similar to: Saving workspace image

To estimate the covariance matrix of e you could use the sample covariance matrix of the residuals. If desired, use its cholesky decomposition to transform to make the error approximately uncorrelated, then refit (and back-transform the coefficient matrix). Stacking the columns of Y and replicating X won't do what you write; it forces each univariate regression to have the same coefficients.

Hi Folks, I don't think I'm up to implementing a function for drawing French Curves, as requested by "dream". However, the discussion about it took me back so vividly to the old days that, paranormally, I felt once again the urge to ascertain magnitudes as it used to be, and should be, done. I have therefore implemented, and hereby donate to the R community, a new function

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of > Ted.Harding at nessie.mcc.ac.uk > Sent: Sunday, December 05, 2004 7:14 PM > To: r-help at stat.math.ethz.ch > Subject: Re: [R] What is the most useful way to detect > nonlinearity in lo > > > On 05-Dec-04 Peter Dalgaard wrote:

How about: nis.na <- complete.cases --- From: <Ted.Harding at nessie.mcc.ac.uk> Hi Folks, My recent response to Laura Quinn's query about matrix subsetting reminded of a question. I wrote: iDir <- ((Winds[,20]<45)|(Winds[,20]>315))&(!is.na(Winds[,20])) Now, I find "!is.na" a bit awkward to type, so I might prefer to type it as "nis.na".

Hi, I finally managed to make it work (I just needed to have a X11 window open). Thank you very much for your help. Agnes -----Original Message----- From: Ted Harding [mailto:Ted.Harding at nessie.mcc.ac.uk] Sent: Friday, July 21, 2006 3:34 PM To: Paquet, Agnes Cc: r-help at stat.math.ethz.ch Subject: RE: [R] connection to X11 problem On 21-Jul-06 Paquet, Agnes wrote: > Dear List, >

Greetings Folks, When R code (as entered or read from a courced file) is executed, is it interpreted from the input form every time having once been read in, or do subsequent invocations use an "intermediate" (pre-interpreted) form? Or, putting it another way, is the execution of R code faster second time time round (and later) because the pre-interpretation has already been done once

Hi Folks, No doubt a question with a well-known answer, but I'm unfortunately not managing to find it readily ... ! I have a quantitative variable Y and a 4-level ordered factor A (with very unequal numbers at the different levels, by the way). The command lm(Y ~ A) returns (amongst other stuff) an intercept, and coefficients A.L, A.Q and A.C for the Linear, Quadratic and Cubic effects.

Hi, Splus allows pasting a graphics object into the plotting window, which makes it possible to do what you describe below. Now I use R which doesn't seem to allow pasting the picture into the graphics window, so I copy the graph onto a transparency sheet, and stick it onto my screen using tape. The coordinates need to be converted to make sense (as you describe). My methodology with the

Hi Folks, Given k RVs with MVN distribution N(mu,S) (S a kxk covariance matrix), let (w.l.o.g.) X1 denote the first r of them, and X2 the last (k-r). Likewise, let mu1 and mu2 denote their respective expectations. Then, of course, the expectation of X2 given X1=x1 is mu2 + S21*inv(S22)*(x1 - mu1) and the covariance matrix of X2 given X1=x2 is S22 - S21*inv(X11)*S12 where Sij is the

Hi folks Does R have anything straightforwardly resembling the commands fprintf, sprintf, printf (derived from C, and present in octave and matlab)? As in printf(format_string, ... ) where "format_string" defines the print format (including any fixed text) and "..." is a list of variables whose values are to be inserted into the line. Example: printf("Case

Hi folks, I have a vaiable Y and an associated factor Z at several (13) levels. boxplot(Y~Z) produces a nice array of boxplots, one for each level of Z, and each duly labaelled with its level of Z. I would like to superpose on each boxplot the actual data points which it represents, i.e. do something conceptually (though not in real R) expressed as points(Y~Z) or points(Z,Y) It can

Hi Folks, I am interested in extending the repertoire of link functions in glm(Y~X, family=binomial(link=...)) to include a "tan" link: eta = (4/pi)*tan(mu) i.e. this link bears the same relation to the Cauchy distribution as the probit link bears to the Gaussian. I'm interested in sage advice about this from people who know their way aroung glm. >From the surface, it looks

Hi Folks, I'm analysing some data which, in its simplest aspect, has 3 factors A, B, C each at 2 levels. If I do lm1 <- lm(y ~ A*B) say, and then summary(lm1, corr=T) I get the correlation matrix of the estimated coeffcients with numerical values for the correlations (3 coeffs in this case). Likewise with 'glm' instead of 'lm'. However, if I do lm2 <- lm(y ~

Something I don't understand ... (!) With the lattice library loaded, I have a loop for( Z in ... ) { ... xyplot(y~x | t, xlab=..., ylab=... ) } and no plot appears on the R graphics device. However, when I run the commands within {...} separately for each instance of Z, I get the plot displayed each time. So it looks as though "xyplot" is not outputting to the graphics

Thanks to Fernando Tusell and especially to Brian Ripley for their work on 'mix', leading to an apparently good package mow available on CRAN. Going through the R code for the function prelim.mix, I am wondering why the following method of calculation is used at one point: umd <- as.integer(round(exp(cumsum(log(d))))) (d is a vector containing, in effect, the numbers of levels of

Documentation for S3 functions is apparently written in troff markup with macro tags like .BG .FN .TL .DN .CS ... Inspection of S3 documentation source files gives a pretty clear idea of what these mean, semantically (and Sd2Rd is a perl script which converts this markup into the Rd format, providing further semantic information along the way). My query is: Can anyone point to troff

Hi Folks, The inspiration for this query is described below, but it prompts a general question: If one wants to use only one or a few functions from a library, is there a way to load only these, without loading the library, short of going into the package source and extracting what is needed (including of course any auxiliary functions and compiled code they may depend on)? What prompted this

Greetings folks, Shafer's S-plus package "norm" for multiple imputation of missing values in multivariate normal data has been most kindly and usefully ported to R by Alvaro A. Novo. Shafer's website http://www.stat.psu.edu/~jls/ lists four S-plus packages in all: NORM - multiple imputation of multivariate continuous data CAT - multiple imputation of multivariate

Hi folks, Suppose I have a series of cases each with categorical factors A, B. What is the best way to "glue" A and B together into a single factor? For example, given A0 B1 ... A1 B1 ... A0 B2 ... A1 B0 ... A0 B0 ... A1 B2 ... then I'd like to end up with a single factor with levels A0B0, A0B1, A0B2, A1B0, A1B1, A1B2 according to all the combinations which actually occur in

Sorry Folks, I'm sure I could suss out the answer myself but I need it soon ... ! 1. Given a set of 4 variables X,Y,Z,W in a dataframe DF, I make a scatter-plot matrix using splom(DF). 2. I do all regressions of U on V using lm(U~V), where U and V are all 12 different ordered pairs from X,Y,Z,W. 3. Now I would like to superpose the regression lines from (2) onto the corresponding