search for: dbraman

I'm wondering if there is a simple way to assign a quantile to a vector in a data frame, much like one could in Stata using centile. Let's say I want 100 slices in my assignation. I can easily see what the limits of each slice by using quantile: quantile(my.df$my.var, probs=seq(0, 1, 0.01)) But how do I assign the appropriate value to each row/record in my data frame? Clearly the

I want the standard error associated with a correlation. I can calculate using cor & var, but am wondering if there are libraries that already provide this function. [[alternative HTML version deleted]]

I'm coming from the AMOS world and am wondering if there is a simple way to do multiple hypothesis testing in the manner of BIC analyses in AMOS using the sem package in R. I've read the documentation, but don't see anything in there except for basic BIC scores. Perhaps someone has devised a simple way to compare the relative likelihood of all possible path-fittings within a

I've been playing around with various table tools, trying to construct a fairly simple cross-tab. It shouldn't be hard, but for some reason it turning out to be (for me). If I want to see how many men and how many women agree with a agree/disagree question (coded 1,0), I can do this: >attach(mydata) >mytable <- table(male, q1.bin) # gender and a binary response variable

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

I'm importing a lot of text tables of data (from Latent Gold) that includes hashes in some of the column names ("Cluster#1", "Cluster#2", etc.). Is there an easy way to strip the offending hashes out before pushing the text into a table or data frame? I thought I'd use gsub, e.g., but can't figure out how to read in a text file without reading it into a table or

I was trying to fit a curve to the number of people who identify as liberal by age. I got some puzzling results which suggested to me that I don't really understand how local polynomial fitting works. Why, I am wondering, is lowess producing a local fit of zero for every age? > liberal.bin [1] 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0

...if I can figure out what's going wrong. Duncan Murdoch ------------------------------ Message: 28 Date: Sun, 30 Aug 2009 13:53:33 -0400 From: Sunil Suchindran <sunilsuchindran@gmail.com> Subject: Re: [R] standard error associated with correlation     coefficient To: Donald Braman <dbraman@law.gwu.edu> Cc: r-help <r-help@r-project.org> Message-ID:     <f2faed070908301053g4bab9b95n681b31d0ace49300@mail.gmail..com> Content-Type: text/plain Here are some options for confidence intervals. #by hand sample_r <- .5 n_sample <- 100 df <- n_sample-1 fisher_z <- 0....

...meuble Ousseynou Thiam Gueye Rue de Thies Point E, BP 15702 CP 12524 Dakar Fann Senegal Phone: +221 33 869 3986 Email: f.wouterse@cgiar.org     [[alternative HTML version deleted]] ------------------------------ Message: 35 Date: Wed, 8 Jul 2009 10:46:46 -0400 From: Donald Braman <dbraman@law.gwu.edu> Subject: [R] recoding strings containing colons To: r-help <r-help@r-project.org>, jfox@mcmaster.ca Message-ID:     <a6f585d20907080746x410ce8d3v83f00e6d567b04e2@mail.gmail..com> Content-Type: text/plain; charset=ISO-8859-1 Curious to know if recode can work with string...

Is there an easy way to invert a table? (not to solve for the inverted matrix, just swap rows for columns & vice versa). I've gone through my data manipulation bible (Phil Spector's book), but to no avail. [[alternative HTML version deleted]]

I'm stumbling my way through manipulating data in multiply imputed datasets, and have run into a problem translating code I used to run on my pre-imputed dataset to multiple datasets. The imputation runs just fine, as does the reading of the mi data sets into an imputationList. I run into trouble, though, when I try to construct a scale across all the data sets. Is there a simple way to do

This is a simple problem that has stumped me: I'm trying to loop through a few dozen variable names in graphs. I've tried various approaches like this: attach(mydata) ivs <- c("oneiv", "anotheriv", "yetanotheriv") dvs <- c("onedv", "anotherdv", "yetanotherdv") for (iv in ivs) { for (dv in dvs) { graphname <- paste(iv,

Hi folks, I have five imputed data sets and would like to apply the same recoding routines to each. I could do this sort of thing pretty easily in Stata using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race

Hi folks, I'm just learning how to use maps. As an initial foray, I'm mapping the states that have "duty to retreat" (blue) and "stand your ground" (red) self-defense standards. Here is my extremely naive script: dtr <- c('alabama', 'arizona', 'conneticut', 'delaware', 'dist of columbia' , 'hawaii',