similar to: make check failure with R-1.7.0

Hi all, Perhaps this in an inappropriate post, but I've found a bug in proj I'd like to track down a bit further before making a formal bug report. The example below shows the problem, if you change the rownames proj fails. The problem seems to be that there is a mismatch in the rownames in the qr objects constructed by aov and the rownames that proj is expecting them to have.

Hi, This might be a minor bug, or it could be that I'm just mis-using the tools (again). If you modify the example for model.tables to introduce an NA, N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <-

I would appreciate it if someone could explain the results of the example from the aov() help file. The output given below is different from book Venables and Ripley - MASS The results In R1.5.1 (Under windows) is as follows: > N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) > P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) > K <-

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I've been able to do most things I'd like to using the map package, but I can't figure out how to fill a region without drawing a black outline. I can draw the outline in a specified colour, but as soon as I set fill to TRUE, I get a black outline in addition. My map has a lot of activity on the coastline so I don't want a black line obscuring what I do there. A pale colour to

Hi, I am trying to plot an interaction.plot with different color for each level of a factor. It has an erratic behavior. For example, it works for the first interaction.plot below, with the example from the ALDA book, but not with the other plots, from the NPK dataset: # from http://www.ats.ucla.edu/stat/r/examples/alda/ch2.htm tolerance <-

Dear Madame, Dear Sir, I am able to obtain the coefficients from a 'summary' of 'lm', but NOT from a 'summary' of 'aov'. The following example shows my steps. ## Initialize rm(list = ls()) # remove (almost) everything in the working environment utils::data(npk, package="MASS") # get data model <- yield ~ block + N*P*K ## Using lm npk.lm <-

I have a square matrix wherein a '*' indicates an HSD between the levels indicated by row name and column name. The '.' is simply marking the diagonal. A blank indicates the same group A B C D E F G H I J K L M N A . B . C * * . D * * . E * * * . F * * * .

I've been looking at the kinship package which looks as though it might be appropriate for my purposes. What I can't find is any reference to the data that is used in the example code. A dataframe called d10 with column names, upn, dadid, momid, sex and affect is required. One can get an idea of what sort of values should be in most columns from the description in the pedigree function,

Dear listmembers, I would like to define a class with a slot that takes either an object of class aov or NULL. I have been reading "S4 Classes in 15 pages more or less" and "Lecture: S4 classes and methods" #First I tried with list and NULL setClass(listOrNULL") setIs("list", "listOrNULL") setIs("NULL", "listOrNULL") #doesn't

I seem to remember this was discussed a year or two ago, but I can't find it in the archives. platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 7.0 year 2003 month 04 day 16 language R

Of course, that's as trivial as it gets on the command line, but I can't work out how to get a column of numbers that are entered as "10^5" from its 'character' format into a numeric one? I feel a bit embarrassed asking such a simple question. Too much Easter.... Thanks -- Patrick Connolly HortResearch Mt Albert Auckland New Zealand Ph: +64-9 815 4200 x 7188

I've been asked about "Canonical Variance Analysis" (CVA). I don't see any reference to it searching the R site. Does it go by other names? Genstat describes it thus: Canonical variates analysis operates on a within-group sums of squares and products matrix, calculated from a set of variates and factor that specifies the grouping of units. It finds linear combinations of the

Dear R friends My goal is to eliminate this specific group(1) if the # of NAs in this group greater than 50%(specifically say greater than 3). Would you please show me how to do it. I have a sample data as following: Thanks a lot. Kevin Lin y group f1 f2 f3 30 NA 1 1 1 1 27 NA 1 1 2 2 48 NA 1 2 1 2 40 -0.6066416 1 2 2 1 24

Dear statistitians / R users, I was told to analyse the effects of the four factors using lm or update for aov. The following is the result from lm. As you can see that in the last few lines enclosed coefficients. 1)Does this mean I can write a formula, like yield =0.26 + 0.03*H + 1.48*T + 0.04*L + 0.004*C 2) in the two levels design, is there any difference between I use (-1, 1) to

I investigated why I was getting this error message... Error in Ops.factor(groups[subscripts], vals[1]) : Level sets of factors are different which led me to putting a browser in panel.superpose: Called from: panel.superpose(x, y, subscripts, ...) Browse[1]> vals vals [1] 1 2 3 Levels: 1 2 3 Browse[1]> groups[subscripts] groups[subscripts] [1] 1 2 3 1 2 1 2 3 1 2 3 1 2 3 Levels: 1 2

Say we have a tab delimited file called bug.txt Part Rep Cage Hb pupae 1 1 S 32 1 M 34 L 42 2 S 36 M 28 L 36 read.delim("bug.txt") Part Rep Cage Hb.pupae 1 1 1 S 32 2 NA 1 M 34 3 NA NA L 42 4 NA NA NA 5 NA NA NA 6 NA 2 S 36 7 NA NA M 28 8 NA NA L 36 >

platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 5.1 year 2002 month 06 day 17 language R I've been using bwplot to plot a dataframe that has a structure like this: str(plot.df) `data.frame': 5952

I'm asking on this list because the question will be more easily understood than on a Perl list. Lots of talented people using R also know how to use Perl, so it's easier to ask them. Running a Perl script with the -d switch can do some pretty neat things, but as far as I can tell, every line has to be done individually. Is there a way to specify where to stop in the way browser() does

V&R describes binomial GLMs with mortality out of 20 budworms. Is it appropriate to use the same approach with mortality out of numbers as low as 3? I feel reticent to do so with data that is not very continuous. There are one continuous and one categorical independent variables. Would it be more appropriate to treat the response as an ordered factor with four levels? If so, what family