56 lines
1.7 KiB
R
56 lines
1.7 KiB
R
##############################################################################
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#
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# Purpose:
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# Use pilot project data to calculate power of a full study through simulation
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#
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# Parts:
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# (0) - Setup
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# (1) - Get the pilot data and clean it
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# (2) - Run the model on the pilot data and extract effects
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# (3) - Set up and run the simulation
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# ====> Set variables at the arrows <====
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#
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##############################################################################
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rm(list=ls())
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set.seed(424242)
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library(readr)
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# (1) - Get the pilot data and clean it
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#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
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#source ('/data/users/mgaughan/kkex_data_110823_3')
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data1 <- read_csv('../power_data_110923_mmt.csv',show_col_types = FALSE)
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#d$nd <- to_logical(d$not.damaging, custom_true=c("Y"))
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#levels(d$source) <- c("IP-based Editors", "New Editors", "Registered Editors", "Tor-based Editors")
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# (2) - Run the model on the pilot data
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pilotM <- glm(up_fac_mean ~ ((mmt)/ (milestones/age)), # give the anticipated regression a try
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family=gaussian(link='identity'), data=data1)
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summary(pilotM) #we expect effect sizes on this order
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pilot.b0 <- coef(summary(pilotM))[1,1]
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pilot.b1 <- coef(summary(pilotM))[2,1]
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pilot.b2 <- coef(summary(pilotM))[3,1]
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pilot.b3 <- coef(summary(pilotM))[4,1]
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# (3) - Set up and run the simulation
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source('powerAnalysis.R') #my little "lib"
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#====>
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nSims <- 5000 #how many simulations to run
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n <- 100 #a guess for necessary sample size (per group)
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#makeData(10) #DEBUGGING CODE -- you can uncomment this if you want to see it work
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#<====
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print("Levels are:")
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print(levels(d$source))
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powerCheck(n, nSims)
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#Sample values
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powerCheck(50, 100)
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powerCheck(80, 1000)
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powerCheck(200, 5000)
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