similar to: xpred.rpart() in library(mvpart)

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

Hi, That's what the DecoderMethod is for. Similarly ParserMatchClass for the asm parser and PrintMethod for the asm printer: def CondCodeOperand : AsmOperandClass { let Name = "CondCode"; } def pred : PredicateOperand<OtherVT, (ops i32imm, i32imm), (ops (i32 14), (i32 zero_reg))> { let PrintMethod = "printPredicateOperand";

Hello! I'm puzzled by the following problem. It occurs while trying to predict responses in a test-dataset using a linear model fitted with regFit from the rMetrics "fRegression"-package. All goes well when I call "predict" using the training dataset. However, a call using the test-dataset retuns an error message - telling me that the latter dataset provides variables

I just updated rpart to the latest version (3.1.0). There are a number of changes between this and previous versions, and some of the code I've been using with earlier versions (e.g. 3.0.2) no longer work. Here is a simple illustration of a problem I'm having with xpred.rpart. iris.test.rpart<-rpart(iris$Species~., data=iris[,1:4], parms=list(prior=c(0.5,0.25, 0.25))) + ) >

Hello James, that was also what I've planned to do but just wasn't sure. Thanks for that. On Mon, Dec 14, 2015 at 11:52 AM, James Molloy <james at jamesmolloy.co.uk> wrote: > Hi, > > You can't nest operands like that - it must be a flattened list. So: > > def *Xpred* : PredicateOperand<OtherVT, (ops *i32imm, i32imm*, i32imm), > (ops (i32 14), (i32

Hello! I'm still working on my problem, which also occurs with the predict.lm() function. - Providing newdata, which is a data.frame with all variables being "numeric", as str() shows, R tells me the following: ar1.xpred.test.pred <- predict(ar1.xpred.fitted, regdata.test, se.fit = FALSE) Fehler: variable 'lag(ret1)' was fitted with type "numeric" but type

Hi All, In ARMInstFormats.td predicate is defined this way: *def pred : PredicateOperand<OtherVT, (ops i32imm, i32imm),* *(ops (i32 14), (i32 zero_reg))> {...}* I use the same definition in my code. But I have another version of predicate which is exactly the same but it is a condition code plus a quantifier! (e.g. Xpred = (pred + i32imm)). I was wondering how we can define a sub sub

------------------- Version 2 of my problem improving the definition of what the optimal solution would be. Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing

Dear R users, I know cross-validation does not work in rpart with user defined split functions. As Terry Therneau suggested, one can use the xpred.rpart function and then summarize the matrix of the predicted values into a single "goodness" value. I need only a confirmation: set for example xval=10, if I correctly understood a single column of the matrix obatined by xpred.rpart gives

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

Hi I would like to understand how to extend the function (FUN) I am using in rollapply below. ###################################### With the following simplified data, test1 yields parameters for a rolling regression data = data.frame(Xvar=c(70.67,70.54,69.87,69.51,70.69,72.66,72.65,73.36), Yvar =c(78.01,77.07,77.35,76.72,77.49,78.70,77.78,79.58)) data.z = zoo(d) test1 =

Updating gnulib has caused -Wformat-signedness to be enabled. This has revealed many problems in C format strings. The fixes here fall into the following main categories: - Using %d with an unsigned parameter. - %x and %o expect an unsigned argument. - uid_t and gid_t are unsigned on Linux. The safe way to print these is to cast them to uintmax_t and then print then using the %ju

Updating gnulib has caused -Wformat-signedness to be enabled. This has revealed many problems in C format strings. The fixes here fall into the following main categories: - Using %d with an unsigned parameter. - %x and %o expect an unsigned argument. - uid_t and gid_t are unsigned on Linux. The safe way to print these is to cast them to uintmax_t and then print them using the %ju

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

Hi, I created an rpart object and pruned the tree using 1-SE rule. I used 10-fold cross validation while creating the tree. Then, I extracted the cross-validated predictions for my data points using xpred.rpart and obtained some statistics like precision, recall, overall error rate, etc. However, these values change each time I run xpred.rpart because of the random shuffling going on before

library(ROCR) n <- 1000 fitglm <- function(iteration,intercept,sigma,tau,beta){ x <- rnorm(n,0,sigma) ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit)) *int*<-coef(model)[1] *slope*<-coef(model)[2] # when add error you are suppose to get slightly bias slope. However when I change

Hello, I use rpart to predict survival time and have a problem in interpreting the output of ?estimated rate?. Here is an example of what I do: > stagec <- > read.table("http://www.stanford.edu/class/stats202/DATA/stagec.data", > col.names=c("pgtime", "pgstat", "age","eet", "g2", "grade", "gleason", >

Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing effectivelly a number of observations of Q in each state. Q is a continous variable. Now, the two datasets should

Forgot to specify that the cross-val error cannot be decreased lower than 0.91. Note that for smaller values of cp than 0.01, the cross-val error increases. Is the cross-val error sum of squared error or relative error for classification problem (method = "class" in rpart function) or another type of error? Is it possible to determine the true positive, false positive using rpart?

Instead of generating the p2v kernel config using the OCaml generator, create a Perl script to do this job, mostly at build time. This is done to rely less on the generator for p2v, and because the generation of these sources is quick enough that it can be done at build time (instead of shipping the generated sources in dist tarballs). The generate-p2v-config.pl mimics what