search for: mymods

Hello R Developers, This is my first foray into using c-code with R, so please forgive my foolishness. I had a look at the archives and did not find anything on this, so hopefully I am not doubling up. I have tried to use R cmd to create an object file from the odesolve dynload example. I am using windows and have just installed rtools, and have the latest version of stable R (2..7.2). This is

I have been trying to make use of the odesolve library on my university's Linux grid - currently R version 2.0.1 is installed and the system runs 64-bit Scientific Linux based on Redhat. I cannot seem to get lsoda working when I define the model as a shared C library. For example, the following snippet uses the mymod.c example bundled with the package: ### START rm(list=ls())

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

On Sun, Mar 18, 2012 at 09:52:12PM -0700, Gregory Szorc wrote: > The automatic generation of the Python ctypes interfaces using the Clang > Python bindings is pretty friggin cool! A nice side effect is that everything is added to the interface. So it is easy to add a small proxy over the lib that shows which parts of the llvm-c API that is exercised by the tests. (have that in my bindings)

I've been working on a wrapper for LLVM's C++ API to use from Objective-C for a scripting language I'm working on. I currently have an issue with passing arguments to a function that takes a struct argument. typedef struct _test_struct { int x; int y; } test_struct; id testLLVMStructFuncCall(test_struct x) { NSLog(@"%d %d",x.x,x.y); return N(x.x + x.y); }

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

On Mon, Mar 19, 2012 at 10:44 AM, Anders Waldenborg <anders at 0x63.nu> wrote: > > * Auto generated vs manual ctypes declarations. > This is purely a cosmetic difference, as we both take the same approach of registering functions on a global/shared ctypes library instance. I think automatic is the way to go (just as long as the automatically generated code is easy to diff when

On 3/17/2012 4:14 PM, Anders Waldenborg wrote: > FYI: > > I've also been working on new python bindings. > > My bindings are written using ctypes (just like the in-tree > clang/cindex bindings). Most of Core.h is bound, and stuff from > ExecutionEngine.h, Analysis, BitReader, BitWriter. The have fairly > good test coverage (using nosetests). The ctypes definitions are

At Fri, 16 Mar 2012 14:12:08 +0100, Christoph Grenz wrote: > > Hello, > > Am Donnerstag, 15. März 2012, 21:15:02 schrieb Gregory Szorc: > > There was some talk on IRC last week about desire for Python bindings to > > LLVM's Object.h C interface. So, I coded up some and you can now find > > some Python bindings in trunk at bindings/python. Currently, the >

The cautions people have given about starting values are worth heeding. That nlxb() does well in many cases is useful, but not foolproof. And John Fox has shown that the problem can be tackled very simply too. Best, JN On 2023-08-19 18:42, Paul Bernal wrote: > Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. > > Cheers, > Paul > > El El

Hi, Suppose we have thousands of functions in a module but only want to exclude a few patterns. In this case, it becomes impractical trying to list all probe patterns except those few you want to exclude. One way to deal with this is to use [! to exclude certain characters. For example, if we construct the clause below, we can exclude function patterns with leading "xyz":

It is frustrating to see the labels I want in the dimensions of a list but not be able to extract those labels into titles for plots generated from component objects. If someone could set me straight, I would appreciate it. For your amusement, I have provided an example of the Byzantine code I am currently using to avoid loops: # Simulate ANOVA type test data sex<-c(rep(1,8),rep(0,8))

Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. Cheers, Paul El El s?b, 19 de ago. de 2023 a la(s) 3:35 p. m., J C Nash < profjcnash at gmail.com> escribi?: > Why bother. nlsr can find a solution from very crude start. > > Mixture <- c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23, 20, 6, 13, 21, 3, 18, 15, > 26, 8, 22) > x1 <- c(69.98, 72.5,

Inthe code below I was trying to to obtain the GMM estimates for CAPM (REGRESSION) for 36 stocks each have 180 observations,however it only gives me one output rather than 36. In SAS i would just put in a *By statement*. I have a variable TICKER that categorize them into 36 groups. *How can I obtain all 36 output instead of just one.* **

I have *not* tracked this bug very far because I hope someone who is more familiar with the glm/anova code will jump in. If one fits a glm model with an offset, the NULL deviance is incorrectly calculated (it is calculated without the offset). The means one can get examples where the model deviance is greater than the NULL deviance. Here is some example output, (without the data). >

I have *not* tracked this bug very far because I hope someone who is more familiar with the glm/anova code will jump in. If one fits a glm model with an offset, the NULL deviance is incorrectly calculated (it is calculated without the offset). The means one can get examples where the model deviance is greater than the NULL deviance. Here is some example output, (without the data). >

Dear friends, Hope you are all doing well and having a great weekend. I have data that was collected on specific gravity and spectrophotometer analysis for 26 mixtures of NG (nitroglycerine), TA (triacetin), and 2 NDPA (2 - nitrodiphenylamine). In the dataset, x1 = %NG, x2 = %TA, and x3 = %2 NDPA. The response variable is the specific gravity, and the rest of the variables are the predictors.

This question pertains to setting up a model in the package "dlm" (dynamic linear models, http://cran.r-project.org/web/packages/dlm/index.html I have read both the vignette and?"An R Package for Dynamic Linear Models" (http://www.jstatsoft.org/v36/i12/paper), both of which are very helpful. There is also some discussion at

Suppose x and y are numeric vectors of the same length. plot(x,y) #scatterplot lmObj1 <- lm(y~x) # best fit line abline(lmObj1) # good lmObj2 <- lm(x~y) #get best fit but with axes interchanged abline(lmObj2) # not what I want. I want the correct line, drawn on the same graph, but with # response and predictor variables interchanged One way to proceed would be to