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Francis Smart EconometricsBySimulation

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@EconometricsBySimulation
EconometricsBySimulation / lmOut
Last active Dec 8, 2020
A simple command to grab coefficients, t-stats, p-values, f-stats, etc from a regression and export them as an easy to use spreadsheet.
View lmOut
lmOut <- function(res, file="test.csv", ndigit=3, writecsv=T) {
# If summary has not been run on the model then run summary
if (length(grep("summary", class(res)))==0) res <- summary(res)
co <- res$coefficients
nvar <- nrow(co)
ncol <- ncol(co)
f <- res$fstatistic
formatter <- function(x) format(round(x,ndigit),nsmall=ndigit)
View gist:fe40ec9f749213d1240011684001dda5

emailto: psteiner@umd.edu student: Francis Smart fsmart@gmail.com EDMS769G

# Text Network graph
#           Student Ability _______________________
#            /           |                         \_____
#          /             |  Public/Private Transport     \
#         ↓              ↓     ↓                          ↘
View ConsumptionInference.R
# Estimating Weekly consumption from periodic purchasing data
library(dplyr)
library(data.table)
library(reshape)
library(tidyr)
library(ggplot2)
# Day Purchase
View FEC-Clinton-PieCharts.R
rm(list=ls())
library(data.table)
library(dplyr)
library(ggplot2)
library(scales)
setwd('Z:/Data/FEC')
dsum <- function(...) dplyr::summarize(...)
View Campaign-Finance-Density.R
rm(list=ls())
library(data.table)
library(dplyr)
library(ggplot2)
setwd('Z:/Data/FEC')
dsum <- function(...) dplyr::summarize(...)
to.data.table <- function(x) {(class(x) <- class(data.table())) ;x}
View 2013-08-21-Doodler
# R - Doodling in R
# I am working on creating some functions that will be capable of creating shapes and plots that look hand drawn.
# I have made some progress in this goal.
# In that process I have also discovered that I can make some doodles that look hand drawn as well.
# In order to accomplish the goal of simulating hand drawing I want to simulate the momentum of hand writing.
@EconometricsBySimulation
EconometricsBySimulation / gist:6235945
Created Aug 14, 2013
2013-07-30 A bit of code showing how to do Structural Equation Modelling
View gist:6235945
clear
set obs 4000
gen id = _n
gen eta1 = rnormal()
gen eta2 = rnormal()
* Generate 5 irrelevant factors that might affect each of the
View server.r
shinyServer(function(input, output) {
output$distPlot <- renderPlot({
# Take a dependency on input$goButton
input$goButton
# Use isolate() to avoid dependency on input$obs
dist <- isolate(rnorm(input$obs))
hist(dist)
})
})
View heightweight.csv
sex ageYear ageMonth heightIn weightLb
f 11.91667 143 56.3 85
f 12.91667 155 62.3 105
f 12.75 153 63.3 108
f 13.41667 161 59 92
f 15.91667 191 62.5 112.5
f 14.25 171 62.5 112
f 15.41667 185 59 104
f 11.83333 142 56.5 69
f 13.33333 160 62 94.5
View server.r
shinyServer(function(input, output) {
output$main_plot <- renderPlot(width = 400, height = 300, {
hist(faithful$eruptions,
probability = TRUE,
breaks = as.numeric(input$n_breaks),
xlab = "Duration (minutes)",
main = "Geyser eruption duration")