similar to: Collapsing panel data

I think that this is an easy one... I have a matrix where each row is an (x,y,z) triplet. Given a potential (xnew,ynew,znew) triplet I want to know if the matrix already contains a row with the new values (the space already has that point). I can do it using a for loop, but I would like to know if there is anyway in which I can do it without the for loop. I do it now like this (this

Hello everybody, I'm a new user of R and I'm working right now with the ACE function from the acepack library. I Have a question: Is there a way to predict new responses using ACE? What I mean is doing something similar to the following code that uses PPR (Projection Pursuit Regression): library(MASS) x <- runif(20, 0, 1) xnew <- runif(2000, 0, 1) y <- sin(x) a <- ppr(x, y,

Dear help-list members, I am a student at Durham University (UK) conducting a PhD on spatial representation in baboons. Currently, I'm analysing the effect of sampling interval on home range calculations. I have followed the baboons for 234 days in the field, each day is represented by about 1000 waypoints (x,y coordinates) recorded at irregular time intervals. Consecutive waypoints in

Dear Researchers, I wrote two function to fit a circle using noisy data. 1- the fitCircle() is derived from MATLAB code of * zhak Bucher* from the link http://www.mathworks.com/matlabcentral/fileexchange/5557-circle-fit/content/circfit.m 2- the CircleFitByPratt() from MATLAB code of *Nikolai Chernov *from the link

Hi, I have a problem when I want to add new points (or a new line) to the graph. Some points (or parts of the line) are not shown on the graph because they lie beyond the scale of the axis. Is there a way to overcome this so all points (or the entire line) are shown on the graph? Here's an example of my problem: colors = c("red", "blue") plot(x=rnorm(100,0,1),

dear:teacher i have a problem which about the polr()(package "MASS"), if the response must have 3 or more levels? and how to fit the polr() to 2 levels? thank you. turly yours [[alternative HTML version deleted]]

Dear community, I'd like to build a PLS plot with scores and loadings, sometimes called "biplot". Like in biplot.mvr {pls} but using ggplot2. 1. Scores plot. No problem! ggplot(data=data1,aes(x=plsr1,y=plsr2))+geom_point(aes(colour=solenergy,shape=type))+geom_text(aes(label=res,size=1,hjust=0,vjust=0)) where, > str(data1) 'data.frame': 295 obs. of 5 variables: $

Hello, I would like to run a script in which a loop is included. Since I'm new to R, I cannot manage the following problem. I really hope someone could help me out. Data in the variable Y should be removed from the simulated data set with probability 0.50 if the variable X has a value below zero, and with probability 0.10 if X has a value above zero (see script). However, the total number of

Dear Experts, The following is my "Iterator". When I try to write a new function with itel, I got error. This is what I have: > supDist<-function(x,y) return(max(abs(x-y))) > > myIterator <- function(xinit,f,data=NULL,eps=1e-6,itmax=5,verbose=FALSE) { + xold<-xinit + itel<-0 + repeat { + xnew<-f(xold,data) + if (verbose) { + cat( +

Hello all, I have an optimization routine that is giving me good results, but the results are not in the nice "model" format like "lm". How can I get optim results into a model so that I can use the clever 'fitted', 'residuals', and 'summary' functions? Using optim is the only way that I was able to make a model that 1) sums the betas to 1, 2)

Hi everyone, I've been having difficulty writing wrapper functions for some functions where those same functions include other functions with eval() calls where the environment is specified. A very simple example using function lmer from lme4: lmerWrapper <- function(formula, data, family = gaussian, method = c("REML", "ML", "PQL", "Laplace",

Folks, I'm doing fine with using orthogonal polynomials in a regression context: # We will deal with noisy data from the d.g.p. y = sin(x) + e x <- seq(0, 3.141592654, length.out=20) y <- sin(x) + 0.1*rnorm(10) d <- lm(y ~ poly(x, 4)) plot(x, y, type="l"); lines(x, d$fitted.values, col="blue") # Fits great! all.equal(as.numeric(d$coefficients[1] + m

Hi, I am trying to write a function to automate multiple graph generation. My data looks like: Table of numeric values with the following headers: timeM1 statusM1 xM1 timeM2 statusM2 xM2 timeM3 statusM3 xM3 1 2 3 4 5 6 Where M1,M2, M3 hve no similarity except they have a max string length of 7. Examples are mcw0045, adl0003, lei0101. Now, what I want to do is Function(M1, M2,

Dear Friends, I'm trying to find if there is a way to automate creation of the design matrix. Suppose we are interested in say running an autoregressive model. The user inputs the following data myfunAR <- function(y, order) {..... ...... } now here y is the data series and order represents the level of the process. In other words if order=2 then we have an AR (2) process. Now it is easy

Dear List, I have been trying to use library sm to do density estimation on a 3D dataset. I am using the current MacOS X binary of sm from CRAN. If I do this on a 2D dataset, sm.density returns a list including the component "estimate" which contains the density estimate over a uniform grid. When doing this with 3D data, although I get a nice plot (even when I don't ask for one),

This may sound like a very naive question, but... give two lists of coordinate pairs (x,y - Cartesian space) is there any simple way to compute the affine transformation matrix in R. I have a set of data which is offset from where i know it should be. I have coordinates of the current data, and matching coordinates of where the data should be. I need to compute the composition of the affine

Hi, I was hoping to clarify the exact behavior associated with this incantation: validate(fit.ols, method='cross', B=50) Output: index.orig training test optimism index.corrected n R-square 0.5612 0.5613 0.5171 0.0442 0.5170 50 MSE 1.3090 1.3086 1.3547 -0.0462 1.3552 50 Intercept 0.0000 0.0000 -0.0040 0.0040

I have a function XOLD() from a nearly verbatim port of legacy FORTRAN in a package. I have remplemented this function as XNEW() using much cleaner native R and built-in functions of R. I have switched the package to the XNEW(), but for historical reasons would like to retain the XOLD() somewhere in the package directory structure. An assertion through a README or other will point to

Hey there, I apologize if this is an irritatingly simple question ... I'm a new user. I can't understand why R flips the sign of all data values when reading in external text files (tab delimited or csv) with the read.delim or read.csv functions. The signs of data values also seem to be flipped after assigning a new data.frame from within R (xnew <-- edit(data.frame()). What am

Hi, I am trying to run some bootstraps with the boot package. When I run it with 400 replicates it does it ok, but then I need to run the same analysis but with 89, 86, 102 and 106 samples (for four different environments), and then is when I get the error message: > mybootstrap <- boot(Datos, mystat, 2000) Error in t.star[r, ] <- statistic(data, i[r, ], ...) : number of items to