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inching towards implementing powerAnalysis

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This commit is contained in:
Matthew Gaughan 2023-11-09 22:19:59 -06:00
parent a2fe06a31c
commit 863a35ffb8
5 changed files with 253 additions and 16 deletions
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21
R/EDA.R
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@ -1,6 +1,7 @@
library(dplyr) library(dplyr)
df<-read.csv('~/Research/kkex_repo/power_data_110923.csv') df<-read.csv('~/Research/kkex_repo/power_data_110923.csv')
df1 <- read.csv('/data_ext/users/kcz1100/kaylea_dissertation/collabnetXS/raw_data/inst_all_packages_full_results.csv')
hist(df$age/365) #there's a big bump at 9 years, why? hist(df$age/365) #there's a big bump at 9 years, why?
hist(df$contributors) #skewed hist(df$contributors) #skewed
@ -18,3 +19,23 @@ table(df$uses_milestones)
#playing around #playing around
cor.test(df$contributors, as.numeric(df$uses_milestones)) cor.test(df$contributors, as.numeric(df$uses_milestones))
cor.test(df$collaborators, as.numeric(df$uses_milestones)) cor.test(df$collaborators, as.numeric(df$uses_milestones))
cor.test(df$contributors / df$collaborators, df$age / 365)
t.test(df$age)
#95 percent confidence interval:
# 2793.638 3066.417
t.test(df$contributors)
#95 percent confidence interval:
# 27.6519 154.6866
t.test(df$collaborators)
#95 percent confidence interval:
# 50.01884 96.77090
t.test(df1$up.fac.mean)
#95 percent confidence interval:
# -0.1961401 -0.1647757
df$mmt <- (df$contributors + (2 * df$collaborators)) / (df$contributors + df$collaborators)
t.test(df$mmt)
# 95 percent confidence interval:
# 1.610638 1.684438
#

6
R/calculatePower.R
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@ -19,12 +19,14 @@ library(readr)
# (1) - Get the pilot data and clean it # (1) - Get the pilot data and clean it
#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R') #source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
#source ('/data/users/mgaughan/kkex_data_110823_3') #source ('/data/users/mgaughan/kkex_data_110823_3')
data1 <- read_csv('/data/users/mgaughan/power_data_110923.csv',show_col_types = FALSE) data1 <- read_csv('../power_data_110923_mmt.csv',show_col_types = FALSE)
#d$nd <- to_logical(d$not.damaging, custom_true=c("Y")) #d$nd <- to_logical(d$not.damaging, custom_true=c("Y"))
#levels(d$source) <- c("IP-based Editors", "New Editors", "Registered Editors", "Tor-based Editors") #levels(d$source) <- c("IP-based Editors", "New Editors", "Registered Editors", "Tor-based Editors")
# (2) - Run the model on the pilot data # (2) - Run the model on the pilot data
pilotM <- glm(nd ~ source, family=binomial(link="logit"), data=data1) pilotM <- glm(up_fac_mean ~ ((mmt)/ (milestones/age)), # give the anticipated regression a try
family=gaussian(link='identity'), data=data1)
summary(pilotM) #we expect effect sizes on this order summary(pilotM) #we expect effect sizes on this order
pilot.b0 <- coef(summary(pilotM))[1,1] pilot.b0 <- coef(summary(pilotM))[1,1]

16
R/justPowerSims.R Normal file
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@ -0,0 +1,16 @@
source('powerAnalysis.R') #my little "lib"
#====>
nSims <- 5000 #how many simulations to run
n <- 100 #a guess for necessary sample size (per group)
#makeData(10) #DEBUGGING CODE -- you can uncomment this if you want to see it work
#<====
print("Levels are:")
#print(levels(d$source))
powerCheck(n, nSims)
#Sample values
powerCheck(50, 100)
powerCheck(80, 1000)
powerCheck(200, 5000)

28
R/powerAnalysis.R
View File

@ -14,16 +14,17 @@ l2p <- function(b) {
} }
makeData <- function(n) { #make a random dataset of size n #Matt:
#4 group IDs makeDataNew <- function(n) {
tDF <- data.frame( sDF <- data.frame(
Group0=rbinom(n=n, size=1, prob=l2p(pilot.b0)), #ASK: what about se in pilot data? sample(2793.638:3066.417, 1, replace = FALSE),
Group1=rbinom(n=n, size=1, prob=l2p(pilot.b0 + pilot.b1)), # shouldn't my probs sample(27.6519:154.6866, 1, replace = FALSE),
Group2=rbinom(n=n, size=1, prob=l2p(pilot.b0 + pilot.b2)), # include se? sample(50.01884:96.77090, 1, replace = FALSE),
Group3=rbinom(n=n, size=1, prob=l2p(pilot.b0 + pilot.b3))) sample(c(1,2), 1, prob=(c(0.5,0.5)), replace = FALSE),
sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes sample(1.610638: 1.684438, 1, replace = FALSE),
colnames(sDF) <- c('source', 'nd') sample(-0.1961401:-0.1647757, 1, replace = FALSE)
)
colnames(sDF) <- c('age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up_fac_mean')
return(sDF) return(sDF)
} }
@ -35,9 +36,10 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
signif3 <- rep(NA, nSims) signif3 <- rep(NA, nSims)
signifM <- rep(NA, nSims) signifM <- rep(NA, nSims)
for (s in 1:nSims) { # repeatedly we will.... for (s in 1:nSims) { # repeatedly we will....
simData <- makeData(n) # make some data simData <- makeDataNew(n) # make some data
m1.sim <- glm(nd ~ source, # give the anticipated regression a try #have updated for kkex through here, now need to look at the underproduction work
family=binomial(link="logit"), data=simData) m1.sim <- glm(up_fac_mean ~ ((mmt)/ (milestones/age)), # give the anticipated regression a try
family=gaussian(link='identity'), data=simData)
p0 <- coef(summary(m1.sim))[1,4] p0 <- coef(summary(m1.sim))[1,4]
p1 <- coef(summary(m1.sim))[2,4] p1 <- coef(summary(m1.sim))[2,4]
p2 <- coef(summary(m1.sim))[3,4] p2 <- coef(summary(m1.sim))[3,4]

196
power_data_110923_mmt.csv Normal file
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@ -0,0 +1,196 @@
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