similar to: superimposing histograms

Earlier, I posted the following question: I want to superimpose histograms from three populations onto the same graph, changing the shading of the bars for each population. After consulting the help files and the archives I cannot find out how to do this (seemly) simple graph. To be clear, I want - a single x axis (from -3 to 18) - three groups of bars forming the histograms of each population

I wrote some code to do this. It only works with 2 groups (that's all I needed), but could probably be generalized. It got my graph made, and I haven't needed a graph like this one again, so I never went back to really clean it up. It works by first plotting both sets of rectangles, then going back over the ones that had the first bar totally covered by the second. Hope this helps,

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 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

Dear users, I would like to plot several histograms superimposed on the same panel with different colors, with superimposed polygons appearing with transparency effects. I also want estimated densities to appear on the same plot. For several reasons, including that I like it, I want to use the lattice package. I have several questions regarding the use of the 'histogram' function with a

Hi all, I have data which is represented as a histogram and want to add more data / another histogram to this plot using another color. That is I need to superimpose multiple histograms. But have no idea how to do this. Can anybody please give me a hint? Thanks, Andreas -- Andreas Wilm Institut fuer Physikalische Biologie Heinrich-Heine-Universitaet Duesseldorf

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

Hello: I would like to superimpose vector polygons (state outlines) from a Shape file on top of a satellite image, imported into a SpatialGridDataFrame from GEOTIFF via gdal_translate and readGDAL. When I plot polygon and point shape files in R, into SpatialPointDataFrame and SpatialPolygonDataFrame, the two feature sets line up geographically, so it seems logical that a SpatialGridDataFrame

Dear all, I have a undirected network (g), representing all the sexual relationships that ever existed in a model community. I also have a directed edgelist (e) which is a subset of the edgelist of g. e represents the transmission pathway of HIV. Now I would like to superimpose the picture of the sexual relationships with arrows in a different colour, to indicate where in the network HIV was

I have two xyplots that i want to superimpose (code below). By default they are displayed on slightly different y scales (one runs from 10 to 25, the other from 10 to 30). I would like to force them both onto the same scale (10 to 30) so the relation between the two is clear. Is there a way to do this? thanks much pct_compl_chart <- xyplot(pct_compl ~ date, col="red",

Hello, I'm trying to superimpose a line plot onto a histogram but I'm not having any luck. I've attached the dataset. What I did was: > hist(data,freq=F) Now I'm trying to superimpose the following points with a line connecting them onto the histogram: x y 100 0.535665393824959 200 0.212744329736556 300 0.0844933242968584 400 0.0335572838043417 500

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. 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

Hi, I am trying to do a simple superimposing of two density plots and am seeing strange behavior (I haven't seen this reported elsewhere). This little snipit will make it apparent... a = rnorm(10) b = rnorm(100, mean=-2, sd=0.5) densityplot( ~ a + b) This is supposed to superimpose the two distributions on the plot, as if they were in different "groups". But the resulting

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 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

Hi all, I've been trying to figure out how to superimpose a histogram on a biplot that shows the relative contribution of each axis. I have been using the NIPALS function (http://biomserv.univ-lyon1.fr/~dray/ files/softwares/nipals.R) to run principal component analyses. Here is a toy example. source("http://biomserv.univ-lyon1.fr/~dray/files/softwares/nipals.R")