similar to: model selection using ANOVA

My question is probably pretty basic, but since I'm really new to R, here it goes .... I have two separate data frames that include class names and various other information on classes. I'm trying to create a match based on class names and if a match exists to create a third data frame with the class name. I was hoping to accomplish this with sapply, but I can't figure out if I can

I haven't used R in a couple of years, and now am trying something as simple as importing a csv file and am running into problems right away. * mydata <- read.csv (Wordata1.csv, sep="") Error in read.table(file = file, header = header, sep = sep, quote = quote, : object 'Wordata1.csv' not found *I've tried in both as as read.csv and read.table and still get the

I am working with a list of dates and I would like to replace each date with the one that comes after, ie. 1/1/07 will become 1/5/07, 1/5/07 will become 1/7/07, etc. The number of days between my dates always varies, so I can't just increase each one by 5 days or so. Does anyone know of a way I can do this in R? thank you [[alternative HTML version deleted]]

I'm trying to convert data from character to numeric. I've imported data as a csv file, I'm assuming that the import is a database - are all the columns in a database considered "vectors" and that they can be operated on individually Therefore I've tried the following mydata <- as.numeric(mydata$apples) when i then look at mydata again the named column is still

I'm trying to create a colored map that would show the number of students per state. My data frame consists of two columns - state and count. I'm using the following code library(maps) map("usa") library(plotrix) state.col<-color.scale(gre$count,0,0,c(0,1)) map("state",fill=TRUE,col=state.col) I'm getting a map, but the values are not being mapped to correct

Hi all, Could anybody please help me understand AIC and BIC and especially why do they make sense? Furthermore, I am trying to devise a new metric related to the model selection in the financial asset management industry. As you know the industry uses Sharpe Ratio as the main performance benchmark, which is the annualized mean of returns divided by the annualized standard deviation of returns.

I'm running the following code numbers <- 1:50 for (i in 1:50) assign(paste("model",numbers[i]),i)<-(lm(temp$Overall.Scaled.Score~temp$raw.score)) where I want R to create 50 different models-1:50, but get the following error message "target of assignment expands to non-language object". I've tried it with

I'm trying to create a fairly basic map using R. What i want to get is the map of the country with circles representing a count of students in each state. What I've done so far is as following - map("state") symbols(data1$count,circles=log(data1$count)*3,fg=col,bg=col,add=T,inches=F) this gives me the map of the country, but one that's not populated by my counts. Does

I am trying to overlay a data frame with lat and longitude(which refer to zip codes) on the map of US that I get by using map ("states"). Is there anyway to do this or do I have to resort to using maptools? thank you [[alternative HTML version deleted]]

In a generalized linear model with k covariates, there are 2(kth power) - 1 possible models (excluding interactions). Awhile ago a posting to R-help suggested Model Selection and Multimodel Inference, 2nd ed, by Burnham and Anderson as a good source for understanding model selection. They recommend (page 71) computing AIC differences over all candidate models in the set of possible models. After

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y

Hello I've been investigating using multi-model inference, based on calculating AIC and AIC weights, using the techniques outlined in Burnham and Anderson's (2002) book. However I notice a couple of emails in the R-help archive stating that there are errors in the technique. Are these errors associated with the particular implementation that B & A propose in their text, or is the

Greetings, I have recently come into some confusion over weather or not AIC results for comparing among models requires that they be nested. Reading Burnham & Anderson (2002) they are explicit that nested models are not required, but other respected statisticians have suggested that nesting is a pre-requisite for comparison. Could anyone who feels strongly regarding either position

I thought I would have another try at explaining my problem. I think that last time I may have buried it in irrelevant detail. This output should explain my dilemma: > dim(S) [1] 1455 269 > summary(as.vector(S)) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.160e+04 0.000e+00 0.000e+00 -4.132e-08 0.000e+00 8.636e+03 > sum(as.vector(S)==0)/(1455*269) [1]

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Hi everyone, I am wondering if there exists a stepwise regression function for the Bayesian regression model. I tried googling, but I couldn't find anything. I know "step" function exists for regular stepwise regression, but nothing for Bayes. Thanks -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26013725.html Sent from

Hi All, Sorry for this basic question as I am new to this R. I would like to know, is it possible to consider a matrix with some columns having numeric data and some other's with characters (strings) data? How do I get this type of data from a flat file. Thanks very much, mallika ____________________________________________________________________________ Mallika Veeramalai, Ph.D.,

I am analyzing particulate matter data (PM10) on a small data set (147 observations). I fitted a semi-parametric model and am worried about overfitting. How can one check for model fit in GAM? Jean G. Orelien

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf: