hi All, I have been trying to get to plot my PCA legend outside of the PCA plot, but success still alludes me. Can you guys please advise how I can achieve this. I used locater() to obtain coordinates for below the Comp.1 axis. Using these coordinates the legend disappears. Below is the code for the PCA and legend. Thanks in advance for the help. Regards Tinus r.cols <-

Hallo everybody! I am trying to perform a TiTAN (Baker & King 2010) analysis with R 2.14.1. I have come that far: h89Abund <- read.csv("Fish89Abund.csv") > names (Fish89Abund) [1] "StationCode" "Abramisbrama" "Alburnoidesbipunctatus" "Alburnusalburnus" [5] "Ameiurusmelas" >

Hi Group, Here is some data. p <- runif(1000) # sample data groups <- rep(c(1,2),each=500) #conditioning variable mydata <- cbind(p,groups) n <- length(p) u <- (1:n)/(n + 1) # uniform distribution reference for qqplot logp <- -log(p,base=10) logu <- -log(u,base=10) qqplot(logp,logu) How can I make the above qqplot in lattice and/or ggplot2. The sample is uniform, and I take

Fellow R users, I'm using the BCE {BCE} function to run a Bayesian sediment mixing model. The aim is to find the optimum % contribution from each of the 4 source areas that can yield the target geochemistry. I have geochemistry for 4 source areas called Rat: Rat<-read.table(text="CaO MgO Na2O Al2O3 Topsoils 2.511250 0.7445500 0.7085500 14.10375 ChannelBanks

I am working on a nlme model that has multiple fixed effects (linear and nonlinear) with a nonlinear (asymptotic) random effect. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) asymporigb<-function(x,th1b,th2b)th1b*(1-exp(-exp(th2b)*x)) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b),

Hi, I was trying as well as looking for an answer without success (a bit strange since it should be an easy problem) and therefore I will appreciate you help: My simple script is: # Loadings data of 5 columns and 100 rows of data data1<-read.csv("C:/?/MyPCA.csv") pairs(data1[,1:4]) pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE) biplot(pca1) The biplot present the data

I have hit a problem with the design of the mcmc package I can't figure out, possibly because I don't really understand the R function call mechanism. The function metrop in the mcmc package has a ... argument that it passes to one or two user-supplied functions, which are other arguments to metrop. When the two functions don't have the same arguments, this doesn't work.

Can someone offer some advice on how to properly evaluate a SYMSXP from a .Call ? I have the following in R: variable xn, with an attribute "mu" which references the variable mu in the global environment. I know "references" is a loose term; mu was defined in this fashion as a way to implement deferred binding: foo <- function(x,mu) { attr(x,"mu") <-

Dear Forum, I have a class variable 1 (populations A-E), and two other class variables, variable 2 and variable 3. What I want is to see if the combination of var 2 and var 3, will give me a pattern that allows to distinguish populations. I found several packages like ade4, with pcaiv function and factoMineR. but there are not working. Using the ade4 package, when I try to build the pca: pca1

This makes it possible to wait for a specific amount of time, rather than wait until infinity. Signed-off-by: Maarten Lankhorst <maarten.lankhorst at canonical.com> --- Splitted out version, I've noticed that I forgot to convert radeon_fence_wait_empty to long r, fixed. drivers/gpu/drm/radeon/radeon_fence.c | 60 +++++++++++++++++++++++------------ 1 file changed, 40 insertions(+),

Am 02.06.2014 15:14, schrieb Maarten Lankhorst: > This makes it possible to wait for a specific amount of time, > rather than wait until infinity. > > Signed-off-by: Maarten Lankhorst <maarten.lankhorst at canonical.com> > --- > Splitted out version, I've noticed that I forgot to convert > radeon_fence_wait_empty to long r, fixed. >

Hi!! I'm having trouble selecting 10 out of 41 attributes of the KDD data set. In order to identify the components with the higher variance I'm using princomp. the result i get for summary(pca1) is: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9

op 15-05-14 11:42, Christian K?nig schreef: > Am 15.05.2014 11:38, schrieb Maarten Lankhorst: >> op 15-05-14 11:21, Christian K?nig schreef: >>> Am 15.05.2014 03:06, schrieb Maarten Lankhorst: >>>> op 14-05-14 17:29, Christian K?nig schreef: >>>>>> + /* did fence get signaled after we enabled the sw irq? */ >>>>>> + if

I am examining the following nlme model. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) mod1<-nlme(fa20~(ah*habdiv+ad*log(d)+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2), fixed=ah+ad+ads+ads2+at+th1+th2~1, random=th1+th2~1, start=c(ah=.9124,ad=.9252,ads=.5,ads2=-.1,at=-1,th1=2.842,th2=-6.917), data=pca1.grouped) However, the two random effects (th1 and th2)

Changes since v1: - Fixed interaction with reset handling. + Use exclusive_lock, either with trylock or blocking. + Bump sw irq refcount in the recovery function to prevent fiddling with irq registers during gpu recovery. - Add radeon lockup detection to the default fence wait function. --- diff --git a/drivers/gpu/drm/radeon/radeon.h b/drivers/gpu/drm/radeon/radeon.h index

I need some help with a genome-wide association scatterplot. A recently published example of such a plot is attached to this message. Typical variables include: Y: -logP-value (range from 0 to 8) X: chromosomal distance Z: chromosomes 1 through 22 and X (a factor with 23 levels) I used coplot(Y~X|CHR) and xyplot(Y~X|CHR), but I am not sure how to get the entire graph in a single panel (see

These were used because the entropy coder originally came from outside libcelt, and thus did not have a common type system. It's now undergone enough modification that it's not ever likely to be used as-is in another codec without some porting effort, so there's no real reason to maintain the typedefs separately. Hopefully we'll replace these all again somedate with a common set

Am 02.06.2014 12:09, schrieb Maarten Lankhorst: > Changes since v1: > - Fixed interaction with reset handling. > + Use exclusive_lock, either with trylock or blocking. > + Bump sw irq refcount in the recovery function to prevent fiddling > with irq registers during gpu recovery. > - Add radeon lockup detection to the default fence wait function. First of all please