similar to: random binary trees in R

Hi dear R-help memebers, When building a CART model (specifically classification tree) using rpart, it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) variables - while other predictors are relevant for other outcome variables (y's only). *How can it be estimated, which explanatory variable is "used" for which of

Dear R helpers   I am having following data   Name           Numbers A                  25 B                   3  C                  13 A                   5  B                   7 C                   0 A                   2 B                 10 C                   9   CONDITIONS   If Name is A, min_val = 1.05, max_val = 1.30 If Name is B, min_val = 1.30, max_val = 1.60 If Name is C, min_val =

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

HI: I need ideas on how to make this code shorter (maybe with a second loop?). The code as it is works, but in this case I only have 14 samples, but it will become insane with more, so I need a way to make it more automatic. The problem is that the output from ts1, ts2, and so on is a vector with more than one value, so I do not know how to solve this. Thanks Prenewbie The code is the

Hi all, For some odd reason when running na?ve bayes, k-NN, etc., I get slightly different results (e.g., error rates, classification probabilities) from run to run even though I am using the same random seed. Nothing else (input-wise) is changing, but my results are somewhat different from run to run. The only randomness should be in the partitioning, and I have set the seed before this

In case you don't get an answer from someone more knowledgeable: 1. I don't know. 2. But it is possible that other packages that are loaded after set.seed() fool with the RNG. 3. So I would call set.seed just before you invoke each random number generation to be safe. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking

Hello! I written several implementations in R of R?my's algorithm for generating random ordered strictly binary trees on 2n+1 vertices. One implementation involves manufacturing character strings like: "X <- list(0,list(0,0))" for the case n=2. If I perform the following two steps: cmd <- "X <- list(0,list(0,0))" eval(parse(text=cmd)) then X becomes a true

Thank you, everybody, who replied! I appreciate your valuable advise! I will move the location of the set.seed() command to after all packages have been installed and loaded. Best regards, Gary Sent from my iPad > On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> >

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

hello people, I want to make a biased dice using the sample() function and print out the results after n number of runs, I've successfully generated the dice using the following command: mydie2<-function(n=1000,y=NULL,...){ for(i in 1:n){ x<-sample(1:6,n,replace=TRUE,prob=c(1,1,2,3,2,1)/10) x=runif(n) if(x<=1/10){y[i]=1} else if(x<=2/10){y[i]=2} else if(x<=4/10){y[i]=3} else

The following helps identify when .GlobalEnv$.Random.seed has changed: rng_tracker <- local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed") last <<- curr } TRUE } }) addTaskCallback(rng_tracker, name = "RNG tracker") EXAMPLE: >

Hello all, This is one of those "Is there a better way to do this questions". Say I have a dataframe (df) with a grouping variable (z). This is my base data. Now I know that there is a higher order level of grouping that exist for my group variable. So what I want to do is create a new column that express that higher order level of grouping based on values in the sub-group (z in this

Hi for the loop section runif needs curved brackets Try IAP <-NA for (i in 1:Sample.Size){ if (DataSet$SES[i]>0) { IAP[i] <- ifelse(runif(1)>0.75, 1, 0) # High SES, higher chance to be in Treatment # } else { IAP[i] <- ifelse(runif(1)<=0.25, 1, 0) # Low SES, lower chance to be in Treatment # } } # End loop # IAP IAP zjiaqi19880219 wrote > Hi,

Hi, I have the following data: Path_Number <- 5 ID.Path <- c(1:Path_Number) # Make vector of ID's. No_of_X <- sample(50:550, length(ID.Path), replace=TRUE) # X <- split(sample(1:10000, sum(No_of_X), replace=TRUE), rep(ID.Path, No_of_X)) Y <- lapply(X,function(x) sample(x, round(runif(1, min=10, max=50)))) X and Y are both lists, and I've made the following function to

There are a couple of races in sftp-server as this patch shows: --- sftp-server.c 28 Jan 2003 18:06:53 -0000 1.1.1.2 +++ sftp-server.c 5 Feb 2003 19:19:42 -0000 @@ -832,19 +832,22 @@ process_rename(void) { u_int32_t id; - struct stat st; char *oldpath, *newpath; - int ret, status = SSH2_FX_FAILURE; + int status; id = get_int(); oldpath = get_string(NULL); newpath = get_string(NULL);

Hi, I have written the following code which works fine step<-5 numSim<-15 N<-double(numSim) A<-double(numSim) F<-double(numSim) M<-double(numSim) genx<-double(numSim) for (i in 1:numSim) { N[i]<-20 PN<-(runif(N[i], 0, 1)) A[i]<-sum(ifelse(PN>0.2, 1, 0)) PF<- runif((A[i]*0.5), 0, 1) F[i]<-sum(ifelse(PF>0.2, 1, 0)) PM<-

Hi all I do not understand why I am getting the following error message. Can anybody help me with this? Thanks in advance. install.packages("cmprsk") library(cmprsk) result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 ) one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2)) predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2)) Error: could not find function

Dear all, I'm looking for a function comparable to switch, to categorize a continuous variable in a few levels. Off course that can be done with a series of ifelse statements, but that looks rather clumsy. I looked at switch, but couldn't figure out how to use it for this. I guess that's not possible, as it only works with characters or integers, not with intervals. Basically,

On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: > The following helps identify when .GlobalEnv$.Random.seed has changed: > > rng_tracker <- local({ > last <- .GlobalEnv$.Random.seed > function(...) { > curr <- .GlobalEnv$.Random.seed > if (!identical(curr, last)) { > warning(".Random.seed changed") > last <<- curr

Hi all, I'm trying to add single points with known coordinates to a level plot, but could not find the proper answer. I got to know that layer() function is good for this, but I don't know which package is related to this function. The source is here: https://stackoverflow.com/questions/28597149/add-xy-points-to-raster-map-generated-by-levelplot but my question is a little different as I