similar to: using "unstack" inside my function: that old scope problem again

Hi Everyone, I am trying to understand the unstack() function but after struggling for two days, I have given up. More specifically, I am trying the exercises at the end of Chapter 1 of Data Analysis and Graphics Using R by Maindonald and Braun, 2nd ed. Exercise 18 (p. 41) asks to unstack the Rabbit data frame from the MASS package to get a certain data frame that is shown in the exercise.

Folks, I have the following dataframe: > x <- structure(list(name = c("EU B", "EU B", "EU B", "EU B", "EU B", "EU B", "AU A", "AU A", "AU A", "AU A", "AU A", "AU A"), date = c("2010-10-11", "2010-10-12", "2010-10-13",

Hi all, I'm having a problem restructuring my data the way I'd like it. I have data that look like this: Candidate.ID Specialty Office Score 110002 C London 47 110002 C East 48 110003 RM West 45 110003 RM

Dear R users, I'm having some problems with the stack() and unstack() functions, and wondered if you could help. I have a large data frame (400 rows x 2000 columns), which I need to reduce to a single column of values (and therefore 800000 rows), so that I can use it in other operations (e.g., generating predictions from a GLM object). However, the problem I'm having can be reproduced

Dear R Community, I am currently facing this seemingly obscure Problem with Panel Corrected Standard Errors (PCSE) following Beck & Katz (1995). As the authors suggest, I regressed a linear model (tmodel) with lm() with option "na.action=na.exclude" (I have also tried other options here). My dataframe is organized in pooled times series fashion, but with various missing values

Dear list, I am trying to replicate an econometrics study that was orginally done in Stata. (Blanton and Blanton. 2009. A Sectoral Analysis of Human Rights and FDI: Does Industry Type Matter? International Studies Quarterley 53 (2):469 - 493.) The model I try to replicate is in Stata given as xtpcse total_FDI lag_total ciri human_cap worker_rts polity_4 market income econ_growth log_trade

I'm trying to use a lattice function within a function and have problems passing the "groups" argument properly. Let's say I have a data frame d <- data.frame(x = rnorm(100), y = c("a", "b")) and want to plot variable x in a densityplot, grouped by the variable y, then I would do something like densityplot(~ x, d, groups = y) If however I wanted to

I'm wondering how to save an object (models like lm, loess, etc) in a DB to retrieve and use it afterwards, an example: wind_ms <- abs(rnorm(24*30)*4+8) air_kgm3 <- rnorm(24*30, 0.1)*0.1 + 1.1 wind_dg <- rnorm(24*30) * 360/7 ms <- c(0:25) kw_mm92 <- c(0,0,0,20,94,205,391,645,979,1375,1795,2000,2040) kw_mm92 <- c(kw_mm92, rep(2050, length(ms)-length(kw_mm92))) modelspline

Hi It clear that to do a classification with svm under 10-fold cross validation one uses svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross = 10) What corresponds to the nnet? nnet(.....,cross=10)? Regards

(R.1.9.1; win2000) Since it's about the tenth time I had to write an "if" around this to catch the error ... Let's look at the line myfit<-lm(res~groupvar,data=Data) Here res is of numeric type and groupvar is a factor. On first sight, it would be logical that if groupvar had only one (single) level we would get: Error in "contrasts<-"(`*tmp*`, value =

I can think of several ways to blunt force hard code what I want but I imagine there is a command or two that can be easily combined to do this: I have a data frame with about 23000 observations. There first variable is the group to which the observation belongs (about 500 different groups). The second variable is a response for each observation that is a 1,2,3,4 or 5. I want to be able to

Hello, I have some code that currently works fine and I am endeavoring to convert the major pieces of it into functions. This involves taking "hard coded" names of variables that are used in various places and figuring out how to abstract them out into functions where the arguments (i.e. a list of variables)?can be passed to the parent function and used within that function for various

Hi all, I am using the lda function from the MASS library to measure the discriminance of different variables with respect to different grouping variables by using lda( RESULTVARS[, 1:750] , GROUPVAR , tol=0 ) where RESULTVARS contains some 750 different variables. Occasionally there is a variable within RESULTVARS that has the same values for all values of GROUPVAR, ie no variance so

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on

Dear all, does anyone know how I can extract specific p-values for covariates from an aftreg object? After fitting a model with aftreg I can find all different variables by using str(), but there's no place where p-values are kept. The odd thing is that print() displays them correctly. EXAMPLE: > testdata start stop censor groupvar var1 var2 1 0 1 0

One would normally want the original order that so that one can stack a list, operate on the result and then unstack it back with the unstacked result having the same ordering as the original. LL <- list(z = 1:3, a = list()) # since we can't do s <- stack(LL,. drop = FALSE) do this instead: s <- transform(stack(LL), ind = factor(as.character(ind), levels = names(LL))) unstack(s)

Hello all,, Im looking for a simple function to produce a crosstab from a dumped sql query result. Its very hard to produce crosstabs with most databases (Access being the exception), so with the vast array of R packages, Im sure this has to have already been implemented somewhere. Examples are always good: Take a csv dump like name code user1 100 user2 100 user1 200 user2 210 user1 300 user2

Hello everyone I appologize for my lack of experience in statistical methods. I am an R user begginer and I am running a logit model using "zelig" and "pcse" packages. I will go to the point and is that Im having problems with interpreting the results of my models.. It is really simple (I guess for the most advanced scholars) however I really dont understand how to interpret

Hello R - users, This may sound simple to may people: I have a list of data as follows type value y 7 y 7 y 8 y 8 y 8 y 9 y 9 y 9 y 9 y 10 y 10 y 10 y 10 y 11 y 11 y 12 y 12 y 14 y 14 y 14 y 15 y 17 y 20 y 20 y 20 y 20 y 25