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adding next iteration of model fit to dsl

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This commit is contained in:
Matthew Gaughan 2025-11-20 15:02:19 -08:00
parent 13d2113b73
commit 495be027e7
1 changed files with 27 additions and 6 deletions
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33
dsl/dsl.R
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@ -4,6 +4,10 @@ library(dsl)
dsl_csv <-"111725_DSL_frame.csv" dsl_csv <-"111725_DSL_frame.csv"
dsl_df <- read.csv(dsl_csv, header = TRUE) dsl_df <- read.csv(dsl_csv, header = TRUE)
dsl_df <- dsl_df |>
dplyr::mutate(ttr_days = TTR / 24) |>
dplyr::mutate(task_resolution = dsl_score)
base_model <- dsl( base_model <- dsl(
model = "logit", model = "logit",
formula = dsl_score ~ human_EP_prop_adac, formula = dsl_score ~ human_EP_prop_adac,
@ -27,7 +31,8 @@ summary(case_model)
logit_model <- dsl( logit_model <- dsl(
model = "logit", model = "logit",
formula = dsl_score ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac formula = dsl_score ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac
+ week_index + as.factor(isAuthorWMF) + median_PC4_adac + n_comments_before + as.factor(source), + week_index + as.factor(isAuthorWMF) + median_PC4_adac + median_PC3_adac + n_comments_before + as.factor(source) +
median_gerrit_reviewers,
predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"), predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"),
prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"), prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"),
sample_prob = "sampling_prob", sample_prob = "sampling_prob",
@ -39,15 +44,14 @@ logit_model <- dsl(
summary(logit_model) summary(logit_model)
#anova(dsl_df$olmo_RK_prop, dsl_df$median_gerrit_reviewers) #anova(dsl_df$olmo_RK_prop, dsl_df$median_gerrit_reviewers)
#chisq.test(table(dsl_df$isAuthorWMF, dsl_df$author_closer)) #chisq.test(table(dsl_df$isAuthorWMF, dsl_df$author_closer))
felm_df <- dsl_df |>
dplyr::mutate(ttr_days = TTR / 24)
# https://cscu.cornell.edu/wp-content/uploads/clust.pdf # https://cscu.cornell.edu/wp-content/uploads/clust.pdf
# https://statmodeling.stat.columbia.edu/2020/01/10/linear-or-logistic-regression-with-binary-outcomes/ # https://statmodeling.stat.columbia.edu/2020/01/10/linear-or-logistic-regression-with-binary-outcomes/
# https://osf.io/preprints/psyarxiv/4gmbv_v1 # https://osf.io/preprints/psyarxiv/4gmbv_v1
felm_model <- dsl( felm_model <- dsl(
model = "felm", model = "felm",
formula = dsl_score ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac + formula = dsl_score ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac +
week_index + median_PC4_adac + n_comments_before + + isAuthorWMF + median_gerrit_reviewers, phase
+ median_PC4_adac + median_PC3_adac + n_comments_before + + isAuthorWMF,
predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"), predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"),
prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"), prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"),
sample_prob = "sampling_prob", sample_prob = "sampling_prob",
@ -59,8 +63,25 @@ felm_model <- dsl(
data=felm_df data=felm_df
) )
summary(felm_model) summary(felm_model)
#httpsfelm_model#https://github.com/naoki-egami/dsl/blob/537664a54163dda52ee277071fdfd9e8df2572a6/R/estimate_g.R#L39
dev_model <- dsl(
model = "logit",
formula = task_resolution ~ human_EP_prop_adac + human_TSOL_prop_adac + human_RK_prop_adac
+ median_PC4_adac + median_PC3_adac + n_comments_before
+ median_gerrit_reviewers + median_gerrit_loc_delta
+ week_index + as.factor(source) * as.factor(isAuthorWMF),
predicted_var = c("human_EP_prop_adac", "human_TSOL_prop_adac", "human_RK_prop_adac"),
prediction = c("olmo_EP_prop_adac", "olmo_TSOL_prop_adac", "olmo_RK_prop_adac"),
sample_prob = "sampling_prob",
cluster="source",
cross_fit = 3,
sample_split = 20,
data=dsl_df
)
summary(dev_model)
#https://github.com/naoki-egami/dsl/blob/537664a54163dda52ee277071fdfd9e8df2572a6/R/estimate_g.R#L39
library(broom) library(broom)
library(dplyr) library(dplyr)
@ -79,7 +100,7 @@ tidy.dsl <- function(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALS
out <- broom:::exponentiate(out) out <- broom:::exponentiate(out)
return(out) return(out)
} }
coef_df <- tidy.dsl(felm_model) coef_df <- tidy.dsl(dev_model)
ggplot(coef_df, aes(x = estimate, y = term)) + ggplot(coef_df, aes(x = estimate, y = term)) +
geom_point(size = 1) + geom_point(size = 1) +
geom_errorbar(aes(xmin = estimate - 1.96*std.error, xmax = estimate + 1.96 *std.error), height = 0.2) + geom_errorbar(aes(xmin = estimate - 1.96*std.error, xmax = estimate + 1.96 *std.error), height = 0.2) +