similar to: using split.screen?

This means that the margins for 10 screens would take up more room than you have - essentially the plot area is being squeezed to nothing. You can try reducing your margins using par. Also, it looks like you're trying to split into 20 screens there. Hope this helps, Matt -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On

# polygon ignores requests to have its border transparent, look at par(bg="transparent") plot(c(0, 3), 0:1) polygon(c(0, 1, 1, 0), c(0, 0, 1, 1), border=NA, col = 0) polygon(c(1, 2, 2, 1), c(0, 0, 1, 1), border="transparent", col = 0) polygon(c(2, 3, 3, 2), c(0, 0, 1, 1), border=0, col = 0) # a quick fix for erase.screen() is the following erase.screen <- function (n =

Dear all, I would be pleased if anyone could help me. The Rhelp description for the figs parameter is "a two-element vector describing the number of rows and colunns in a screen matrix". So, why does my code (below) produce a 2x1 screen matrix instead of a 1x2 one? Thanks in advance, rodrigo. ----------------------------------------------------------- plot.new()

Hello. I know how to make a bar plot in which a numeric y variable is plotted against some grouping variable X (say, groups A, B, C) when this grouping variable is subdivided into each of two subgroups; so the bars would be: (group A subgroup 1) beside (group A subgroup 2), then (group B subgroup 1) beside (group B subgroup 2), and so on. This is done using the beside=TRUE argument in the

Hi, I successfully created 3 screens with the following: fig.mat<-c(0,.5,.5,.5,1,1,0,0,.5,1,.5,1) fig.mat<-matrix(fig.mat,nrow=3) fig.mat split.screen(fig.mat) I can plot three different plots on those 3 screens, but when I try the following: (Trying to create three graphs with a common x-axis but different y-axis on screen 1) screen(1) par(oma=c(3,3,0,0)) par(fig=c(0,1,0,0.33))

Folks, I'm battling the layout() functionality in graphics, and getting a bit mixed up. I'd like to create subscreens like so: _________ _________ | | | | 1 | 2 | |_________|________ | | | | | 3 | 4 | |_________|_________| | |____6____| | 5 |____7____| |_________|____8____| Note that subscreens 1:5 are the same

I want to draw a figure with several panels of unequal size, so i thought I would try using screen(). However, I can't figure out how to define the sizes as a matrix. I've tried this: split.screen(matrix(c(0,0.5,0,0.5, 0.5,1,0.5,1), byrow=F, ncol=4)) and a couple of variants on it, but get the same error: Error in par(.split.screens[[cur.screen]]) : invalid value specified

Hi all, after installing samba 2.2.5 (on Solaris8 running NIS+) it is not possible to create user entries in smbpasswd. I generated the smbpasswd file by # cat /dev/null | /samba/samba-2.2.5/source/script/mksmbpasswd.sh > /usr/local/samba/private/smbpasswd ----- then tried to create user entry "dummy" in smbpasswd by (user "dummy" exists under NIS+) # smbpasswd -a dummy

Hello. Please excuse this off-topic request, but I know that the question has been debated in summary form on this list a number of times. I would find a paper that lays out the advantages and disadvantages of using different types of SS in the context of unbalanced data in ANOVA, regression and ANCOVA, especially including the use of different types of contrasts and the meaning of the

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using the

Hello. I am sorry for posting a (seemingly) simple question, but I have just spent 2 hours trying to find the answer, without success. I want to make a histogram with conditioning on a factor, using Trellis graphics. However, I do not want any colours (only black and white) either in the histograms or in the strip. There must be some simple argument but I can’t find it. Here is my code so

Hello. Following pages 342-347 of Pinheiro & Bates, I am trying to write a self-starting nonlinear function (a non-rectagular hyperbola) to be used in nonlinear least squares regression (and eventually for a mixed model). When I use the getInitial function for my self-starting function I get the following error message: > getInitial(photo~NRhyperbola(Irr,theta,Am,alpha,Rd),dat) Error

Hello. I am trying to determine whether I should be using ML or REML methods to estimate a linear mixed model. In the book by Pinheiro & Bates (Mixed-effects models in S and S-PLUS, page 76) they state that one difference between REML and ML is that « LME models with different fixed-effects structures fit using REML cannot be compared on the basis of their restricted likelihoods. In

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

Hello. I have to fit a mixed model from a repeated measures split-plot experiment in which the response variable is binary. This requires a generalised linear mixed model in which I can specify a binomial distribution. I can’t find the appropriate package in R. I have looked at glmmML, but it doesn’t seem to allow any mixed structure beyond a simple 2-level one. Can anyone point me to the

Hello. I want to plot the distribution of a continuous variable (y) in each of two groups on the same graph as histograms. I suppose one could call this a 2-d histogram? Can this be done in R? Here is a typical data.set: y group 1.2 1 3.3 1 2.4 2 5.7 1 0.2 2 etc. Bill Shipley Subject Matter Editor, Ecology North American Editor, Annals of

Hello. I cannot find out how to extract the residuals from a mixed model using the lmer function. Can someone help? Bill Shipley North American Editor, Annals of Botany Editor, "Population and Community Biology" series, Springer Publishing Département de biologie, Université de Sherbrooke, Sherbrooke (Québec) J1K 2R1 CANADA Bill.Shipley@USherbrooke.ca

Hello. I am having trouble downloading the lme4 package from the CRAN site. The error is: > local({a <- CRAN.packages() + install.packages(select.list(a[,1],,TRUE), .libPaths()[1], available=a, dependencies=TRUE)}) trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 26129 bytes opened URL downloaded 25Kb

Hello. I am about to do a rather complicated analysis and am not sure how to do it. The experiment has a split-plot design and also repeated measures. Both of these complications require one to define an error term and it seems that one cannot specify two such terms. The split-plot command is: aov(y~covariates +A*B+Error(C), data=) where A and B are the fixed effects and C is the

Hello. I am trying to fit a non-rectangular hyperbola function to data of photosynthetic rate vs. light intensity. There are 4 parameters that have to be estimated. I find the nls function very difficult to use because it often fails to converge and then gives out cryptic error messages. I have tried playing with the control parameters but this does not always help. Is there another