similar to: Shading area under density curves

I am writing a resampling program for multiple regression using lm(). I resample the data 10,000 times, each time extracting the regression coefficients. At present I extract the individual regression coefficients using brg = lm(Newdv~Teach + Exam + Knowledge + Grade + Enroll) bcoef[i,] = brg$coef This works fine. But now I want to extract the t tests on these coefficients. I cannot

Dear list, I'm afraid this is a mundane question. Here's the background: I've produced a function which allows me to sequentially measure angles and distances from a specified reference point to a curve defined by empirical data points while (i.e. using a while loop) the angle being measured is within certain bounds. Because the curve is circular I need to pars the data

Is there a way to make a shading interpolation on an image plot? Something similar to matlab 'shading interp', I think it is called Gouraud shading. What I want is to make a image plot look nicer. with image() it looks very facetted, and I would like to make it look smoother. I've tried with interp.surface() in fields package but it (obviously) makes nan values at the borders and

I am writing a program in which I would like to take in a formula, change the response (Y) variable into something else, and then pass the formula, with the new Y variable to another function. That is, I am starting with formula <- Y~X1+X2+X3 and I'd like to do something like Y <- formula$Y newY <- f(Y) lm(newY~X1+X2+X3) So far, it seems that my

Hi, I am trying to simulate animal movement in a gridded landscape made up of cells. At each time step (iteration), the animal moves from one cell to another in a random fashion. This is how I am simulating movement, where a and b are the x,y co-ordinates of the animal at the previous time step: for (i in 1:no.of.steps){ direction <- sample(1:8, 1) if(direction == 1){ a <- a b <- b -

Dear list, First off, let me offer my apologies, I know this is a very basic question. After amassing a large number of objects (from multiple projects) in one working directory, I'd like to be able to start using different directories, just for the sake of organization alone. But I have no idea how to do this. I am using a mac, running R 2.5. Searching the FAQ online I find: 12.1 How

Hi all, subj has the first line: #! /usr/dcs/software/supported/bin/bash I think we could be more liberalistic :) best regards, --- Valery A.Khamenya

Dear all, As you can see from the attachment I'm using R to automatically annotate peptide fragmentation mass spectra, which are represented by impulse plots. I'd like to poll you on approaches of how to deal as generally as possible with the two biggest annotation issues I run into: 1) very close annotated masses (impulses) with similar y-axis dimensions - resulting in overlapping labels

Hi thank you for talking the time to help me with this. I have a sequence of numbers in a file and an equal sequence of various character, say(a b c d) each occurs more than once. I need to plot the numbers so that numbers corresponding to a in the other sequence would have green dots, those corresponding to b a red dot, nothing on c and blue square for d. i.e 2 a show a green dot 4 b show a

Hello all, I'm running R2.8.1 on a Mac OS 10.4.11. While trying to install the package gdata, I was presented with the following (error at end of report): R > install.packages("gdata") also installing the dependency ?gtools? trying URL 'http://cran.stat.ucla.edu/bin/macosx/universal/contrib/2.8/gtools_2.5.0-1.tgz' Content type 'application/x-tar' length 85484

Dear R- helpers Following a draft structure of the R script for which I am facing problem Step 1 x <- of type array with original values y <- of type array with original values Step 2 for (ctr in 1:10) { # my problem here the both x and y still show the original values from step 1 # in spite of making changes to the old values of the arrays x and y in the function function

Dear All, I'd like to know if it is possible to create animations with R. To be specific, I attach a code I am using for my research to plot some analytical results in 3D using the lattice package. It is not necessary to go through the code. Simply, it plots some 3D density profiles at two different times selected by the user. I wonder if it is possible to use the data generated for different

Colleagues, I am using "nls" successfully (2.11.1, OS X) but I am having difficulties retrieving part of the output - residual sum of squares. I have assigned the output to FIT: > > FIT > Nonlinear regression model > model: NEWY ~ PMESOR + PAMPLITUDE * cos(2 * pi * (NEWX - POFFSET)/PERIOD) > data: parent.frame() > PMESOR PAMPLITUDE POFFSET >

#Hello, # I have a question about the addition of values in specific columns and rows of a Data frame. # Below I have created two data frames, X.df and "Y.df". ## creation of X.df data frame X<- matrix(0,16,3) X.df<-data.frame(X) X.df[,1] <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4) X.df[,2] <- c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4) names(X.df)[1]<-"L(A)a(i)"

Good afternoon, Hope you all have a wonderful day. I am glad to be here. Hope you could help me with the following errors that i have been trying to figure it all out since last week. I am using Splus from Insightful, and as i read, R and Splus are very similar. So hope you could help me. I have been continously received these error messages after i ran my small program for couple of

I'm trying to convert an S-Plus program to R.  Since I'm a SAS programmer I'm not facile is either S-Plus or R, so I need some help.  All I did was convert the underscores in S-Plus to the assignment operator <-.  Here are the first few lines of the S-Plus file:   sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8,              tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2),

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several

This is my first excursion into using reshape2 and I want to ensure that the melt() function call is syntactically correct. The unmodifed data frame is organized this way: head(tds.anal) site sampdate param quant 1 UDS-O 2006-12-06 TDS 10800 4 STC-FS 1996-06-14 Cond 280 7 UDS-O 2007-10-04 Mg 1620 9 UDS-O 2007-10-04 SO4 7580 19 JCM-10B 2007-06-21 Ca 79 20

I got two questions on factors in regression: Q1. In a table, there a few categorical/factor variables, a few numerical variables and the response variable is numeric. Some factors are important but others not. How to determine which categorical variables are significant to the response variable? Q2. As we knew, lm can deal with categorical variables. I thought, when there is a categorical

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this