jknowles

# Script to demonstrate distributions
library(VGAM)
library(eeptools)
library(shiny)
library(ggplot2)




shinyServer(function(input,output){

jknowles

# Script to demonstrate distributions
library(eeptools)
library(shiny)
library(ggplot2)


rnormcor <- function(x,rho) rnorm(1,rho*x,sqrt(1-rho^2))

shinyServer(function(input,output){
  output$distPlot<-reactivePlot(function(){

jknowles

library(shiny)
library(scales)

shinyServer(function(input,output){
  trialInput<-reactive(function(){
    bias<-input$coin
    sims<-input$obs
    reps<-input$reps
    trials<-rbinom(reps,sims,0.5+bias)
  })

jknowles

library(shiny)
# data from : http://is-r.tumblr.com/post/35266021903/five-thirty-hate
election.data <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/election2012.csv")
#election.data <- read.csv("electiondata.csv")

shinyServer(function(input,output){
  
  output$voteplot<-reactivePlot(function(){
    
    Mode <- function(X) {

jknowles

library(lavaan)
model <- ' 
  # latent variable definitions
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + a*y2 + b*y3 + c*y4
     dem65 =~ y5 + a*y6 + b*y7 + c*y8

  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60

jknowles

How to Ask for Help using R
========================================================

The key to getting good help with an R problem is to provide a minimally working 
reproducible example (MWRE). Making an MWRE is really easy with R, and it will 
help ensure that those helping you can identify the source of the error, and 
ideally submit to you back the corrected code to fix the error instead of sending
you hunting for code that works. To have an MWRE you need the following items:

- a minimal dataset that produces the error

jknowles

# Helper functions that allow string arguments for  dplyr's data modification functions like arrange, select etc. 
# Author: Sebastian Kranz

# Examples are below

#' Modified version of dplyr's filter that uses string arguments
#' @export
s_filter = function(.data, ...) {
  eval.string.dplyr(.data,"filter", ...)
}

jknowles

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

jknowles

# Calculate the AUC of a GLM model easily
# Jared Knowles
# model = a fitted glm in R
# newdata = an optional data.frame of new fitted values
auc.glm <- function(model, newdata = NULL){
  if(missing(newdata)){
    resp <- model$y
    # if(class(resp) == "numeric"){
    #   resp <- factor(resp)
    # }

jknowles

predict.robust <- function(model, data, robust_vcov = NULL, level = 0.95, 
                           interval = "prediction"){
  # adapted from
  # https://stackoverflow.com/questions/38109501/how-does-predict-lm-compute-confidence-interval-and-prediction-interval
  # model is an lm object from r
  # data is the dataset to predict from
  # robust_vcov must be a robust vcov matrix created by V <- sandwich::vcovHC(model, ...)
  # level = the % of the confidence interval, default is 95%
  # interval = either "prediction" or "confidence" - prediction includes uncertainty about the model itself
  if(is.null(robust_vcov)){