similar to: Variable name as string

Hi, I am not a statistics expert, so I have this question. A linear model gives me the following summary: Call: lm(formula = N ~ N_alt) Residuals: Min 1Q Median 3Q Max -110.30 -35.80 -22.77 38.07 122.76 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.5177 229.0764 0.059 0.9535 N_alt 0.2832 0.1501 1.886 0.0739

When using a gtkDrawingArea as a Cairo device I very often encounter the error: "figure margins too large" Even for the below "getting started" example from http://www.ggobi.org/rgtk2/ this is the case. > win = gtkWindow() > da = gtkDrawingArea() > win$add(da) > asCairoDevice(da) [1] TRUE > plot(1:10) Fehler in plot.new() : Grafikr?nder zu gro? > Also

Hello all, I've put together a quick and dirty menubar + dialogs + spreadsheet GUI for R using the RGtk package. Performance is not great (OOP is a real memory hog?), the design may be worse, but the hope is that it will be useful in an introductory stats course while we await the arrival of a real gui with ObveRsive and SciViews. The package can be found at

Hello all, I've put together a quick and dirty menubar + dialogs + spreadsheet GUI for R using the RGtk package. Performance is not great (OOP is a real memory hog?), the design may be worse, but the hope is that it will be useful in an introductory stats course while we await the arrival of a real gui with ObveRsive and SciViews. The package can be found at

Hi, I am looking for documentation, reference guides, etc. that explain the output of functions... For example using cor.test(...., method="pearson") with Pearson's corr coeff the output is: Pearson's product-moment correlation data: a and b t = 0.2878, df = 14, p-value = 0.7777 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval:

Hi All, Another nls related problem (for background, I'm migrating a complicated modelling package from S-plus to R). Below I've reduced this to the minimum necessary to demonstrate my problem (I think); the real situation is more complicated. Two similar selfStart functions, ssA and ssB. The 'initial' function for ssB modifies its arguments a little and then calls getInital

Hello, I was trying to build some functions which I would like to integrate over an interval using the function 'integrate' from the 'stats' package. As an example, please consider the function h(u)=sin(pi*u) + sqrt(2)*sin(pi*2*u) + sqrt(3)*sin(pi*3*u) + 2*sin(pi*4*u) Two alternative ways to 'build' this function are as in f and g below: coeff<-sqrt(1:4)

Hello, I can`t figure out how can increase the velocity of the fitting data by nls. I have a long data .csv I want to read evry time the first colunm to the other colunm and analisy with thata tools setwd("C:/dati") a<-read.table("Normalizzazione.csv", sep=",", dec=".", header=F) for (i in 1:dim(a[[2]]]) { #preparazione dati da analizzare

Hi all, This is probably going to come off as unnecessary (and show my ignorance) but I am trying to understand the parameter estimates I am getting from R when doing an ANCOVA. Basically, I am accustomed to the estimate for the categorical variable being equivalent to the respective cell means minus the grand mean. I know is the case in JMP - all other estimates from these data match the

I am interested in testing two similar nls models to determine if the lines are statistically different when fitted with two different data sets; one corn, another soybean. I know I can do this in linear models by testing for interactions. See Introductory Statistics with R by Dallgaard p212-218 for an example. I have two different data sets I am comparing to lai. ci.re should have very

Hi, I would like to asign titles into strip of a panel of curves designed thanks to a xyplot function (lattice package) I ve tried the "strip" function, ... here is my code I would like to write in the strip of each panel conditionning varialbe (here, it is data[,1]) xyplot (data[,3] ~ data[,2] | data[,1] , strip.default (which.given=1, which.panel = c(1:15), var.name= c

Hi, Being a R-newbie I am wondering how to calculate a correlation coefficient (preferably with an associated p-value) for data like: > d[,1] [1] 25.5 25.3 25.1 NA 23.3 21.5 23.8 23.2 24.2 22.7 27.6 24.2 ... > d[,2] [1] 0.0 11.1 0.0 NA 0.0 10.1 10.6 9.5 0.0 57.9 0.0 0.0 ... Apparently corr(d) from the boot-library fails with NAs in the data, also cor.test cannot cope with a

I'm trying to plot a scatterplot of two variables using pch to plot different characters based on a third factor. Here is my example > data("ToothGrowth") > attach(ToothGrowth) > levels(supp) [1] "OJ" "VC" > plot(len ~ dose,pch=as.numeric(supp)) > legend(locator(1),pch=as.numeric(supp),legend=levels(supp)) The command as.numeric(supp) returns 2 2

Hi R users I am trying to run a non linear parameter optimization using the function optim() and I have problems regarding memory allocation. My data are in a dataframe with 9 columns. There are 656100 rows. >head(org_results) comb.id p H1 H2 Range Rep no.steps dist aver.hab.amount 1 1 0.1 0 0 1 100 0 0.2528321

Dear all, I am trying to calculate the fitted values using a ridge model (lm.ridge(), MASS library). Since the predict() does not work for lm.ridge object, I want to get the fitted_value from the coefficients information. The following are the codes I use: fit = lm.ridge(myY~myX,lambda=lamb,scales=F,coef=T) coeff = fit$coef However, it seems that "coeff" (or "fit$coef") is

Good day! I'm creating HDL IP CORE (for using in FPGA) for theora encoder (now only I-frames). I don't undestand one moment. Now i develop such stages: 1. From RBG(byer) to YCbCr converter 2. DCT processing (8x8 pixels blocks) 3. Quantizator of DCT coeff. 4. Zig-Zag of quantized DCT coeff. and now i have uresolved last stage of compression - how i must send 8x8 blocks to huffman

Dear all I try to print 9 plots on a page, arranged as the code shows below. nf <- layout(matrix(c(1,0,2,0,0,3,0,4,0,5,0,6,0,0,0,0,7,0,8,9), 10,2)) layout.show(nf) but when I try to plot, an error message Fehler in plot.new() : Grafikr?nder zu gro? appears to verify p.e. with plot(runif(10:1)) i tried with plot(runif(10:1), ann=F) to produce more space, but neither. The second

Hello, I have different results from these two softwares for a simple binomial GLM problem. >From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 >From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36 Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data

Hello, I am trying to make a package for windows using my linux installation of R. I tried to make the package by first using R CMD build (under linux) to create a tar.gz package. Then I uncompressed and rearchived as a zip package. After installing this under windows, the data files work fine, but the R files aren't seen under windows. Is there some easy way to create a working zip package

For what it is worth, here are my experiences. When I first got my zaurus I compiled R version 1.4.0 for the it. It took some time to do, but managed with the skiffclusters at familiar.org. (libf2c was the hardest part). Compilation should be easier now that there are onboard compilers but I haven't tried. I thought this would be neat to have in the classroom. (It did get one student to buy