similar to: scoping problem when calling step inside a function

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

Hi, If I create a formula with say 100 terms and then paste it: xnam <- paste("x", 1:100, sep="") fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+"))) paste(fmla) The result seems to cut off everything after the first 500 characters and gives no warning message. I have the most recent version of R from the R website and the problem occurs

Are the results dramatically different? The result would be expected to be somewhat different, as setting importance=TRUE would make many calls to the random number generator (for permuting OOB data in each variable), making all but the first tree in the forest different than if importance=FALSE. Cheers, Andy > From: Scott Gilpin > > Hi - > > When using the randomForest

Hi - When using the randomForest function for regression, I get different results for mean-squared error of the predictions depending on whether or not I specify to calculate variable importance. There is an example below. I looked briefly at the source code, but couldn't find anything that would indicate why calculating variable importance would (or should) change predictions. I'm

Dear R-Help, My problem/bug came to light,when fitting a linear model using stepwise selection. I'd started with the straightfoward command step(lm(y~., dataset)) This worked fine, but because this starts with all the possible explanatory variables, it results in a model with too many explanatory variables. Hence I wanted to start with just a constant and do forward selection, to get a

I'm not familiar with Solaris, so take this with appropriate dose of NaCl... For the 64-bit build, why not have the -O2 for gcc, since you have it for g77 and g++? If you just run vanilla configure for the 32-bit build, I believe it uses -O2 for all three compilers by default. If that's the difference, perhaps it's sufficient to explain the performance difference. The other thing

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 =

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),

Hello, I am using the function step.gam() from the 'gam' package (header info from library(help=gam) included below) and have come across some behavior that I cannot understand. In short, I have written a function that 1) creates a dataframe, 2) calls gam() to create a gam object, then 3) calls step.gam() to run stepwise selection on the output from gam(). When I do this, gam()

Hello, I have a question pertaining to the stepwise regression which I am trying to perform. I have a data set in which I have 14 predictor variables accompanying my response variable. I am not sure what the difference is between the function "mle.stepwise" found in the wle package and the functions "step" or "stepAIC"? When would one use

Hello all, I am trying to use stepwise procedure to select covariates in Cox model and use bootstrap to repeat stepwise selection, then record how many times variables are chosen by step() in bootstrap replications. When I use step() (or stepAIC) to do model selection, I got errors. Here is the part of my code for (j in 1:mm){ #<--mm=10 for (b in 1:nrow(reg.bs)){ #<--bootstrap 10

Dear all, I cannot seem to get the R functions step or stepAIC to perform forward or stepwise regression as I expect. I have enclosed the example data in a dataframe at the end of this mail. Note rubbish is and rnorm(17) variable which I have deliberately added to the data to test the stepwise procedure. I have used wateruse.lm<-lm(waterusage~.,data=wateruse) # Fit full model

Thank you for Prof.Ripley's suggestion. I fixed the program by adding a lower scope, and the program ran, but I still got warning messages, and don't know what is going on, would this affect my results? ... Step: AIC= 12337.74 Surv(tlfup, cen) ~ MI[[j]]$trt + MI[[j]]$agem40 + MI[[j]]$agem40sq + mhtypeed1 + mhtypeed2 Df AIC <none> 12338 -

In species distribution modeling where one uses a large sample of background points to capture background variation in presence\pseudo-absence or use\available models (0\1 response) it is frequently recommended that one weight the data so the sum of the absence weights is equal to the sum of presence weights so that the model isn?t swamped by an overwhelming and arbitrary number of background

hello I built logistic regression model. To model check I used stepAIC. But I don't know how it is interpreted . I could not any find any explanation about it For instance which model is preferable ? What are the critarias to choose beter model I will appreciate if you give me an explanation ? models --------- > lo1.step$anova Stepwise Model Path Analysis of Deviance Table Initial

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?

Greetings -- I'm programming a data mining system in R for protein structural data. As a seasoned Perl and Python and Ada and ML, et al., programmer, I am severely befuddled by the environment problem, where data is not found in a 3rd level nested function. I did peruse the parent frame not on the search path idea, and came up with a hack which kinda works, also below with the code which

All, Could anyone recommend a package that allows the user to constrain the coefficients from a multiple regression equation? I tried using the gl1ce function in lasso2, but couldn't get it to work. I created a contrived example to illustrate my starting point. data(cars) fmla <- formula(dist ~ speed) gl1c.E <- gl1ce(fmla, data = cars) gl1c.E gl1c.E <- gl1ce(fmla, data =

Could someone point me in the right direction for the following issue: A function is defined as follows: tfun <- function(dat) { fmla <- as.formula("y~x+z") dat2 <- dat mdl <- lm(fmla,dat2) mdl <- step(mdl) } Then the following code dat <- data.frame(x=1:10,z=1:10,y=(1:10)^2+10*(1:10)) tfun(dat) generates the output Start: AIC= 43.67

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] "~"