similar to: Insert .eps files in to an R plot.

Hello All, In bash, to re-execute a command, say, 'gnuplot plot.gnu', one can type !gnu. Is there a similar feature in the R console? For example: plot(g1$mean,g2$mean) . . . Lines, many lines of commands ->Here I want to repeat the previous plot command. !plot and plot(+TAB) do not work. This question was asked in this forum back in 2006 but the answer seems to have been lost

Hello All, I am trying to make a figure with 3x2 plots in it. Let us name the plots as such: 1 2 3 4 5 6 I begin my script with: par(mfcol=c(3,2)) par(oma=c(0,0,0,0)) --> This is for a postscript figure so I really don't need the outer margins. d=5 par(mar=c(d,d,d,d)) --> This applies to all the 6 plots. Now if d=0, plots 1-2,3-4 and 5-6 will have no gap between them and

Hello All, My question is not directly related to R but rather on which statistical method I should look in to for capturing the entropy of a data-set as a number. In this figure http://www.twitpic.com/18sob5 are two data sets blue and green (x-axis is time) that fluctuate between (-1,+1). Clearly, green has 4 jumps while blue has 1 (and a some?). Intuitively, green has more entropy than blue. Is

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

I have a data frame that looks like this: > gctablechromonly[1:5,] refseq geometry gccontent X60_origin X60_terminus length kingdom 1 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 2 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 3 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 4 NC_009484 cir 0.6799

Is the labeling/naming of levels in the documentation for the predict.glmmPQL function "backwards"? The documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family =

Hi, I was recently misfortunate enough to have to use regular expressions to sort out some data in R. I'm working on a data file which contains taxonomical data of bacteria in hierarchical order. A sample of this file can be generated using: tax.data <- read.table(header=F, con <- textConnection(' G9SS7BA01D15EC Bacteria(100) Cyanobacteria(84) unclassified G9SS7BA01C9UIR

Sorry about this one being long, and I apologise beforehand if there is something obvious here that I have missed. I am new to creating my own functions in R, and I am uncertain of how they work. I have a data set that I have read into a data frame: > gctable[1:5,] refseq geometry X60_origin X60_terminus length kingdom 1 NC_009484 cir 1790000 773000 3389227 Bacteria 2

Dear all, I am interested in correctly testing effects of continuous environmental variables and ordered factors on bacterial abundance. Bacterial abundance is derived from counts and expressed as percentage. My problem is that the abundance data contain many zero values: Bacteria <-

Hello, I want to place two plots on top of each other. However, the problem is that I can't figure out a simple way to align them correctly. Is there a way to specify this? Since the data is bunch of coordinates and the second layer is an outline of a map (a .ps file I import using the grImport package), I suppose one option would be to specify a set of "artificial" coordinates that

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Dear R community, I would like to compare the degree of aggregation (or dispersion) of bacteria isolated from plant material. My data are discrete counts from leaf washes. While I do have xy coordinates for each plant, it is aggregation in the sense of the concentration of bacteria in high density patches that I am interested in. My attempt to analyze this was to fit negative binomial

Is it possible to add lines or other user defined graphics to a plot in R that does not depend on the user scale for the plot? For example I have a plot plot(x,y) and I want to add some graphic that is scaled in inches or cm but I do not want the graphic to change when the x-y scales are changed - like a thermometer, scale bar or other symbol - How does one do this? I want to build my own

Hi ALL, I have very simple question regarding pattern matching. Could anyone tell me how to I can use R to retrieve string pattern from text file. for example my file contain following information SpeciesCommon=(Human);SpeciesScientific=(Homo sapiens);ReactiveCentres=(N,C,C,C,+ H,O,C,C,C,C,O,H);BondInvolved=(C-H);EzCatDBID=(S00343);BondFormed=(O-H,O-H);Bond+

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of

I am new to R, and I couldn't find the answers to my question in a faq. This could however be because I didn't know what to look for...:) I have three classes of data, data for bacteria, archaea and eukaryotes. I wish to display these in a histogram where all of the values are used to calculate each column. But, I want each column split in three, where the size of each coloured area

I am having trouble displaying a dendrogram of evolutionary relationships (a phylogram imported from the ape package) as the vertical component of a heatmap, but keeping the hierarchical clustering of the horizontal component. The relationships of the vertical component in the generated heatmap are not that of the dendrogram, although the ordering is. In more detail, I am attempting to generate