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fit models to subset data

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mjgaughan 2024-11-02 19:05:39 -05:00
parent 918066e98b
commit 085b520267
6 changed files with 34 additions and 14 deletions
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13
110124_supp_analysis/110124_supp_analysis.Rproj Normal file
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Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: pdfLaTeX

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110124_supp_analysis/110224_contrib_length_subset.rda Normal file
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110124_supp_analysis/110224_log1p_readme_strict_subset.rda Normal file
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110124_supp_analysis/110224_readme_strict_subset.rda Normal file
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15
110124_supp_analysis/contrib_supp_analysis.r
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@ -1,7 +1,7 @@
#library(tidyverse)
#library(plyr)
library(tidyverse)
library(plyr)
contrib_df <- read_csv("110124_contrib_strict_subset.csv")
contrib_df <- read_csv("110124_contrib_length_subset.csv")
#some preprocessing and expansion
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
contrib_df <- contrib_df[,col_order]
@ -43,9 +43,10 @@ mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg")
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
#all_gmodel <- glmer.nb(log1p_count ~ D * week_offset + scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link),
# control=glmerControl(optimizer="bobyqa",
# optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
#saveRDS(all_gmodel, "0711_contrib_all_01.rda")
all_gmodel <- glmer.nb(count ~ D * week_offset + (D * week_offset | upstream_vcs_link),
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
summary(all_gmodel)
saveRDS(all_gmodel, "110224_contrib_length_subset.rda")
#all_residuals <- residuals(all_gmodel)

20
110124_supp_analysis/readme_supp_analysis.R
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@ -1,10 +1,6 @@
library(readr)
library(tidyverse)
library(plyr)
library(lme4)
library(stringr)
library(tidyr)
readme_df <- read_csv("110124_supp_analysis/110124_readme_strict_subset.csv")
readme_df <- read_csv("110124_readme_strict_subset.csv")
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
readme_df <- readme_df[,col_order]
@ -57,4 +53,14 @@ median(all_actions_data$log1p_count)
var(all_actions_data$count)
mean (all_actions_data$count)
sd (all_actions_data$count)
median(all_actions_data$count)
median(all_actions_data$count)
library(lme4)
all_log1p_gmodel <- glmer.nb(log1p_count ~ D * week_offset + (D * week_offset | upstream_vcs_link), data=all_actions_data, nAGQ=1, control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e5)))
summary(all_log1p_gmodel)
saveRDS(all_log1p_gmodel, "110224_log1p_readme_subset.rda")
all_gmodel <- glmer.nb(count ~ D * week_offset + (D * week_offset | upstream_vcs_link), data=all_actions_data, nAGQ=1, control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e5)))
summary(all_gmodel)
saveRDS(all_gmodel, "110224_readme_subset.rda")