similar to: Multiple expressions, when using substitute()

I am puzzled by the warning message in the output below. It appears whether or not I fit the seasonal term (but the precise point of doing this was to fit what is effectively a second seasonal term). Is there some deep reason why AR parameters ("Warning message: some AR parameters were fixed: ...") should somehow intrude into the fitting of a model that has only MA terms? >

I'd like to be able to modify axlab in (C) below so that 'Inf' is replaced by the infinity symbol. y <- rnorm(40) breaks <- c(-Inf, -1, 1, Inf) x <- cut(y, breaks=breaks) plot(unclass(x), y, xaxt="n", xlab="") ## A: The following gives the axis labels "(-Inf, 1]", etc. axis(1, at=1:3, labels=expression("(-Inf,-1]", "(-1,1]",

You need to give the model formula that gave your output. There are two sources of variation (at least), within and between locations; though it looks as though your analysis may have tried to account for this (but if so, the terms are not laid out in a way that makes for ready interpretation. The design is such (two locations) that you do not have much of a check that effects are consistent over

The following is from version 1.6.2 of R under Windows, or 1.6.1 under Mac OSX/X11 > density(1) Error in if (!(lo <- min(hi, IQR(x)/1.34))) (lo <- hi) || (lo <- abs(x[1])) || : missing value where logical needed I am not sure how this should be handled. I encountered it in connection with densityplot(). In that connection, it might be enough to modify density() so that it

I propose the following enhancements and changes to plot.lm(), the most important of which is the addition of a Residuals vs Leverage plot. (1) A residual versus leverage plot has been added, available by specifying which = 5, and not included as one of the default plots. Contours of Cook's distance are included, by default at values of 0.5 and 1.0. The labeled points, if any, are those

Fisher is not the only person that it may be necessary to read 4 or 5 times. The same may be the case for side comments that Bill Venables is wont to make. Now to the parallel universe that I have in mind. I wonder whether the time is opportune for a list that focuses on "Statistical Methodology for R Users". The difference from other statistical methodology lists is that it will be

In this connection, note the following > a4 <- 4 > plotThis <- bquote(alpha==.(a), list(a=a4)) > do.call(plot, list(1:10, main=do.call(expression, c(plotThis)))) > do.call(plot, list(1:10, main=do.call(expression, plotThis))) Error in do.call(expression, plotThis) : second argument must be a list > ## Whereas plotThis has class "call", c(plotThis) has class

You've omitted a comma. races2000 is a data frame, which for purposes of extracting rows behaves like a 2-dimenional object. The following works fine: hills2000 <- races2000[races2000$type == 'hill', ] Additionally, you might like to ponder > type <- races2000[names(races2000)=="type"] > type[1:4] Error in "[.data.frame"(type, 1:4) :

Dear R community, I am trying to resolve a discrepancy between the way SPSS and R handle 2-way, repeated measures ANOVA. An experiment was performed in which samples were drawn before and after treatment of four groups of subjects (control and disease states 1, 2 and 3). Each group contained five subjects. An experimental measurement was performed on each sample to yield a

Under help(matrix) it is written: 'is.matrix' tests if its argument is a (strict) matrix. It is generic: you can write methods to handle specific classes of objects, see InternalMethods. Further down, under "Details", the meaning of "strict" is explained more explicitly: 'is.matrix' returns 'TRUE' if 'x' is a matrix

I have posted, at http://wwwmaths.anu.edu.au/~john/r/newsets/ image (.rda) files, and first stabs at .Rd files for various data on deaths in London from 1629 to 1939. (There are of course gaps.) The sources (Guy 1882 & Stocks 1942) are documented in the .Rd files: (1) poxetc: measles, smallpox & total deaths: 1629-1881 [I have deliberately left several inconsistencies that were in

Hi I have a matrix with 30 observations and roughly 30000 variables, each obs belongs to one of two groups. With svm and slda I get into memory troubles ('cannot allocate vector of size' roughly 2G). PCA LDA runs fine. Are there any way to use the memory issue withe SVM's? Or can you recommend any other classification method for such huge datasets? P.S. I run suse 9.1 on a 2G RAM

Can anyone suggest an equivalent, for lattice graphs, of the base graphics argument bty="l"? NB that I am leaving off the box around the strip, with a strip function: stripfun <- function(which.given,which.panel, factor.levels=as.expression(levlist), ...){ panel.text(x=0, y=0.5, lab = as.expression(levlist[which.panel[which.given]]),

The inst/doc directory of the DAAG package has 6 files coral551.spot, ... that are around 0.85 MB each. It would be useful to be able to zip then, but that as matters stand interferes with the use of the Sweave file that uses them to demonstrate input of expression array data that is in the "spot" format. They do not automatically get unzipped when required. I have checked that

I'd like to make the following change to termplot(): Add panel=points as an extra parameter. Replace if (partial.resid) points(xx, pres[, i], cex = cex, pch = pch, col = col.res, ...) by if (partial.resid) panel(xx, pres[, i], cex = cex, pch = pch, col = col.res, ...) Also, I think, add col.smooth, span and iter as named parameters; see below.

The following works fine with the x11 device, though it may well be that an initial plot is overwritten. With a pdf or postscript device, I get two plots, the first of which still has the red border from having the focus, while the second is the plot that I want. library(lattice); library(grid) plt <- xyplot(uptake ~ conc, groups=Plant, data=CO2) print(plt)

Although base graphics does not mix with lattice in the one graph, I've found that print.trellis(position=..., ) and the use of par(fig=...) to put regular and trellis graphics on the one graphics page works like a treat, at least in version 2.0.1 of R. [Base graphics functions that are themselves inconsistent with par(fig=...) are obviously disallowed.] I am wondering whether there are

Does anyone know any more than is in the following press release about REvolution Computing and their commercialization of R? http://www.intel.com/capital/news/releases/080122.htm "Intel Capital, the global investment arm of Intel Corporation, today announced that it has invested in the Series A financing of REvolution Computing, creator of parallel computing software for computational

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

Is there any way, after use of print.trellis(), to obtain the co-ordinates of the plot region, e.g., in what are then the native co-ordinates? e.g. library(DAAG) library(lattice); library(grid) data(cuckoos) pushViewport(viewport(layout=grid.layout(2, 1))) pushViewport(viewport(layout.pos.row=1)) cuckoos.strip <- stripplot(species ~ length, data=cuckoos) print(cuckoos.strip,