Daniel Lakens Lakens

## standardized_alpha_power_analysis.R
#Standardized p-value
#This is untested alpha software provided with no guarantees - use at your own risk

#Good 1982: P SQRT(N/100)
#Enter the observed p-value and the sample size

p_stan <- function(p, N){
  p * sqrt(N/100)
}

## emailing_students_from_R.R
#Load packages
library(readxl)
library(mailR)

#Read student data
info <- read_excel("student_names_email.xls",
                   sheet = 1,
                   col_names = TRUE)

#Loop from 1 to the number of email addresses in the spreadsheet

## p-value_misconceptions_figures.R
options(scipen=999) #disable scientific notation for numbers

#Figure 1 & 2 (set to N <- 5000 for Figure 2)
# Set x-axis upper and lower scalepoint (to do: automate)
low_x<--1
high_x<-1
y_max<-2

#Set sample size per group and effect size d (assumes equal sample sizes per group)
N<-50 #sample size per group for indepndent t-test

## blog_hits.csv

          
            NAME
            COMMENTS
            HITS
            DATE

            
              Equivalence testing in jamovi
              12
              1870
              14-3-2017

            
              No  the p-values are not to blame: Part 53
              1
              2675
              10-3-2017

            
              How p-values solve 50% of the problems with p-values
              4
              2273
              6-3-2017

            
              ROPE and Equivalence Testing: Practically Equivalent?
              9
              1991
              12-2-2017

            
              Examining Non-Significant Results with Bayes Factors and Equivalence Tests
              26
              2332
              29-1-2017

            
              Why Type 1 errors are more important than Type 2 errors (if you care about evidence)
              32
              7435
              18-12-2016

            
              TOST equivalence testing R package (TOSTER) and spreadsheet
              8
              3885
              9-12-2016

            
              Why Within-Subject Designs Require Fewer Participants than Between-Subject Designs
              0
              4792
              12-11-2016

            
              Improving Your Statistical Inferences Coursera course
              7
              3154
              6-10-2016

## TOSTvsROPE.R
#Make sure JAGS is installed or R will crash: https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/
require(BEST) #To calculate HDI
require(TOSTER) #To calculate Equivalence Tests

set.seed(1)

x<-rnorm(100) #Generate 100 random normally distributed observations
y<-rnorm(100) #Generate 100 random normally distributed observations

#ROPE test

## volunteer_reanalysis.R
# Code from https://osf.io/aqi5j/ made by original authors, adapted by Daniel Lakens
# Download the csv file from https://osf.io/aqi5j/
# Paper: http://www.tandfonline.com/doi/full/10.1080/23743603.2016.1273647

require(car)

# IMPORTANT: change this to match your path/file
fileLocation <- 'C:\\Users\\Daniel\\surfdrive\\Data\\blog volunteer study\\VolunteeringDataFile_05.09.16.csv'

# Dienes functions

## 3dplotLR.R
#Create single plot for graph----
power <- seq(0.01,1,0.01)
alpha <- seq(0.01,1,0.01)
pH0 <- 0.5
tp<-((1-pH0)*power)
fp<-((pH0)*alpha)
likelihood_ratio<-outer(tp, fp, "/")

#Create color shading
nrz<-nrow (likelihood_ratio)

## WithinBetween.R
if(!require(ggplot2)){install.packages('ggplot2')}
library(ggplot2)
if(!require(Rcpp)){install.packages('Rcpp')}
library(Rcpp)
if(!require(MASS)){install.packages('MASS')}
library(MASS)
options(digits=10,scipen=999)


#Set color palette

## DanceBayesFactors.R
if(!require(BayesFactor)){install.packages('BayesFactor')}
library(BayesFactor)
#to get emoticons for each test, set wait to 0.5 and showfaces to 1.
#When running large number of simulations, set wait to 0 and showfaces to 0.
options(scipen=20) #disable scientific notation for numbers
waitx<-0.5 #To see a small wait between individual trials, set a wait time to e.g., 0.5
showfaces<-1 #Set to 0 if you do not want the faces, set to 1 if you want to see the faces

cohensd<-0.3 #set true effect size
n<-75 #sample size in each group

## test.R
if(!require(ggplot2)){install.packages('ggplot2')}
library(ggplot2)
if(!require(pwr)){install.packages('pwr')}
library(pwr)

nSims <- 100000 #number of simulated experiments

M<-106 #Mean IQ score in the sample
n<-26 #set sample size
SD<-15 #SD of the simulated data
	#Standardized p-value
	#This is untested alpha software provided with no guarantees - use at your own risk

