similar to: Using napredict in prcomp

Dear all, I have a series of variables that looks roughly like the sample data below and I'm trying to conduct a factor analysis. I've omitted cases with missing values for the factor analysis, but now I'd like to use the scores on each component as new variables in the *original* data set for analysis. That is, I'd like to take the scores on each of the two factors and see how

I imitated predict.glm, my thing worked, now I need to revise. It would help me very much if someone would explain predict.glm line 28, which says object$na.action <- NULL # kill this for predict.lm calls I want to know 1) why does it set the object$na.action to NULL 2) what does the comment after mean? Maybe I need a pass by value lesson too, because I can't see how changing that

Maybe a useful addition to the predict functions would be to return the values of the predictor variables. It just (unless there are problems) requires an extra line. I have inserted an example below. "predict.glm" <- function (object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE,

Hi, I''m trying to increase the bandwidth between two hosts (backup). Both hosts are in the same /24 subnet and each of them is connected to a Cisco switch by 2 GbE interfaces (intel e1000). The switches/host are located in different building which are connected by 3 x GbE. building A | building B | --------

I would like to predict a matrix containing missing values according to a fitted linear model. The predicted values must have the same length as the number of observations in newdata, where missing predicted values (due to missing explanatory values) are replaced by NA. How can I achieve this? I tried the following example: > x <- matrix(rnorm(100), ncol=10) > beta <- rep(1, 10) >

x <- NA na.act <- na.action(na.exclude(x)) y <- rep(0,0) naresid(na.act,y) ... currently produces the result... numeric(0) ... whereas the documentation might lead you to expect NA The behaviour is caused by the line if (length(x) == 0L) return(x) in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd expected in the above example (and in a

I'm wondering whether anyone has any insight into why the 'simulate' methods for the built-in glm() families (binomial, Poisson, Gamma ...) extract the prior weights using object$prior.weights rather than weights(object,"prior") ? At first I thought this was so that things work correctly when e.g. subset= and na.action=na.exclude are used. However, the current versions of

I am having trouble understanding the way naresid is used when using predict/residual in a new data frame. >From the example below, the NAs are displayed when predicting inside the original frame, but are dropped when applied to a new frame. ultimately I need to cbind the residuals and predictions to a dataframe. Any help would be appreciated. Thanks in advance. >

Dear All, It might be a simple question. But I could not find the answer from function “prcomp” or “princomp”. Does anyone know what are the codes to get coefficient and scores of Principal component analysis in R? Your reply will be appreciated! Best Zunqiu [[alternative HTML version deleted]]

Hi everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

Hello, Does anyone know when one should use cbq versus (simpler more accurate) htb? Specifically does cbq have added functionality that may be of interest to certain applications? from htb home> Both CBQ and HTB help you to control the use of the outbound bandwidth on a given link. Both allow you to use one physical link to simulate several slower links and to send different kinds of

Hola! I have some problems with gam in mgcv. Firts a detail: it would be nice igf gam would accept an na.action argument, but that not the main point. I want to have a smooth term for time over a year, the same pattern repeating in succesive years. It would be natural then to impose the condition s(0)=s(12). Is this possible within mgcv? I tried to obtain this with trigonometric terms, aca:

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

I would think I could do this but for some reason I am stymied. I have a PRI from RCN connected to a cisco 3640 (in my day "cisco" is all lower case :-)). My config looks something like this on the cisco... --------------------------------------------------------- voice-card 3 dsp services dspfarm ! ip cef ! isdn switch-type primary-5ess ! controller T1 3/0 framing esf linecode

https://bugs.freedesktop.org/show_bug.cgi?id=71472 Priority: medium Bug ID: 71472 Assignee: nouveau at lists.freedesktop.org Summary: GT-740M not detected QA Contact: xorg-team at lists.x.org Severity: normal Classification: Unclassified OS: Linux (All) Reporter: temnota.am at gmail.com Hardware:

buenas   Por favor me pueden decir como ajusto unos datos con las distribuciones poisson generalizada poisso-pascalgeneralizada conway-maxwell-poisson   no se en que paquette estan   Gracias [[alternative HTML version deleted]]

Hello R people I now know how to run my discriminant analysis with the lda function in MASS: lda.alain=lda(Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, gr, CV = FALSE) and it works fine. But I am missing a test and cannot find any help on how to get it, if it exist. The "S" equivalent: discrim(structure(.Data = Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, class = "formula"), data = gr,

Hello, everybody. I recognize I'm asking you to deal with a not-very-important problem. But its important to me :) I've noticed a little inconsistency in the print.function() output. I traced the matter to deparse.c, for which I attach a patch that addresses 3 separate things. There's one important thing that I'm willing to stand behind and 2 litter things that I think would be

Sorry, I was using a modified compiler, which by coincidence made the bug much easier to reproduce. In some rare cases, the compiler will use x30 as a general-purpose register; in that case, outlining breaks because the "ret" branches to the wrong address.  Testcase (reproduce with "clang -O3 --target=aarch64-pc-linux-gnu -mllvm -enable-machine-outliner"): extern long g1;

Hi Eli, > I just tried some tests, and I'm seeing a bunch of failures on SPEC at -O3; looks like mostly crashes at runtime. I can try to reduce a testcase if you need it. If you could do that, that would be great. Our testing has been primarily for -Oz and -O2, so I haven’t looked at -O3 at all. > I don't think this is really the right approach. With LTO, you can have a mix of