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further specification of model and formula

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mjgaughan 2024-04-23 13:59:06 -05:00
parent 634f60b6a8
commit 67946e6e10
1 changed files with 33 additions and 13 deletions
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46
R/readmeRDDAnalysis.R
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@ -34,10 +34,11 @@ for (i in 2:nrow(readme_df)){
#filter out the windows of time that we're looking at
window_num <- 8
windowed_data <- expanded_data |>
filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
mutate(D = ifelse(week > 26, 1, 0))
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#separate out the cleaning d
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
@ -54,18 +55,37 @@ qqplot(all_actions_data$count, y)
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
library(lme4)
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
# (D |upstream_vcs_link) or (week | upstream_vcs_link)
poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week || upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
summary(poisson_all_model)
poisson_residuals <- residuals(poisson_all_model)
qqnorm(poisson_residuals)
#making some random data
sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
expanded_sample_data <- expand_timeseries(sampled_data[1,])
for (i in 2:nrow(sampled_data)){
expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
}
windowed_sample_data <- expanded_sample_data |>
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
windowed_sample_data$week_offset <- windowed_sample_data$week - 27
all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
#test model
test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
summary(test_model)
#plot results
p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
geom_point(size=3, show.legend = FALSE) +
geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
theme_bw()
p
##end of the model testing and plotting section
all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
summary(all_model)
all_residuals <- residuals(all_model)
qqnorm(all_residuals)
# for visualization, may have to run model for each project and then identify top 5 projects for RDD graphs
#
#
poisson_mrg_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week |upstream_vcs_link), data=mrg_actions_data, family = poisson(link = "log"))
summary(poisson_mrg_model)
poisson_mrg_residuals <- residuals(poisson_mrg_model)
qqnorm(poisson_mrg_residuals)
mrg_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=mrg_actions_data, REML=FALSE)
summary(mrg_model)
mrg_residuals <- residuals(mrg_model)
qqnorm(mrg_residuals)
# Performance:
library(merTools)