similar to: Polymorphism of predict

In help(gc) I read, "...the primary purpose of calling 'gc' is for the report on memory usage". What memory usage does gc() report? And more importantly, which memory uses does it NOT report? Because I see one answer from gc(): used (Mb) gc trigger (Mb) max used (Mb) Ncells 14875922 794.5 21754962 1161.9 17854776 953.6 Vcells 59905567 457.1 84428913 644.2

> Why do you say that people who compile, e.g., functional languages > would benefit from type variables in LLVM? > I like the level the LLVM is at, and would prefer to deal with > instantiating parametric polymorphism at a higher level. I'm surprised you're happy with a non-polymorphic llvm. Does Cayenne target llvm? Dependent types take polymorphism to new heights -- but

Why do you say that people who compile, e.g., functional languages would benefit from type variables in LLVM? I like the level the LLVM is at, and would prefer to deal with instantiating parametric polymorphism at a higher level. On Wed, Feb 18, 2009 at 10:43 PM, DeLesley Hutchins <delesley.spambox at googlemail.com> wrote: >> I think many people were confused by this at first but an

I'm using R 1.7.0 on linux. With this version of R the package modreg is automatically loaded at start of session. However attempting to use predict.smooth.spline() produces Error: couldn't find function predict.smooth.spline. The function smooth.spline() is OK. What am I missing? ====================================== I.White ICAPB, University of Edinburgh Ashworth Laboratories, West

Hi, forget about the below details. It is not related to the fact that the function is returned from a function. Sorry about that. I've been troubleshooting soo much I've been shoting over the target. Here is a much smaller reproducible example: x <- 1:10 y <- 1:10 + rnorm(length(x)) sp <- smooth.spline(x=x, y=y) ypred <- predict(sp$fit, x) # [1] 2.325181 2.756166 ...

> I think many people were confused by this at first but an excellent counter > example was provided in a previous thread: C99 ABIs can require that struct > return values are returned via a pointer to a preallocated struct passed as > an auxiliary argument *except* when you're talking about a C99 complex, in > which case the return value is conveyed in a completely different

> I think the problem is deeper than that, in that LLVM has no official > concept of a subtype, so I don't see how the idea of polymorphism > could be defined in it. Parametric polymorphism is different from subtype polymorphism; you can have one without the other. Parametric polymorphism just means that you can use type variables (like T) in the IR, which are later instantiated to

Hi, I've noted that not all `predict' methods are hidden in the namespace: > methods("predict") [1] predict.ar* predict.Arima* [3] predict.arima0* predict.glm [5] predict.HoltWinters* predict.lm [7] predict.loess* predict.mlm [9] predict.nls* predict.poly [11] predict.ppr* predict.prcomp* [13]

I'm a newcomer to llvm, but what you've done so far is very impressive. Llvm is a godsend to anybody who is attempting to implement their own their own language. :-) My company is considering using llvm as the backend for a small matlab-like language for scientific computation; our other option is MSIL. After reading through the documentation, I noticed that llvm seems to have one major

Ticket 4052 (http://dev.rubyonrails.org/ticket/4052) just came through trac, which introduces the need for a better way to access fixtures. I believe the basic problem is the same as 3935 (http://dev.rubyonrails.org/ticket/3935) in that the accessor method which is constructed by the fixture call can''t infer the class name from the table name. The band-aid in 3935 was to allow you to

Hi, I'm having trouble seeing the added value over functions defined by setGeneric vis-a-vis methods defined by inheritance and polymorphism. setGeneric offers a 'clean' call to a generic function, ie. no need to call new(), so less typing to do for the user. But such explicit calls can also be avoided by functions like f <- function(x, y) new(Class = SomeClass, x=x, y=y). Then

Hello, despite many readings I still don''t understand how to solve my problem. I have Publication, Picture, File classes A Publication can be related (a one way relation, like a www link) with another Publication, Picture or File I tried this : Publication : has_many :related_elements, :dependent => :destroy, :foreign_key => :publication_id has_many :related, :through =>

I'm sure I'm doing something completely boneheaded here, but I've used this idiom (constructing an interpolation spline and using prediction from a backSpline to find an approximation profile confidence interval) many times before and haven't hit this particular problem: r2 <- c(1.04409027570601, 1.09953936543359, 1.15498845516117, 1.21043754488875,

With ref to my previous post: http://www.ruby-forum.com/topic/180356 I am now able to access items from topics controller. However, now I need to add sub-item e.g.: An item itself could have many sub-items. So my items model is like: class Item < ActiveRecord::Base validates_uniqueness_of :title, :scope => [:topic_id] validates_presence_of :topic_id, :title, :owner, :position

At present R has separate functions "spline" and "splinefun". The first of these carries out spline interpolation of a data set and returns the interpolated values; the second returns the interpolating function itself (approx and approxfun are similar). I would like to combine these into a single function "spline" with an (optional) argument which determines which

I am fittling a spline to a variable in a regression model, I am then using the predict.glm funtion to make some predictions. When I use bs to fit the spline I don't have any problems using the predict.glm function however when I use ns I get the following error: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for

Here I think S3 dispatch is very natural. Try the following: page <- function(x, method = c("dput", "print"), ...) UseMethod("page") page.getAnywhere <- function(x, ..., idx=NULL) { name <- x$name; objects <- x$obj; if (length(objects) == 0) stop("no object named '", name, "' was found"); if (is.null(idx)) {

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

Hi, If I set df=2 in my smooth.spline function, is that equivalent to running a linear regression through my data? It appears that df=# of data points gives the interpolating spline and that df = 2 gives the linear regression, but I just want to confirm this. Thank you, Steven