Adam Bear adambear91

## Templeton_Presentation.ipynb

      
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                adambear91
                / Templeton_Presentation.ipynb
            
            
              Created
              January 29, 2019 20:26
            
              
                Code and data for Templeton presentation 1/30/19.
              
          
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## tobit_vs_OLS_simulations.R
require(ggplot2)
require(GGally)
require(VGAM)

# Function to create a simulated data set and run models on it
oneRun <- function(number_of_obs,eff_IV1,eff_IV2,baseline,sd,lowerCutoff,upperCutoff,roundOn){
  # partition data into 4 conditions, specified by IV1 and IV2
  partition <- number_of_obs /4
  df <- expand.grid(IV1 = c(T,F), IV2 = c(T,F))
  df <- df[rep(row.names(df), partition), 1:2]

## AA_BearKaganRand_master_file.m
%   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %
%                 CODE FOR BEAR, KAGAN & RAND (2017) PROCB
%
% This MATLAB script runs the low mutation limit (steady state) calculations
% shown in Figures S1-S12
%
% This script calls the following functions:
%
% calculate_payoff_YDYI(strategy1,strategy2,b,c,d,p,YD,YI)
%       - returns the payoff of strategy1 against strategy2 given

## AA_BearRand_master_file.m
%   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %
%                 CODE FOR BEAR RAND (2016) PNAS
%
% This MATLAB script runs the low mutation limit (steady state) calculations
% shown in Figures 2a and S1-S3, as well as the agent-based simulations
% for higher mutation rates shown in Figure S3, and plots the results.
%
% This script calls the following functions:
%
% calculate_payoff(strategy1,strategy2,b,c,d,p)
	require(ggplot2)
	require(GGally)
	require(VGAM)

	# Function to create a simulated data set and run models on it
	oneRun <- function(number_of_obs,eff_IV1,eff_IV2,baseline,sd,lowerCutoff,upperCutoff,roundOn){
	# partition data into 4 conditions, specified by IV1 and IV2
	partition <- number_of_obs /4
	df <- expand.grid(IV1 = c(T,F), IV2 = c(T,F))
	df <- df[rep(row.names(df), partition), 1:2]
	% % % % % % % % % % % % % % % % % % %
	% CODE FOR BEAR, KAGAN & RAND (2017) PROCB
	%
	% This MATLAB script runs the low mutation limit (steady state) calculations
	% shown in Figures S1-S12
	%
	% This script calls the following functions:
	%
	% calculate_payoff_YDYI(strategy1,strategy2,b,c,d,p,YD,YI)
	% - returns the payoff of strategy1 against strategy2 given
	% % % % % % % % % % % % % % % % % % %
	% CODE FOR BEAR RAND (2016) PNAS
	%
	% This MATLAB script runs the low mutation limit (steady state) calculations
	% shown in Figures 2a and S1-S3, as well as the agent-based simulations
	% for higher mutation rates shown in Figure S3, and plots the results.
	%
	% This script calls the following functions:
	%
	% calculate_payoff(strategy1,strategy2,b,c,d,p)