regression

I am transitioning from Stata to R. In Stata, if I label a factor levels (say--0 and 1) to (M and F), 0 and 1 would remain as they are. Moreover, this is required for dummy-variable linear regression in most software including Excel and SPSS. However, I've noticed that R defaults factor levels to 1,2 instead of 0,1. I don't know why R does this although regression internally (and correctly) assumes 0 and 1 as the factor variable. I would appreciate any help. Here's what I did: Try #1: sex<-c(0,1,0,1,1) sex<-factor(sex,levels = c(1,0),labels = c("F","M")) str(sex) Factor w/ 2 levels "F","M": 2

I've written a function to iteratively forecast models built using the package dyn, and I'd like some feedback on it. Is there a better way to do this? Has someone written canonical "forecast" methods for the dyn class (or dynlm class), or am I venturing into uncharted territory here? ipredict <-function(model, newdata, interval = "none", level = 0.95, na.action = na.pass, weights = 1) { P<-predict(model,newdata=newdata,interval=interval, level=level,na.action=na.action,weights=weights) for (i in seq(1,dim(newdata)[1])) { if (is.na(newdata[i])) { if (interval=="none") { P[i]<-predict(model

I'm trying to perform a Difference in Differences (with panel data and fixed effects) analysis using Python and Pandas. I have no background in Economics and I'm just trying to filter the data and run the method that I was told to. However, as far as I could learn, I understood that the basic diff-in-diffs model looks like this: I.e., I am dealing with a multivariable model. Here it follows a simple example in R: https://thetarzan.wordpress.com/2011/06/20/differences-in-differences-estimation-in-r-and-stata/ As it can be seen, the regression takes as input one dependent variable and tree sets

I'm unable to cluster standard errors using R and guidance based on this post . The cl function returns the error: Error in tapply(x, cluster1, sum) : arguments must have same length After reading up on tapply I'm still not sure why my cluster argument is the wrong length, and what is causing this error. Here is a link to the data set that I'm using. https://www.dropbox.com/s/y2od7um9pp4vn0s/Ec%201820%20-%20DD%20Data%20with%20Controls.csv Here is the R code: # read in data charter<-read.csv(file.choose()) View(charter) colnames(charter) # standardize NAEP scores charter$naep.standardized <-