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What is the best way to find out what elements/numbers that are in one object are not in another. I came up with this method, but I'm wondering if there is a more efficient way (and one that doesn't seem so "clunky"). #Example id <- c(1,2,3,4,5,6,7,9,10) example.1 <- data.frame(id) #Second object, this time with an 8 in it. id <- c(1,2,3,4,5,6,7,8,9,10) example.2

I need to look through a dataset with two factor variables, and depending on certain criteria, create a new variable containing the data from one of those other variables. The problem is, R keeps making my new variable an integer and saving the data as a 1 or 2 (I believe the levels of the factor). I've tried using as.factor in the IF output statement, but that doesn't seem to work. Any

Hello, I have a dataset with student performance on a math test. There are multiple cases for each student (identified by id) and the concept as a variable. > rtest id test subject grade concept correct tested per_corr year 1 1 83 Mathema 8 8.2.D 1 1 100 2011 2 1 83 Mathema 8 8.3.A 1 2

I think the code is part of the RTAQ package but is not included in it, as I obtained it from https://r-forge.r-project.org/scm/viewvc.php/pkg/RTAQ/R/HAR_model.R?view=markup&root=blotter&sortby=author&pathrev=1028. It is not my code and I make no claim to other's good work, and apologize if I should even be posting it I am not sure, but in the transform function it allows to

I have a multiple text files, separated by a single space, that I need to combine into a single data.frame. Below is the track I'm on using list.files to capture the names of the files and then lapply with read.table. But I run into problems making a usable dataframe out of the data. #Creating example data in similar format to data I have sub <- rep(1,10) trial <- seq(1,10,1) size

I am working with xts dependent data, and my code is as follows (the problem is explained throughout): dat <- getdat("prices") dat <- read.zoo(dat, sep = "",format="%d/%m/%Y %H:%M", tz="", FUN=NULL, regular=TRUE, header=TRUE, index.column=1, colClasses=c("character", "numeric")) dat <- as.xts(dat)

I have 2 questions on ANOVA with 1 between subjects factor and 2 within factors. 1. I am confused on how to do the analysis with aov because I have seen two examples on the web with different solutions. a) Jon Baron (http://www.psych.upenn.edu/~baron/rpsych/rpsych.html) does 6.8.5 Example 5: Stevens pp. 468 - 474 (one between, two within) between: gp within: drug, dose aov(effect ~ gp * drug *

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >

When I try to create a Rasch simulation of data using the sim.rasch function, I get more items than I intend #My code library(eRm) #Number of items k <- 20 #Number of participants n <- 100 #Create Rasch Data #sim.rasch(persons, items, seed = NULL, cutpoint = "randomized") r.simulation <- sim.rasch(n,k) I end up with 20 participants and 100 items, but the instructions say

I'm trying to kick off some of the rust and learn some of the R packages for Rasch modeling. When I tried using the eRm package, I get item difficulty estimates for all my items accept the first (in terms of order) item. #Begin code library(eRm) r.simulation <- sim.rasch(20,100) r.data <- r.simulation$items #eRm results erm.rasch <- RM(r.data) names(erm.rasch) erm.items <-

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Thanks for the response. I was not very clear in my original request. What I am asking is if in a non-linear estimation problem using nls(), as the condition number of the Hessian matrix becomes larger, will the t-values of one or more of the parameters being estimated in general become smaller in absolute value -- that is, are low t-values a sign of an ill-conditioned Hessian? Typical