jepusto
/ simulation-in-R-2016
Created Sep 28, 2016
Rmd for my presentation on simulation studies in R, Quant Methods brownbag colloquium 2016/09/28
View simulation-in-R-2016
| --- | |
| title: "Designing simulation studies in R" | |
| author: "James E. Pustejovsky" | |
| date: "September 28, 2016" | |
| output: | |
| ioslides_presentation: | |
| css: custom.css | |
| widescreen: true | |
| transition: faster | |
| --- |
jepusto
/ bug-in-nlme::getVarCov.R
Last active Aug 11, 2016
View bug-in-nlme::getVarCov.R
| # Demonstrate the problem with gls model | |
| library(nlme) | |
| data(Ovary) | |
| gls_raw <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data = Ovary, | |
| correlation = corAR1(form = ~ 1 | Mare), | |
| weights = varPower()) |
jepusto
/ Fatal crashes by city, 2006-2015
Created Aug 24, 2015
View Fatal crashes by city, 2006-2015
| library(tidyr) | |
| library(dplyr) | |
| library(stringr) | |
| library(ggplot2) | |
| cities_select <- c("HOUSTON","SAN ANTONIO","DALLAS","AUSTIN","FORT WORTH","EL PASO") | |
| #---------------------------------------- | |
| # get population estimates | |
| #---------------------------------------- |
jepusto
/ Austin crashes - annual data figures
Last active Aug 29, 2015
Code for figures displaying annual number of fatal automobile crashes, fatalities, etc. in Austin and Travis County, 2003-2015
View Austin crashes - annual data figures
| library(tidyr) | |
| library(dplyr) | |
| library(stringr) | |
| library(ggplot2) | |
| #-------------------------------- | |
| # format the data for graphing | |
| #-------------------------------- | |
| crash_dat <- read.csv("http://blogs.edb.utexas.edu/pusto/files/2015/08/Yearly_crash_data_Austin_and_Travis_County.csv") |
jepusto
/ metafor-BRL.R
Created Apr 25, 2014
Bias-reduced linearization covariance estimator and degrees of freedom for multi-variate meta-analysis models
View metafor-BRL.R
| require(Formula) | |
| require(metafor) | |
| require(sandwich) | |
| require(zoo) | |
| require(lmtest) | |
| #----------------------------------------------- | |
| # Identify outer-most clustering variable | |
| #----------------------------------------------- |
jepusto
/ RVE-correlated.R
Created Apr 21, 2014
Correlated effects example from Tanner-Smith & Tipton (2013)
jepusto
/ metafor-sandwich.R
Created Apr 21, 2014
Calculate sandwich covariance estimators for multi-variate meta-analysis models
View metafor-sandwich.R
| require(Formula) | |
| require(metafor) | |
| require(sandwich) | |
| require(zoo) | |
| require(lmtest) | |
| #----------------------------------------------- | |
| # Functions for making sandwich standard errors | |
| #----------------------------------------------- |
jepusto
/ RVE-hierarchical.R
Last active Aug 29, 2015
Hierarchical effects example from Tanner-Smith & Tipton (2013)
View RVE-hierarchical.R
| # robumeta calculations | |
| library(grid) | |
| library(robumeta) | |
| data(hierdat) | |
| HTJ <- robu(effectsize ~ males + binge, | |
| data = hierdat, modelweights = "HIER", | |
| studynum = studyid, | |
| var.eff.size = var, small = FALSE) |
jepusto
/ Simulate Welch t-test in parallel
Last active Dec 31, 2015
An adaptation of the Welch t-test simulation, for running in parallel on the Stampede server of the Texas Advanced Computing Cluster.
View Simulate Welch t-test in parallel
| #---------------------------------------------- | |
| # data-generating model | |
| #---------------------------------------------- | |
| two_group_data <- function(iterations, n, p, var_ratio, delta) { | |
| Group <- c(rep("C", n * p), rep("T", n * (1 - p))) | |
| Y_C <- matrix(rnorm(iterations * n * p, mean = 0, sd = 1), n * p, iterations) | |
| Y_T <- matrix(rnorm(iterations * n * (1 - p), mean = delta, sd = sqrt(var_ratio)), n * (1 - p), iterations) | |
| dat <- data.frame(Group, rbind(Y_C, Y_T)) | |
| return(dat) |
jepusto
/ Simulate Welch t-test
Last active Dec 29, 2015
A simulation evaluating the confidence interval coverage for a difference in means, allowing for unequal variances, based on Welch's approximation for the degrees of freedom. The code demonstrates the basic structure of a simulation study in R.
View Simulate Welch t-test
| #---------------------------------------------- | |
| # data-generating model | |
| #---------------------------------------------- | |
| two_group_data <- function(iterations, n, p, var_ratio, delta) { | |
| Group <- c(rep("C", n * p), rep("T", n * (1 - p))) | |
| Y_C <- matrix(rnorm(iterations * n * p, mean = 0, sd = 1), n * p, iterations) | |
| Y_T <- matrix(rnorm(iterations * n * (1 - p), mean = delta, sd = sqrt(var_ratio)), n * (1 - p), iterations) | |
| dat <- data.frame(Group, rbind(Y_C, Y_T)) | |
| return(dat) |