similar to: predict.lm() out-of-sample predictions - problem with data classes

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

One of the reports recently was of predict.lm misbehaving if newdata=data.frame(x=rep(NA, 10)) was given a logical column when it had been fitted as a numeric one. The exact problem was because model.matrix was trying to handle a 0-level factor (which is what that logical column got converted to by contrasts<-). However, the problem is more general and I have added to R-devel a layer of

Below is a SIMEX object that was generated with the "simex" function from the "simex" package applied to a logistic regression fit. From this mountain of information I would like to extract all of the values summarized in this line: .. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... Can someone suggest how to go about doing this? I can extract the

I'm trying to get started with maximum likelihood estimation with a simple regression equivalent out of Bolker (Ecological Models and Data in R, p302). With this code: #Basic example regression library(bbmle) RegData<-data.frame(c(0.3,0.9,0.6),c(1.7,1.1,1.5)) names(RegData)<-c("x", "y") linregfun = function(a,b,sigma) { Y.pred = a+b*x

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";

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))) + ) >

I imported a spreadsheet into a variable sh e.g. sh$aaaa, sh$bbbb, etc... doing the following: tsSource <- ts(paste("sh$",NAMEVARIABLE,sep="") ... ) fails. The paste isn't evaluating properly. What is the proper way to concatenate a data source with a member name such that they evaluate properly. actual code below: doEnv <-

R-users E-mail: r-help@r-project.org Hi! R-users. http://finzi.psych.upenn.edu/R/library/mvpart/html/xpred.rpart.html says: data(car.test.frame) fit <- rpart(Mileage ~ Weight, car.test.frame) xmat <- xpred.rpart(fit) xerr <- (xmat - car.test.frame$Mileage)^2 apply(xerr, 2, sum) # cross-validated error estimate # approx same result as rel. error from printcp(fit) apply(xerr, 2,

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

Hi there! I'm stuck with a problem aligning financial timeseries and haven't found a cue how to fix it... When I run that simple script, everything goes well until the "align"-command: ------ rm(list=ls()) x <- yahooSeries("^GDAXI") head(x) xAligned <- align(x = x, by = "1d", method = "before", include.weekends = FALSE) ------ Here's

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

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

The in-kernel nfsroot code is obsoleted by kinit. Remove it; it causes conflicts. Signed-off-by: H. Peter Anvin <hpa at zytor.com> --- commit 161e1dc16ec1129b30b634a2a8dcbbd1937800c5 tree c30da837d746fe65d8a13ccf6f27bd381948edb4 parent 018604e070e143657abcf0cb256a1e2dda205d97 author H. Peter Anvin <hpa at zytor.com> Sat, 20 May 2006 16:24:05 -0700 committer H. Peter Anvin <hpa at

I posted this one as an R bug (https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=14767), but Prof. Ripley says I'm premature, and I should raise the question here. Here's the behavior I assert is a bug: The output from delete.response on a terms object alters the formula by removing the dependent variable. It removes the response from the "variables" attribute and it changes

Dear r-developers, on January 23rd I reported on this list a dramatic performance loss of R1.8 compared to previous versions. As I did not receive any answer for this important observation, I try again with some more recent results. The slowdown happens when manipulating parts of objects in other environments. This severely impedes Henrik Bengtsons oo extensions and usage of my package ref. It

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

Dear All, I have a question regarding predict.rpart. I use rpart to build classification and regression trees and I deal with data with relatively large number of input variables (predictors). For example, I build an rpart model like this rpartModel <- rpart(Y ~ X, method="class", minsplit =1, minbucket=nMinBucket,cp=nCp); and get predictors used in building the model like

Hi, I work with hyperspectral remote sensing data and I try to built a pls model with this data. I already built the model but if I try to calculate the RMSEP and R2 with a test data set I get the following error message: Error: variable 'subX' was fitted with type "nmatrix.501" but type "nmatrix.73" was supplied The problem is that I don't get the message for

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 =

Someone sent me a bug report for survival2.44.1-1 that involves a model with both cluster and offset.? It turns out to be a 3 part issue with [.terms and my own untangle.specials routine.?? I've spent an evening sorting out the details. ? 1. The delete.response() function doesn't remove the response from the dataClasses attribute, which leads to a later failure in [.terms for