similar to: AIC in glm.fit with intercept

Hi All, I have a quick question about using plot.Predict now that the rms package uses lattice. I'd like to add tick marks along the regression line, which is given by data=llist(variablename) in the plot call. The ticks show up fine, but I'd like to alter the color. I know the ticks are produced by scat1d, but after spending a fair bit of time going through documentation, it still

The summary stats for the xin and yin variables below are correct. However, if I use plot(xin,yin), an exception is thrown saying that "object xin is not found." Also, it is apparent that I can't successfully replace the x and y vectors with values from xin and yin. The four plots on one panel are showing but the range of x and y is only [0,1], and therefore, it seems like an

Dear all, I am trying to calculate the fitted values using a ridge model (lm.ridge(), MASS library). Since the predict() does not work for lm.ridge object, I want to get the fitted_value from the coefficients information. The following are the codes I use: fit = lm.ridge(myY~myX,lambda=lamb,scales=F,coef=T) coeff = fit$coef However, it seems that "coeff" (or "fit$coef") is

I'm not understanding something. I'm trying to add xlab & ylab to a balloon plot of a table object. From docs I thought following should work: require(gplots) # From balloonplot example: # Create an example using table xnames <- sample( letters[1:3], 50, replace=2) ynames <- sample( 1:5, 50, replace=2) tab <- table(xnames, ynames) balloonplot(tab)

I'm using R embedded in PostgreSQL (via PL/R), and would like to use it to create images. It works fine, except that I have to create every image in a file (owned by and only readable by the PostgreSQL server), and then use PostgreSQL to read from that file and return it to the client. It would be much nicer if I could plot images into an R variable (for instance, a matrix), and return that

Dear all R gurus, I have following syntax: y = c(1:10) chippy <- function(x) { y[5] = x sin(cos(t(y)%*%y)*exp(-t(y)%*%y/2)) } curve(chippy, 1, 20, n=200) But I am getting error while executing : Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ In addition: Warning message: number of items to

Dear R-List, I've written some code to put measurement values at a position x and y in bins (xb and yb). It works, but I wonder if there isn't a function that would do what I do by hand in "# fill data in bins"? Here is the code: # data x <- c( 1.1, 1.5, 2.3, 2.5, 2.6, 2.9, 3.3, 3.5 ) y <- c( 6.3, 6.2, 5.9, 5.3, 5.4, 4.2, 4.8, 4.6 ) val <- c( 50, 58, 32, 14, 12,

Hi, I have a cloud of randomly distributed points in 2-dimensional space and want to set up a grid, with a given grid-cell size, that minimizes the distance between my points and the grid nodes. Does anyone know of an R function or toolbox that somehow addresses this problem? This is a problem of optimizing the location of the grid, not a problem of deciding what should be the grid-cell size,

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341

Hello, I am a new R user and I am trying to estimate some generalized linear models (glm). I am trying to compare a model with a gaussian distribution and an identity link function, and a poisson model with a log link function. My problem is that while the gaussian model has significantly lower (i.e. "better") AIC (Akaike Information Criterion) most of the coefficients are not

Greetings: I would like comments on this example and after fixing it up, I need help from someone who has access to insert this in R's help page for plotmath. I uploaded a drawing http://pj.freefaculty.org/R/Normal-2009.pdf that is created by the following code http://pj.freefaculty.org/R/Normal1_2009_plotmathExample.R This will be a good addition to the plotmath help page/example.

I'm using R-2.6.2 on Fedora Linux 9. I've been experimenting with plotmath. I wish it were easier to combine expressions in plotmath with values from the R program itself. There are two parameters in the following example, the mean "mymean" and standard deviation "mystd". I am able to use bquote to write elements into the graph title like mu = mymean and R will

Hi all, I am trying to understand how is calculated the AIC returned by glm(). I have a model object m1 which fitting results are: > summary(m1) [...] (Dispersion parameter for gaussian family taken to be 3.735714) Null deviance: 1439.8 on 15 degrees of freedom Residual deviance: 52.3 on 14 degrees of freedom AIC: 70.357 Since there are 2 parameters, I would naively compute: AIC

Hi R list, I like the default ticks that are set up using grid.xaxis() or grid.yaxis() with no arguments. Finding good values for the 'at' argument is usually not a trivial task; the default behavior of these functions seems to work well. The problem with this strategy is that I cannot figure out how to "recover" the positions of these ticks when you do NOT specify the

I am trying to know exactly the formulas used to calculate aic for glm models. In glm.fit, the calculus of aic is: aic.model <- aic(y, n,mu, weights, dev) + 2 * fit$rank where 2 * fit$rank is (may be am i wrong?) twice the numbers of parameters p and aic(y, n, mu, weights, dev) refers to the function defined in the family function (which is for Gamma family, for instance) aic

Here's another example of my plotmath whipping boy, the Normal distribution. A colleague asks for a Normal plotted above a series of axes that represent various other distributions (T, etc). I want to use vectors of equations in plotmath to do this, but have run into trouble. Now I've isolated the problem down to a relatively small piece of working example code (below). If you would

I've been testing plotmath. But I'm getting some funny output one one computer. The problem is that characters are 'jumbled' and overstrike when symbols are introduced. Sample code: mu <- 440.0 sigma <- 12.5 myx <- seq( mu - 4*sigma, mu+ 4*sigma, length.out=500) myDensity <- dnorm(myx,mean=mu,sd=sigma) # Here's one way to retrieve the values of mu and sigma and

Dear All, could someone please shed some light on the use of the .C or .Fortran function: I am trying load and running on R the following function // psi.cpp -- psi function for real arguments. // Algorithms and coefficient values from "Computation of Special // Functions", Zhang and Jin, John Wiley and Sons, 1996. // // (C) 2003, C. Bond. All rights reserved. // //

Hi, I try to ask here, because I hope someone will help me understand this problem- I have fittet a glm in R with the results > glm1 <- > glm(log(claims)~log(sum)*as.factor(grp),family=gaussian(link="identity")) > summary(glm1) Call: glm(formula = log(claims) ~ log(sum) * as.factor(grp), family = gaussian(link = "identity")) Deviance Residuals: Min 1Q

Dear Researchers, I wrote two function to fit a circle using noisy data. 1- the fitCircle() is derived from MATLAB code of * zhak Bucher* from the link http://www.mathworks.com/matlabcentral/fileexchange/5557-circle-fit/content/circfit.m 2- the CircleFitByPratt() from MATLAB code of *Nikolai Chernov *from the link