similar to: Problem with pasteing formulas (PR#8897)

Hi everyone - I'm trying to do a forward stepwise regression (I've tried both step and stepAIC) inside of a function. I can do it outside the function with no problems (first example in code below). I can also do a backward stepwise regression inside a function (second example), but forward stepwise regression ( third example ) fails with the error: "Error in

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

Hi, I'd like to apply a 2^k factorial design with k=10 parameters. Obviously this results in a quite long term for the model equation due to the high number of combinations of parameters. How can I specify the equation for the linear model (lm) without writing all combinations explicitly down by hand? Does a R command exist for this problematic? Thanks for your help in advance, Sven

Hi, Can you please help me with this please? What I am trying to do is call a vector from R function and used in the new function So I create 4 functions with these arguments M11 <- function(TrainData,TestData,mdat,nsam) { ls <- list() I have few statments one of them is vectx <- c(,1,2,3,4,5,6,6) vectz <- c(12,34,5,6,78,9,90) and then................ ls(vectx=vtecx,vectz=vectz)

Thanks a lot for help in advance. I am switching from matlab to R and I guess I need some time to get rolling. I was wondering why this code : > fit.0 <- lm( Response ~ 1, data = ds3) > step(fit.0,scope=list(upper=~.,lower=~1),data=ds3) Start: AIC= -32.66 Response ~ 1 Call: lm(formula = Response ~ 1, data = ds3) Coefficients: (Intercept) 1.301 is not working

Hello, we have 80 text files with matrices. Each matrix represents a map (rows for latitude and columns for longitude), the 80 maps represent steps in time. In addition, we have a vector x of length 80. We would like to compute a regression between matrices (response through time) and x and create maps representing coefficients, r2 etc. Problem: the 80 matrices are of the size 4000 x 3500 and we

Dear list, given this formula: > fmla <- formula(y1 ~ spp1 + spp2 + spp3 + spp5) > fmla[[3]] spp1 + spp2 + spp3 + spp5 is this the intended behaviour of as.character: > as.character(fmla[[3]]) [1] "+" "spp1 + spp2 + spp3" "spp5" ? Where does the extra "+" come from? > as.character(fmla) [1] "~"

Hi! I had a database with some variables in sequence. Let me say: TX01, TX02, TX03 and TX04. But I need to run some regressions changing the variables... so: variable <- paste("TX0", 1:4, sep="") for(i in 1:4){ test[i] <- lm(variable[i] ~ INCOME, data=database) } But doesn't work... lm tries to find a variable inside database named variable[i] ... Suggestions?

Hi, I have a problem in getting step function work. I am getting the following error: > fit1 <- lm(Response~1) > fmla <- as.formula(paste(" ~ ",paste(colnames,collapse="+"))) > sfit <- step(fit1,scope=list(upper= fmla,lower= ~1),k=log(nrow(dat))) Start: AIC= -1646.66 Response ~ 1 Error in "contrasts<-"(`*tmp*`, value =

Dear all, I am working on a function formula.design that should automatically generate reasonable lm formulae for a number of different designs. All works well as long as all variables used are columns of the design data frame. For one function, I would like to incorporate a dummy variable for center points that is not a column of the design. Without this function, it would work like this (at

I want use survfit() and basehaz() inside a function, but it doesn't work. Could you take a look at this problem. Thanks for your help. Following is my codes: library(survival) n <- 50 # total sample size nclust <- 5 # number of clusters clusters <- rep(1:nclust,each=n/nclust) beta0 <- c(1,2) set.seed(13) #generate phmm data set Z <- cbind(Z1=sample(0:1,n,replace=TRUE),

Hi, I am trying to build a series of rlm models. I have my data frame and the models will be built using various coulmns of the data frame. Thus a series of models would be m1 <- rlm(V1 ~ V2 + V3 + V4, data) m2 <- rlm(V1 ~ V2 + V5 + V7, data) m3 <- rlm(V1 ~ V2 + V8 + V9, data) I would like to automate this. Is it possible to use a string in place of the formula? I tried doing: fmla

I am attempting to create a formula using as.formula for a PLS analysis. I have used the code below successfully, but in a previous R version and with many fewer predictors. Any help getting all of these predictors into one formula would be greatly appreciated. TC.fmla <- as.formula(paste("TC ~ ", paste(vars, collapse= "+"))) As I mentioned, this code worked fine in a

Hi, I'm trying to compile half precision program for ARM, while it seems LLVM fails to automatically generate fused-multiply-add instructions for c += a * b. I'm wondering whether I did something wrong, if not, is it a missing feature that will be supported later? (I know there're fp16 FMLA intrinsics though) Test programs and outputs, $ clang -O3 -march=armv8.2-a+fp16fml

Greetings I want to take a fitted regression and replace all uses of a variable in a formula. For example, I'd like to take m1 <- lm(y ~ x1, data=dat) and replace x1 with something else, say x1c, so the formula would become m1 <- lm(y ~ x1c, data=dat) I have working code to finish that part of the problem, but it fails when the formula is more complicated. If the formula has log(x1)

Greetings. I've written a convenience function for multicollinearity diagnosis. I'd like to report to the user the formula that is used in a regression. I get output like this: > mcDiagnose(m1) [1] "The following auxiliary models are being estimated and returned in a list:" [1] "`x1` ~ ." formula(fmla)() [1] "`x2` ~ ." I'd like to fill in the period

Thanks for reply. I was using LLVM 8.0. Let me try trunk and will let you know if it works. On Wed, Sep 4, 2019 at 11:19 PM Sjoerd Meijer <Sjoerd.Meijer at arm.com> wrote: > > Hi, > Which version of Clang are you using? I do get a "vfma.f16" with a recent trunk build. I haven't looked at older versions and when this landed, but we had an effort to plug the remaining

hi netters i have a vector NAMES containing a series of variable names: NAMES=c(x,r,z,m,st,qr,.....nn). i wanna fit a regression tree by using the code: my.tree<-tree(y~x+r+z+m+....nn,my.dataframe) but i don't want to type out "x+r+z+m+....+nn" one by one, as there are so many variables. besides, sometimes i wanna put the code in a function. so i need to have the

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

I would like to ask for advice from R experts about the benefits or dangers of using attr to return information with an object that is returned from a function. I have a feeling as though I have cheated by using attributes, and wonder if I've done something fishy. Maybe I mean to ask, where is the dividing line between attributes and instance variables? The separation is not clear in my mind