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Hello, I?m with a problem in using nonlinear quantile regression, the function nlrq. I want to do a quantile regression o nonlinear function in the form a*log(x)-b, the coefficients ?a? and ?b? is my objective. I try to use the command: funx <- function(x,a,b){ res <- a*log(x)-b res } Dat.nlrq <- nlrq(y ~ funx(x, a, b), data=Dat, tau=0.25, trace=TRUE) But a can?t solve de problem, How I put the formula ?y ~ funx(x,a,b)?? _________________________________________________________________ MSN Busca: f?cil, r?pido, direto ao po...

...lt;- gl(2,5) by(X, g, colMeans) Suggested fix: --- by-old.R 2007-12-10 15:26:22.501086600 +0100 +++ by.R 2007-12-10 15:25:58.390477200 +0100 @@ -26,7 +26,7 @@ IND[[1]] <- INDICES names(IND) <- deparse(substitute(INDICES))[1] } else IND <- INDICES - FUNx <- function(x) FUN(data[x,], ...) + FUNx <- function(x) FUN(data[x, , drop=FALSE], ...) nd <- nrow(data) ans <- eval(substitute(tapply(1:nd, IND, FUNx)), data) attr(ans, "call") <- match.call() _ platform i386-pc-mingw32 arch...

...----------start------------------->--- function (data, INDICES, FUN, ..., simplify = TRUE) { if (!is.list(INDICES)) { IND <- vector("list", 1L) IND[[1L]] <- INDICES names(IND) <- deparse(substitute(INDICES))[1L] } else IND <- INDICES FUNx <- function(x) FUN(data[x, , drop = FALSE], ...) nd <- nrow(data) ans <- eval(substitute(tapply(1L:nd, IND, FUNx, simplify = simplify)), data) attr(ans, "call") <- match.call() class(ans) <- "by" ans } <environment: namespace:base&g...

...the call to tapply in the function "by": by.data.frame = function (data, INDICES, FUN, ...) { if (!is.list(INDICES)) { IND <- vector("list", 1) IND[[1]] <- INDICES names(IND) <- deparse(substitute(INDICES))[1] } else IND <- INDICES FUNx <- function(x) FUN(data[x, ], ...) nd <- nrow(data) <<<< ans <- eval(substitute(tapply(1:nd, IND, FUNx)), data) ==== ans <- eval(substitute(tapply(1:nd, IND, FUNx, simplify=FALSE)), data) >>>> attr(ans, "call") <- match.call() class...

...ix results. Thanks in advance for any code snipplets/advice! Regards, Ken # -------------------------------------------------------------------------------------------------------------------------- > my.mat [,1] [,2] [,3] [1,] 1.5 4.5 7.5 [2,] 2.5 5.5 8.5 [3,] 3.5 6.5 9.5 > funx( a=my.mat ) [,1] [,2] [,3] [1,] -1.5 -4.5 -7.5 [2,] -2.5 -5.5 -8.5 [3,] -3.5 -6.5 -9.5 > # -------------------------------------------------------------------------------------------------------------------------- library( inline ) c.code <- " SEXP res; int nprotect = 0,...

...ode[13] <- " d2 = (x-cx)*(x-cx) + (y-cy)*(y-cy);" code[14] <- " rdata[x + y*nx] = data[x + y*nx] * exp(-d2/sigma2);" code[15] <- " }}" code[16] <- " UNPROTECT(nprotect);" code[17] <- " return res;" library(inline) funx <- cfunction(signature(a="array", s="numeric", centre="numeric"), code) x <- array(runif(50*50), c(50,50,1)) res <- funx(a=x, s=15, centre=c(30,35)) image(res[,,1]) res <- funx(x, 10, c(15,15)) x11(); image(res[,,1]) -- Dr Oleg Sklyar * EBI/EMBL, Cambri...

Hallo R-users, I'm a R (release 0.65) novice, but have read some about S. Is there a similar function in R like the S function "by()" ? This function groups data typically from a matrix by a variable and applies a function on the grouped data. Example in S: > by(data, year, summary) where "data" is a matrix containing the variable "year". After grouping

I have a fix to by.data.frame() that works for my example. Can anyone see a problem with this? The old code has: > get("by.data.frame",3) function (data, INDICES, FUN, ...) { ### code skipped to save space ans <- eval(substitute(tapply(1:nrow(data), IND, FUNx)), ### The problem seems to be here data) attr(ans, "call") <- match.call() class(ans) <- "by" ans } > My change is: Nseq <- 1:nrow(data) ans <- eval(substitute(tapply(Nseq, IND, FUNx)), data) I think what happens in the or...

I have a question about running an optimization function on an existing LOESS function defined in R. I have a very large dataset (1 million observations) and have run a LOESS regression. Now, I want to run a Newton-Raphson optimization to determine the point at which the slope change is the greatest. I am relatively new to R and have tried several permutations of the maxNR and nlm functions with

...ned long) seed); for (int i=0; i<len; i++) { v[i] = gsl_rng_get (r); } gsl_rng_free(r); return RcppSexp(v).asSexp(); ' ## turn into a function that R can call ## compileargs redundant on Debian/Ubuntu as gsl headers are found anyway funx <- cfunction(signature(s="numeric", n="numeric"), gslrng, includes="#include <gsl/gsl_rng.h>", Rcpp=TRUE, cppargs="-I/usr/include", libargs=...

...ned long) seed); for (int i=0; i<len; i++) { v[i] = gsl_rng_get (r); } gsl_rng_free(r); return RcppSexp(v).asSexp(); ' ## turn into a function that R can call ## compileargs redundant on Debian/Ubuntu as gsl headers are found anyway funx <- cfunction(signature(s="numeric", n="numeric"), gslrng, includes="#include <gsl/gsl_rng.h>", Rcpp=TRUE, cppargs="-I/usr/include", libargs=...

I have a list of dataframes, and want to apply a function to subsets of the rows of each dataframe. It seemed natural to write a function that takes a dataframe as an argument, and uses 'by() within it to apply the function to the dataframe subsets. However, I cannot get it to work. The problem seems to be passing the data argument of by() as a function argument. Is this bug, or am I

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,