similar to: Expand DF with all levels of a variable

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

Sir, I have hit a the limits of my understanding of text / par / opar etc... I have a few related xts data frames which have multiple columns, and have written a for-loop to make a set of charts - however i do not get any margin text when i run the loop. when i test the code outside of the loop the margin text appears - however when i run the loop the margin text does not appear ... what gives?

Hi All, I am trying to run a loop that will have varying numbers of rows with each output. Previously I have had the same number of rows so I would use (and I appreciate that this will no doubt achieve some gasps as being thoroughly inefficient!): xdfrow<-(0) xdfrow1<-(1:32) xdfrow2<-(33:64) xdfrow3<-(65:96) xdfrow4<-(97:128) xdfrow5<-(129:160) xdfrow6<-(161:192)

I have NexentaCP2 (Opensolaris b104+). Parameters for the kernel: ''zfs-bootfs=syspool/rootfs-nmu-000,bootpath=/xpvd/xdf@0:a'' Pygrub returns: ''zfs-bootfs=syspool/30,bootpath=/xpvd/xdf@0:a'' Result: Boot with pygrub don''t work. What is "30" in syspool/30 ? _______________________________________________ Xen-users mailing list

Dear R devel I've been wondering about this for a while. I am sorry to ask for your time, but can one of you help me understand this? This concerns duplicated labels, not levels, in the factor function. I think it is hard to understand that factor() fails, but levels() after does not > x <- 1:6 > xlevels <- 1:6 > xlabels <- c(1, NA, NA, 4, 4, 4) > y <- factor(x,

Hi, Some try: > names(mi$xlevels) [1] "f" > all.vars(mi$formula) [1] "D" "x" "f" "Y" > names(mx$xlevels) [1] "f" > all.vars(mx$formula) [1] "D" "x" "f" When offset is indicated out of the formula, it does not work... Marc Le 07/03/2018 ? 06:20, Bendix Carstensen a ?crit?: > I would like

library(effects) mod <- lm(Measurement ~ Age + Sex, data=d) e <-effect("Sex",mod) The effect is evaluated at the mean age. > e Sex effect Sex F M 43.33083 44.48531 > > e$model.matrix (Intercept) Age SexM 1 1 130.5859 0 23 1 130.5859 1 To evaluate the effect at Age=120 I tried: e

Hello. I am new to R, so my question/problem might be very basic, but I cannot figure out how to solve it. So, I would really appreciate your help. I would like to rename a variable in a very large file (181GB) called "2013.xdf" that is saved on my external hard drive. I tried running the following code to rename variable # 2 in the file and save this change, which produced some

Dear list, I would like to do some calculation using different grouping variables. My 'df' looks like this: # Some data set.seed(345) id <- seq(200,400, by=10) ids <- sample(substr(id,1,1)) group1 <- rep(1:3, each=7) group2 <- rep(1:2, c(10,11)) group3 <- rep(1:4, c(5,5,5,6)) df <- data.frame(id, ids, group1, group2, group3) df <- rbind(df, df, df) df$time <-

I want to do a series of contour plots, 4 in all. The data is coming from a data frame named "nd.frame", which has elements "xdf", "ydf", "zdf", and "pndt". I am treating "pndt" as a factor, and it has four levels. I make a call to the lattice graphics routine "contourplot" like so:

Hello, Everybody: This may not be a "bug", but for me it is an unexpected outcome. A factor variable's levels do not retain their ordering after the levels function is used. I supply an example in which a factor with values "BC" "AD" (in that order) is unintentionally re-alphabetized by the levels function. To me, this is very bad behavior. Would you agree? #

I'm working on predict.survreg and am confused about xlevels. The model.frame method has the argument, but none of the standard methods (model.frame.lm, model.frame.glm) appear to make use of it. The documentation for model.matrix states: xlev: to be used as argument of model.frame if data has no "terms" attribute. But the terms attribute has no xlevels information in it, so I

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

all.vars works fine, EXCEPT, it give a bit too much. I only want the regression variables, but in the following example I also get "k" the variable holding the chosen knots. Any machinery to find only "real" regression variables? cheers, Bendix library( splines ) y <- rnorm(100) x <- rnorm(100) k <- -1:1 ml <- lm( y ~ bs(x,knots=k) ) mg <- glm( y ~

I would like to extract the names, modes [numeric/factor] and levels of variables needed in a data frame supplied as newdata= argument to predict.glm() Here is a small example illustrating my troubles; what I want from (both of) the glm objects is the vector c("x","f","Y") and an indication that f is a factor: library( splines ) dd <- data.frame( D =

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

Dear all, I have discovered that there are many things that I used to do in my GIS which are easily done directly in R, for example calculating interpoint distances using geoR and pick out points inside a polygon using splancs. I now wonder, is there a function to create a line object like a watercourse and then calculate the distances between many points in space and this line? I couldn't

I was looking at how the model.frame method for lm works and comparing it to my own for coxph. The big difference is that I try to retain xlevels and predvars information for a new model frame, and lm does not. I use a call to model.frame in predict.coxph, which is why I went that route, but never noted the difference till now (preparing for my course in Nashville). Could someone shed light

Hello, I have tried reading the documentation and googling for the answer but reviewing the online matches I end up more confused than before. My problem is apparently simple. I fit a glm model (2^k experiment), and then I would like to predict the response variable (Throughput) for unseen factor levels. When I try to predict I get the following error: > throughput.pred <-

Andy, OSOL 2009.06 been installed in stub domain (hvm domain) at mentioned Dom0 with 2.6.30 xenified kernel via xen-patches-2.6.30-2.tar.bz2 refuse to activate PV drivers. When same Dom0 starts with 2.6.29.4 xenified kernel (xen-patches-2.6.29-6.tar.bz2) kernel OSOL 2009.06 in stub (hvm) domain does activate PV drivers for xdf, xnf . Boris