	#Good 1982: P SQRT(N/100)
	#Enter the observed p-value and the sample size

	p_stan <- function(p, N){
	p * sqrt(N/100)
	}
	#Load packages
	library(readxl)
	library(mailR)

	#Read student data
	info <- read_excel("student_names_email.xls",
	sheet = 1,
	col_names = TRUE)

	#Loop from 1 to the number of email addresses in the spreadsheet
	options(scipen=999) #disable scientific notation for numbers

	#Figure 1 & 2 (set to N <- 5000 for Figure 2)
	# Set x-axis upper and lower scalepoint (to do: automate)
	low_x<--1
	high_x<-1
	y_max<-2

	#Set sample size per group and effect size d (assumes equal sample sizes per group)
	N<-50 #sample size per group for indepndent t-test
NAME	COMMENTS	HITS	DATE
Equivalence testing in jamovi	12	1870	14-3-2017
No the p-values are not to blame: Part 53	1	2675	10-3-2017
How p-values solve 50% of the problems with p-values	4	2273	6-3-2017
ROPE and Equivalence Testing: Practically Equivalent?	9	1991	12-2-2017
Examining Non-Significant Results with Bayes Factors and Equivalence Tests	26	2332	29-1-2017
Why Type 1 errors are more important than Type 2 errors (if you care about evidence)	32	7435	18-12-2016
TOST equivalence testing R package (TOSTER) and spreadsheet	8	3885	9-12-2016
Why Within-Subject Designs Require Fewer Participants than Between-Subject Designs	0	4792	12-11-2016
Improving Your Statistical Inferences Coursera course	7	3154	6-10-2016
	#Make sure JAGS is installed or R will crash: https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/
	require(BEST) #To calculate HDI
	require(TOSTER) #To calculate Equivalence Tests

	set.seed(1)

	x<-rnorm(100) #Generate 100 random normally distributed observations
	y<-rnorm(100) #Generate 100 random normally distributed observations

	#ROPE test
	# Code from https://osf.io/aqi5j/ made by original authors, adapted by Daniel Lakens
	# Download the csv file from https://osf.io/aqi5j/
	# Paper: http://www.tandfonline.com/doi/full/10.1080/23743603.2016.1273647

	require(car)

	# IMPORTANT: change this to match your path/file
	fileLocation <- 'C:\\Users\\Daniel\\surfdrive\\Data\\blog volunteer study\\VolunteeringDataFile_05.09.16.csv'

	# Dienes functions
	#Create single plot for graph----
	power <- seq(0.01,1,0.01)
	alpha <- seq(0.01,1,0.01)
	pH0 <- 0.5
	tp<-((1-pH0)*power)
	fp<-((pH0)*alpha)
	likelihood_ratio<-outer(tp, fp, "/")

	#Create color shading
	nrz<-nrow (likelihood_ratio)
	if(!require(ggplot2)){install.packages('ggplot2')}
	library(ggplot2)
	if(!require(Rcpp)){install.packages('Rcpp')}
	library(Rcpp)
	if(!require(MASS)){install.packages('MASS')}
	library(MASS)
	options(digits=10,scipen=999)


	#Set color palette
	if(!require(BayesFactor)){install.packages('BayesFactor')}
	library(BayesFactor)
	#to get emoticons for each test, set wait to 0.5 and showfaces to 1.
	#When running large number of simulations, set wait to 0 and showfaces to 0.
	options(scipen=20) #disable scientific notation for numbers
	waitx<-0.5 #To see a small wait between individual trials, set a wait time to e.g., 0.5
	showfaces<-1 #Set to 0 if you do not want the faces, set to 1 if you want to see the faces

	cohensd<-0.3 #set true effect size
	n<-75 #sample size in each group
	if(!require(ggplot2)){install.packages('ggplot2')}
	library(ggplot2)
	if(!require(pwr)){install.packages('pwr')}
	library(pwr)

	nSims <- 100000 #number of simulated experiments

	M<-106 #Mean IQ score in the sample
	n<-26 #set sample size
	SD<-15 #SD of the simulated data