search for: indepent

Hello, Is there a better way to use paste such as: a = paste(colnames(list.indep)[1],colnames(list.indep)[2],colnames(list.indep)[3],colnames(list.indep)[4],colnames(list.indep)[5],sep="+") > a [1] "aa+dummy1+dummy2+bb+cc" I tried a = paste(colnames(list.indep)[1:5],sep="+") > a [1] "aa" "dummy1" "dummy2"

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1

Hi useRs, I'm experiencing something quite weird with glm() and weights, and maybe someone can explain what I'm doing wrong. I have a dataset where each row represents a single case, and I run glm(...,family="binomial") and get my coefficients. However, some of my cases have the exact same values for predictor variables, so I should be able to aggregate up my data frame and

I commend you to (a) the recent article by Doug Bates on "Fitting nonlinear mixed models in R" pp. 27-30 in the latest issue of "R News" available from "www.r-project.org" -> Newsletter and (b) Doug's book with Pinheiro (2000) Mixed-Effects Models in S and S-PLUS (Springer). I suggest you try the same analysis using in "lmer", library(lme4), and

Hello R community, I have a question about the logistic regression function. Specifically, when the predictor variable has not just 0's and 1's, but also fractional values (between zero and one). I get a warning when I use the "glm(formula = ... , family = binomial(link = "logit"))" which says: "In eval(expr, envir, enclos) : non-integer #successes in a binomial

Hello R users, I am new to R and am wondering if anyone can help me out with the following issue: I wrote a function to build ts models using different inputs, but when R displays the call for a model, I cannot tell which variables it is using because it shows the arguments instead of the real variables passed to the function. (e.g Call: lm(formula = dyn(dep ~ lag(dep, -1) + indep)) --->

In a function, say foo.glm for glm objects I want to use the name of the object as a label for some output, but *only* if a glm object was passed as an argument, not a call to glm() producing that object. How can I distinguish these two cases? For example, I can use the following to get the name of the argument: foo.glm <- function(object) { oname <- as.character(sys.call())[2]

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

I wonder if anyone out there has written a routine to solve the simple linear calibration problem? - fit regression of y vs x - estimate the value x0 (with 95% CI) that gives y0 Thanks for any help. Bill -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Could anyone tell me what am I doing wrong? > pro<-function(indep,dep){ + d<-data.frame(indep) + form<-formula(lm(dep~.,data=d)) + forward<-step(lm(dep~X1,data=d),scope=form,trace=0,direction='f') + return(forward) + } > pro(m,q) Error in inherits(x, "data.frame") : Object "d" not found Where q is a vector with the dependent variable's

Dear R community, I am a newbie to R and I am using lme() to analyzed a two way repeated measures ANCOVA on some data I have gathered. In producing some graphs based on the fixed effects I noticed that I get vary different results depending on how I order my levels in my factor statement (see code below). Now, I have read that different models treat data from the factor class

Hello R users, I have been trying to output all my results (text, plots, etc) into the same postscript file as one document, but have been unable to...Can anyone help me improve my code below so that I can accomplish this? Currently I have to output them separately then piece them back together into one document.. Thanks in Advance for any help! options (scipen=999, digits=7)

I'm trying to develop some graphic methods for glm objects, but they only apply for models where all predictors are discrete factors. How can I test for this in a function, given the glm model object? That is, I want something that will serve as an equivalent of is.discrete.glm() in the following context: myplot.glm <- function(model, ...) { if (!inherits(model,"glm"))

Dear R-users, An updated version of the energy package, energy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of

Dear R-users, An updated version of the energy package, energy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of

Default arguments are evaluated in the function frame, not in the calling environment (nor in the same place as explicit arguments). > Which to me reads that a with statement as above is equivalent to > > > attach(data) ; aov.SS1(y=Obs) ; detach(data) > > Or is that just wishful thinking?? The latter. On Thu, 7 Oct 2004, RenE J.V. Bertin wrote: > Hello, > >

...my problem is like this: now after processing the varibles, the remaining 160 varibles(independent) and a dependent y. when I used PLS method, with 10 components, the good r2 can be obtained. but I donot know how can I express my equation with the less varibles and the y. It is better to use less indepent varibles. that is how can I select my indepent varibles. Maybe GA is good method, but now I donot gasp it. and can you give me more good varibles selection's methods. and In R, which method can be used to select the potent varibles . and using the selected varibles to model a equation wi...

Hello world, I'm trying to do an anova on data in data.set, dependent variable is a column named "dep.var", grouping variable is in a column called "indep.var", and is.factor(indep.var) is TRUE... why can't I just do aov(dep.var ~ indep.var, data = data.set)? What have I done to deserve this?! What gives? Am I missing something totlly obvious? R-base-1.0.0-1,

Hi, I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below). I'm using R2.13 Can you help me? Do you know, what is happens? Thanks Raquel multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo))) # two independent datasets (dim = 13447 x 36) mp =