similar to: plot gam "main effect functions" in one graph

Hi, I'm trying to work through the examples and code in Loader's LOCAL REGRESSION AND LIKELIHOOD, and have run into a problem with the code for one sided smoothing and change point analysis (p. 110-112). The code, after loading locfit: midp<-(1945:1988)+0.5 fitl<-locfit(thickness~left(year), data=penny, alpha=c(0,10), deg=1, ev=midp)

Hi all user R, My simple question is...I have a vector of names of predictors, text<-c("datem","cola","eslom")...I try to plot the model with this predictor in sequence loop...., for(i in 1:3){ png(paste("fig_",i,sep="")) plot(preplot.gam(mod9)[[i]],se=T,rug=F,main="",xaxt="n",ylab="",xlab="")

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

Hi all, I would like to export the smooth function estimate I got from gam to plot it in another graphics software. In S-plus I use the function preplot() for that, but it seems not to work in R. Has somebody an idea how to solve that? Thanks Stephanie ******************************** Stephanie von Klot Institut f?r Epidemiologie GSF - Forschungszentrum f?r Umwelt und Gesundheit Ingolst?dter

We have a test that looks like this… define void @array16_store() { ; CHECK-LABEL: array16_store: ; CHECK: ldi [[REG1:r[0-9]+]], 204 ; CHECK: ldi [[REG2:r[0-9]+]], 170 ; CHECK: sts int.array+3, [[REG2]] ; CHECK: sts int.array+2, [[REG1]] ; CHECK: ldi [[REG1:r[0-9]+]], 187 ; CHECK: ldi [[REG2:r[0-9]+]], 170 ; CHECK: sts int.array+1, [[REG2]] ; CHECK: sts int.array, [[REG1]] ; CHECK: ldi

Hi all, In several of our tests, I have noticed that the register allocator allocates to virtual registers in a different order when compiling with the clang option -g. Before entering the register allocator, the code is identical when compiling with and without -g (with the exception of " DBG_VALUE" instructions). The only difference I can see is the value assigned to the slot index

Hi All, I am interested in creating a function that will take x number of lm objects and automate the comparison of each model (using anova). Here is a simple example (the actual function will involve more than what Im presenting but is irrelevant for the example): # sample data: id<-rep(1:20) n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)

I’m not sure if it’s truly deterministic. It always gives the same results (so far) on my machine but I’m not sure that’s enough. My guess is it’s probably going to be deterministic on one machine but might well not be deterministic across environments. Like it might give varying results if cross compiled on different hosts, macOS vs intel Linux vs arm vs s390. (Obviously AVR is always a cross

Hi list, in our LLVM-based-project we are writing a backend for our processor. The architecture is a quite straight-forward RISC, but it does not have hardware interlocks, i.e. data hazards involving memory access must be resolved by the compiler, either by scheduling unrelated instructions or by inserting NOOPs into the load delay slots: ---- For example, code which looks like that: load

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

On Jan 19, 2009, at 9:17 AM, Patrick Boettcher wrote: > Hi list, > > in our LLVM-based-project we are writing a backend for our > processor. The > architecture is a quite straight-forward RISC, but it does not have > hardware interlocks, i.e. data hazards involving memory access must be > resolved by the compiler, either by scheduling unrelated > instructions or >

I've been out of town, and too hastily deleted some earlier messages from this thread.... > > > Dear friends. I would like to link an object file made in C. > > Dyn.load rather wants to link a whole library and I only wanted to > > link this small object file. I followed instructions given for S - > > by Clive Loader in his software for changepoint handling and >

> On Feb 2, 2017, at 8:20 AM, David Blaikie <dblaikie at gmail.com> wrote: > > The goal/intent is that debug info does not affect code generation. There are (many?) bugs. I think Apple folks (cc'd Adrian) may be looking at this a bit recently, not sure. > > The fixes aren't usually too invasive (usually involve something counting instructions where it needs to skip

Each of the data sets contains monthly observations on price indices for 7 countries. I use the fitted values from reg1 in the reg2 model. The interior loop executes without error as long as I explicitly specify the data set, i.e. data=dat70. However the code fails to execute if I specify the model in the form of the commented line, i. e reg1 <-dynlm(form1,data=Dnames[j]) I get the following

> On Feb 2, 2017, at 8:20 AM, David Blaikie via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > The goal/intent is that debug info does not affect code generation. There are (many?) bugs. I think Apple folks (cc'd Adrian) may be looking at this a bit recently, not sure. > > The fixes aren't usually too invasive (usually involve something counting instructions where

Dear R Users, I am looking for an alternative to AIC or BIC to choose model parameters. This is somewhat of a general statistics question, but I ask it in this forum as I am looking for a R solution. Suppose I have one dependent variable, y, and two independent variables, x1 an x2. I can perform three regressions: reg1: y~x1 reg2: y~x2 reg3: y~x1+x2 The AIC of reg1 is 2000, reg2 is

Hello, I'm writing a backend for an architecture that only has LOAD Instructions that first copy the old value of the target register in another register and after that load the provided value into the register. Example of an addition: load a, reg1; // -> copies old value of reg1 in reg2 and loads value from a into reg1 load b, reg1; // -> copies old value of reg1 in reg2 and loads

To generate code for a target which doesn't have immediate constant as instruction operand, do I (the target specific back-end, XXXTarget) need to provide code to break up the SDNode with constant (like ISD::ADD $reg1, #1) to 2 SDNodes (ISD::LOAD $reg2, #1; ISD::ADD $reg1, $reg2) in my XXXTargetLowering::LowerOperation, or LLVM target-independent framework can do such transformation

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

I have a puzzle.... When I include an interaction in the model, many predicted probabilities are above 1. Is that a problem with my model? I thought the predicted prob can't be bigger than 1... Any help would be really appreciated! Thanks! K. reg1<-glm(pyea~male+edu+married+inc+relig+factor(time)+ factor(time)*male, data=mydata, family=binomial(link="logit"))