johnjosephhorton

df.odw <- data.frame(application = df$application, p = df$p)
if(dbExistsTable(con, "predicted_response_probabilities")){
  dbRemoveTable(con, "predicted_response_probabilities")
}
dbWriteTable(con, "predicted_response_probabilities", df.odw)

johnjosephhorton

Papers

Published & Forthcoming Papers in Economics

"The Online Laboratory: Conducting Experiments in a Real Labor Market" (with David Rand and Richard Zeckhauser) Experimental Economics, 14:3 (2011), 399-425.

johnjosephhorton

(add-to-list 'load-path "~/.emacs.d/")
(add-to-list 'load-path "/usr/share/emacs/site-lisp/ess")

;; LaTeX stuff  
(load "auctex.el" nil t t)
(load "preview-latex.el" nil t t)

;; browse the kill ring
(load "browse-kill-ring.el" nil t t)

johnjosephhorton

## ----------------------------------------------------------------------------
## Author: Jason Morgan (borrowing heavily from code contained in Martin Elff's
##         memisc package).
##
## Notes:  Additional methods for mtable formatting of lme4 model
##         objects. Requires that the memisc package be loaded prior to
##         sourcing these functions.
## ----------------------------------------------------------------------------

setSummaryTemplate(mer = c("Log-likelihood" = "($logLik:f#)",

johnjosephhorton

library(ggplot2)
library(RPostgreSQL)
library(plyr)
library(scales) # now needed for ggplot2

drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname='db',
                 host = 'localhost',
                 user = 'db',
                 password ='pwd',

johnjosephhorton

Coefficients:
                                                Estimate Std. Error t value
(Intercept)                                    -0.070957   0.022836  -3.107
log(job_desc_length)                            0.032188   0.003320   9.695
grouptreatment                                  0.042197   0.008536   4.943
factor(level1)Business Services                 0.023747   0.027189   0.873
factor(level1)Customer Service                 -0.010701   0.030912  -0.346
factor(level1)Design & Multimedia               0.077688   0.017592   4.416
factor(level1)Networking & Information Systems  0.048057   0.030840   1.558
factor(level1)Sales & Marketing                 0.119671   0.017374   6.888

johnjosephhorton

n <- 100
x <- runif(n)
y <- 5*x 
missing <- runif(n) > .80
df <- data.frame(y = y, x = x, missing = missing)
df$x[missing] <- mean(df$x[!missing])
df$y[!df$missing] <- df$y[!df$missing] + .80
m <- lm(y ~ x + missing, data = df)
summary(m)

johnjosephhorton

def hashfree(input_dir):
    """Makes sure---before we start doing lots of intense computations---that
       there are not any files w/ hash in front of them (which the shutil utility
       cannot copy for some reason) """
    no_bad_files = True 
    bad_files = []
    for root, subFolders, files in os.walk(input_dir):
        for f in files:
            if re.search('\.#.*', f):
                no_bad_files = False

johnjosephhorton

#!/bin/bash 
cp $1 ~/Dropbox/Public/
echo "http://dl.dropbox.com/u/<your id>/"$1 | xclip -i -selection clipboard 

johnjosephhorton

get.sql.out <- function(file, nrows = -1){
    raw <- read.table(file, header=TRUE, nrow = nrows, sep="|", fill=TRUE, strip.white=TRUE)
    num.rows <- dim(raw)[1] 
    raw$index <- 1:num.rows 
    raw <- subset(raw, index != 1 & index != num.rows) 
    raw 
}