similar to: Cubic splines in package "mgcv"

R-users E-mail: r-help@r-project.org Hi! R-users. I am just wondering what the definition of "dffits" in R language is. Let me show you an simple example. function() { library(MASS) xx <- c(1,2,3,4,5) yy <- c(1,3,4,2,4) data1 <- data.frame(x=xx, y=yy) lm.out <- lm(y~., data=data1, x=T) lev1 <- lm.influence(lm.out)$hat sig1 <-

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

R-users E-mail: r-help at stat.math.ethz.ch Hi! I would like to produce DLL files to be linked to R objects on Windows98SE. The source files are written in Fortran77. I input the command below on R console. Rcmd SHLIB aaa.f The result is: Error: syntax error Does this mean that "Rcmd SHLIB aaa.f" contains symtax error, or "aaa.f" contains it? Or do I need to do

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

Dear R users (r-help@r-project.org), The definition of AIC (Akaike information criterion) for normal error models has just been changed. Please refer to the paper below on this matter. Eq.(22) is the new definition. The essential part is RSS(n+q+1)/(n-q-3); it is close to GCV. The paper is temporarily available at the "Papers In Press" place. Kunio Takezawa(2012): A Revision of

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

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,

R users E-mail: r-help at stat.math.ethz.ch I really appreciate information from Dr. Ligges and Dr. Wang. I managed to create DLL files by MinGW and use them as subroutines on R. Thank you very much again. ******** E-mail: takezawa at affrc.go.jp ******** ***** http://cse.naro.affrc.go.jp/takezawa/patent-e.html *****

Hi All, What do you consider to be the best book(reference) on nonparametric regression? I am currently reading the book of Kunio Takezawa(2006): "Introduction to nonparametric regression". Is the book of Hardle(1990): "Applied nonparametric regression" better? or maybe another book? This is off topic, but most of the books is using R or S-plus. Thanks Hennie

Hello, I'm using mvpart option xv="1se" to compute a regression tree of good size with the 1-SE rule. To better understand 1-SE rule, I took a look on its coding in mvpart, which is : Let z be a rpart object , xerror <- z$cptable[, 4] xstd <- z$cptable[, 5] splt <- min(seq(along = xerror)[xerror <= min(xerror) + xvse * xstd]) I interprete this as following: the

Hello R-list. I am a "long time listener - first time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the

Hi all! I'm getting a model fit from glm() (a binary logistic regression fit, but I don't think that's important) for a formula that contains powers of the explanatory variable up to fourth. So the fit looks something like this (typing into mail; the actual fit code is complicated because it involves step-down and so forth): x_sq <- x * x x_cb <- x * x * x x_qt <- x * x * x

Hi, I'm responding to a post about finding roots of a cubic or quartic equation non-iteratively. One obviously could create functions using the explicit algebraic solutions. One post on the subject noted that the square-roots in those solutions also require iteration, and one post claimed iterative solutions are more accurate than the explicit solutions. This post, however, is about

Hi, I have a problem that concerns combination of the package Maptools and Splancs I have 2 shapefiles that i want to manipulate (one of type point and one polygon).I import them in R using Maptools but then i can't estimate a quartic Kernel using Splancs. The package doesn't recognize the shapes (invalid points and poly argument).I don't know if this is an easy task but i have

Hi, I would need some help with the splans package in R. I am using a Shapefile (downloadable at) http://rapidshare.com/files/215206891/Redlands_Crime.zip and the following execution code setwd("C:\\Documents and Settings\\Dejan\\Desktop\\GIS\\assignment6\\DataSet_Redlands_Crime\\Redlands_Crime") library(foreign) library(splancs) auto_xy<-read.dbf("Auto_theft_98.dbf")

I have a variable which is roughly age categories in decades. In the original data, it came in coded: > str(xxx) 'data.frame': 58271 obs. of 29 variables: $ issuecat : Factor w/ 5 levels "0 - 39","40 - 49",..: 1 1 1 1... snip I then defined issuecat as ordered: > xxx$issuecat<-as.ordered(xxx$issuecat) When I include issuecat in a glm model, the result

I'm in teaching mode, kernel densities. {History: density() was newly introduced in version 0.15, 19 Dec 1996; most probably by Ross or Robert } When I was telling the students about different kernels (and why their choice is not so important, and "equivalent bandwidths" etc,etc) I wondered about the "Cosine" in my teaching notes which is defined there as k(x)

To R-helpers, R offers the polyroot function for solving mentioned equations iteratively. However, Dr Math and Mathworld (and other places) show in detail how to solve mentioned equations non-iteratively. Do implementations for R that are non-iterative and that solve mentioned equations exists? Regards, Mads Jeppe

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

R-users E-mail: r-help@r-project.org Hi! R-users. A simple object as below was created to see how gam() of package "mgcv" and anova() work. function() { library(mgcv) set.seed(12) nd <- 100 xx1 <- runif(nd, min=1, max=10) xx1 <- sort(xx1) yy <- sin(xx1)+rnorm(nd, mean=5, sd=5) data1 <- data.frame(x1=xx1, y=yy) fit1 <- gam(y~s(x1, k=5),