135 lines
5.2 KiB
R
135 lines
5.2 KiB
R
library(tidyverse)
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entest_fp <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/en-testing_0217_extensions_ve_weekly_commit_count_data.csv"
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entest_df <- read.csv(entest_fp, header = TRUE) |> mutate(rd_event = "en-testing")
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widetest_fp <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/wide-testing_0217_extensions_ve_weekly_commit_count_data.csv"
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widetest_df <- read.csv(widetest_fp, header = TRUE) |> mutate(rd_event = "wide-testing")
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event_fp <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/event_0217_extensions_ve_weekly_commit_count_data.csv"
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event_df <- read.csv(event_fp, header = TRUE) |> mutate(rd_event = "default")
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#input_df <- bind_rows(entest_df, widetest_df, event_df)
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#dropping the event (2013-07-01) from the modeling
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input_df <- bind_rows(entest_df, widetest_df)
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input_df <- input_df |>
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mutate(nonbot_commit_count = commit_count - bot_commit_count)|>
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mutate(other_commit_count = nonbot_commit_count - mediawiki_dev_commit_count - wikia_commit_count - wikimedia_commit_count) |>
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mutate(wikimedia_commit_count = wikimedia_commit_count + mediawiki_dev_commit_count + wikia_commit_count) |>
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dplyr::select(-mediawiki_dev_commit_count) |>
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dplyr::select(-wikia_commit_count)
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library(MASS)
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library(lme4)
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library(dplyr)
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#get into mlm format
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long_df <- input_df |>
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pivot_longer(cols = c(other_commit_count, wikimedia_commit_count),
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names_to = "commit_type",
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values_to = "lengthened_commit_count")
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intermediate_long_df <- long_df |>
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mutate(commit_share = lengthened_commit_count / (nonbot_commit_count)) |>
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mutate(log_commits = log1p(lengthened_commit_count))|>
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mutate(scaled_long_commits = lengthened_commit_count / 10)
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library(rdd)
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intermediate_long_df <- intermediate_long_df |>
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drop_na()
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var(intermediate_long_df$lengthened_commit_count) # 1253.343
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mean(intermediate_long_df$lengthened_commit_count) # 44.92381
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median(intermediate_long_df$lengthened_commit_count) # 39.5
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get_optimal_bandwidth <- function(df){
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bw <- tryCatch({
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IKbandwidth(df$relative_week, df$lengthened_commit_count, cutpoint = 0, verbose = FALSE, kernel = "triangular")
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}, error = function(e) {
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NA
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})
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}
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optimal_bandwidth <- get_optimal_bandwidth(intermediate_long_df)
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window_num <- 4
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final_long_df <- intermediate_long_df |>
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filter(relative_week >= (- window_num) & relative_week <= (window_num))
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library(fitdistrplus)
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descdist(final_long_df$lengthened_commit_count, discrete=FALSE)
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#start_values <- list(shape1 = 1, shape2 = 1)
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#fit <- MASS::fitdistr(as.numeric(long_df$lengthened_commit_count), "negative binomial")
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print(fit)
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#NOTE should not run if you've already dropped NA
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mlm <- glmer.nb(lengthened_commit_count ~ before_after*relative_week +
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(before_after*relative_week|commit_type) +
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(before_after*relative_week|rd_event),
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control=glmerControl(optimizer="bobyqa",
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optCtrl=list(maxfun=2e5)), nAGQ=0,
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data=final_long_df)
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#mlm <- lmer(lengthened_commit_count ~ before_after*relative_week+
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# (before_after*relative_week|commit_type) +
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# (before_after*relative_week|rd_event) ,data=long_df)
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summary(mlm)
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qqnorm(residuals(mlm))
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res <- ranef(mlm)
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print(res)
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#final_long_df <- final_long_df |>
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# drop_na()
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library(performance)
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#descdist(long_df$commit_share, discrete=FALSE)
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#fit <- MASS::fitdistr(as.numeric(long_df$commit_share), "normal")
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#print(fit)
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wikimedia_long_df <- final_long_df |>
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filter(commit_type == "wikimedia_commit_count")
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wikimedia_share_lmer <- lmer(commit_share ~ before_after*relative_week +
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(1| rd_event),
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data=wikimedia_long_df)
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summary(wikimedia_share_lmer)
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icc(wikimedia_share_lmer)
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other_long_df <- final_long_df |>
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filter(commit_type == "other_commit_count")
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other_share_lmer <- lmer(commit_share ~ before_after*relative_week +
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(1| rd_event),
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data=other_long_df)
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summary(other_share_lmer)
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icc(other_share_lmer)
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#power analysis
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#library(simr)
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#simrOptions(progress=FALSE)
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## Intercept and slopes for intervention, time1, time2, intervention:time1, intervention:time2
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#wmf_fixed <- c(0.511, -0.166, 0.002, 0.007)
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## Random intercepts for participants clustered by class
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#wmf_rand <- matrix(c(
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# 0.01, 0.005, 0.002, 0.001,
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# 0.005, 0.02, 0.003, 0.004,
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# 0.002, 0.003, 0.015, 0.006,
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# 0.001, 0.004, 0.006, 0.01
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#), nrow=4, byrow=TRUE)
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## residual variance
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#wmf_res <- 0.2065
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#wmf_model <- makeLmer(commit_share ~ before_after*relative_week + (before_after*relative_week | rd_event),
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# fixef=wmf_fixed, VarCorr=wmf_rand, sigma=wmf_res, data=wikimedia_long_df)
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#sim_treat <- powerSim(wmf_model, nsim=100, test = fcompare(commit_share~relative_week))
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#sim_treat
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#model_ext_subj <- extend(wmf_model, within="rd_event+before_after+relative_week", n=30)
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#sim_treat_subj <- powerSim(model_ext_subj, nsim=100, test = fcompare(commit_share~before_after*relative_week))
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#sim_treat_subj
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#p_curve_treat <- powerCurve(model_ext_subj, test=fcompare(commit_share~before_after*relative_week),
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# within="rd_event+before_after+relative_week",
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# breaks=c(5,10,15,20))
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#plot(p_curve_treat)
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