similar to: qqnorm() with weighted data?

I'd like to generate some plots like you'd see on the old "normal probability graph paper", like the first plot in: <http://www.itl.nist.gov/div898/handbook/eda/section3/normprpl.htm> except the horizontal scale would have 1%, 5%, 25%, 50%, 75%, 95%, 99%, or similar quantiles, with associated tick/grid lines. [still hunting around for a good example...] something like

I am writing some R code that generates some figures. I am primarily developing on GNU/Linux, but will ultimately run the code on Windows in order to get WMF files I can integrate in an existing document. The win.metafile() function, of course, doesn't exist in the GNU/Linux version of R. I am contemplating writing an abstraction layer that runs the right thing, but I need to know how the

I am getting ready to load a bunch of data into R. The data is all numeric, but some of the numbers are integer codes representing non-numeric semantics. What is the best way to "fix" the data frames so that these compenents are recognized as "factors" or "ordered", as appropriate? Can I "assign" to some attribute of the data frame component, like the

I am generating some graphs (on GNU/Linux using R 1.5.x) for a PowerPoint presentation my boss is giving next week. He just tried cut/pasted a plot off of a PDF file I had created, but he complained that the fonts were too small and fuzzy, and that he couldn't change the background. I have been playing with png(bg=transparent), but I am not sure what appropriate height,width parameters and

I've got a data frame that looks like this: subject foo bar 2 1.7 3.2 2 2.3 4.1 3 7.6 2.3 3 7.1 3.3 3 7.3 2.3 3 7.4 1.3 5 6.2 6.1 5 3.4 6.9 ... That is, I've got multiple rows per subject. I need to compute summaries within categories where the subject has the same number

I"ve got a data frame with a selection of columns I want to compute a rank-order correlation matrix from without disturbing the original data frame. foo[,c("a","b","d","f","g")] What I wanted to do, intuitively, was: > cor(rank(foo[,c("a","b","d","f","g")])) but rank in that context

I want to write a function to take an argument as the response variable of a linear model, e.g. to do anova's across a list of variables, something like the following (except, of course, this doesn't work): function(x) { anova(lm(x ~ my.factor,data=my.data)) } The x in lm() above is getting evaluated at the wrong level. How can I make this work? -- Russell Senior ``The two

Here is the summary on predict when using an arima0 object: The arima0 object must be based on a time series vector. That is; x <- ts(xm1, frequency=12, start=c(1975,1)) x.ar <- arima0(x,order=c(1,1,1)) predict(x.ar,n.ahead=3) Thanks so much to Prof. Brian Ripley and David Brahm and other! Sincerely, Erin Hodgess

I am writing a little abstraction for a series of tests. For example, I am running an anova and kruskal.test on a one-factor model. That isn't a particular problem, I have an interface like: my.function <- function(model,data) { print(deparse(substitute(data))) a <- anova(lm(formula,data)) print(a) if(a$"Pr(>F)"[1] < 0.05) { pairwise.t.test(???) }

I notice that devices are not hardlinked when using the --link-dest option, for instance: rsync -aH --link-dest=../bar/ foo/ baz/ When foo/zero is a device, baz/zero is not a hardlink to bar/zero. How come? $ rsync --version rsync version 2.5.6 protocol version 26 on linux 2.4.x -- Russell Senior ``I have nine fingers; you have ten.'' seniorr@aracnet.com

Suppose I have a set of binned data, counts exceeding a series of arbitrary thresholds, a total N, a minimum and maximum, those sorts of things. Is there a "standard" method for estimating arbitrary quantiles from this? My initial thought is that the counts and min/max give me solutions at various points along the empirical cdf. As the data are roughly log-normal, I thought maybe I

I am interested in making a random sample from a uniform distribution of points over the surface of the earth, using the WGS84 ellipsoid as a model for the earth. I know how to do this for a sphere, but would like to do better. I can supply random numbers, want latitude longitude pairs out. Can anyone point me at a solution? Thanks very much. -- Russell Senior ``I have nine fingers;

Given a set of data: > names(data) [1] "city" "house" "visit" "value" I am looking for a way to compute the variance components of the nested model (ie, visit 1 at house 2 at city 3 isn't related to visit 1 and house 2 at city 4), but different houses in the same city may be related, and different visits to the same house are probably

If some can help to find under wich package can i find the commando's wich i schould to use to work with the empirical cummulative (ecdf) distribution and also the QQplot. Zmarrou Hicham Univesity of Amsterdam -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I am trying to use rsync for making snapshots, sharing hardlinks at the destination using the --link-dest option. In conjunction with this, I would like for --dry-run to report some reliable-ish number for how much space it is going to consume at the destination so that I can arrange to free such space prior the launching rsync "for effect". Right now, that isn't working: #

We have recently digitized a set of points from some scanned engineering drawings (in the form of PDFs). The digitization resulted in x,y page coordinates for each point. The scans were not aligned perfectly so there is a small rotation, and furthermore each projection (e.g. the yz-plane) on the drawing has a different offset from the page origin to the projection origin. From the dimensions

Referring to "Using R for Data Analysis and Graphics" by J H Maindonald, and available from the R site, I found the example on p.30 non-working: > stem(qqnorm(possum$hdlngth)) Error in stem(qqnorm(possum$hdlngth)) : 'x' must be numeric Since qqnorm(possum$hdlngth) plots, and > class(possum$hdlngth) [1] "numeric" , the problem must be here: >

I want to qqnorm every column in a table. When I try the first column using qqnorm(table$column1), it worked. But when I use qqnorm(table[1]), it tells me "Error in stripchart(x1, ...) : invalid plotting method". What happen? How can I make a function that qqnorms every column? thanks a lot. -jiong The email message (and any attachments) is for the sole use of the intended

Hello, I am trying to do some plotting to check random effect assumptions for a model I fit using lme. I want to use qqnorm and pairs (similarly to examples given in Pinheiro & Bates p. 188), but it's not working. Here's some relevant code and the error message: library(nlme) data(Machines) m1 <- lme(fixed=score~Machine,random=~1|Worker/Machine, data=Machines) qqnorm(m1,

Dear List, invoking qqnorm-plots in Version 63.3 produces funny things: using the option `type="s"´ on qqnorm should give a nice *line* of observed quantiles. Now, the line is walking along in order to the points index instead from lowest to highest, wich makes funny slopes. try x <- table(rnorm(1000) # or similar and qqnorm(x,type="s") # in 0.63.2 and 63.3 Well, the