Raw probabilities (#16)

* use raw probabilities

10.visualize-gender.Rmd

@@ -65,21 +65,13 @@ iscb_pubmed <- iscb_gender_df %>%
     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities)) %>%
-  group_by(type, year, gender) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-    # weight = 1
-  )
+  group_by(type, year, gender) 
 
 iscb_pubmed_sum <- iscb_pubmed %>%
   summarise(
     # n = n(),
-    mean_prob = mean(weighted_probs),
-    # mean_prob = mean(probabilities, na.rm = T),
-    # sd_prob = sd(probabilities, na.rm = T),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities, na.rm = T),
+    se_prob = sd(probabilities, na.rm = T),
     # n = mean(n),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
@@ -102,7 +94,7 @@ fig_1 <- iscb_pubmed_sum %>%
   # group_by(year, type, gender) %>%
   gender_breakdown("main", fct_rev(type))
 fig_1
-ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5)
+ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5, dpi = 600)
 ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5)
 ```
 
@@ -131,8 +123,8 @@ fig_1d <- iscb_pubmed %>%
   ) %>%
   group_by(type2, year, gender) %>%
   summarise(
-    mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities),
+    se_prob = sd(probabilities)/sqrt(n()),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
   ) %>%
@@ -173,49 +165,49 @@ iscb_pubmed_sum %>%
 ```{r echo = F}
 get_p <- function(inte, colu) {
   broom::tidy(inte) %>%
-    filter(term == "weighted_probs") %>%
+    filter(term == "probabilities") %>%
     pull(colu) %>%
     sprintf("%0.5g", .)
 }
 ```
 
 ```{r}
 iscb_lm <- iscb_pubmed %>%
-  filter(gender == "probability_female", !is.na(weighted_probs)) %>%
+  filter(gender == "probability_female", !is.na(probabilities)) %>%
   mutate(type = as.factor(type)) %>% 
   mutate(type = type %>% relevel(ref = "Pubmed authors"))
 ```
 
 ```{r}
 scaled_iscb <- iscb_lm %>%
   filter(year(year) >= 2002)
-# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
+# scaled_iscb$s_prob <- scale(scaled_iscb$probabilities, scale = F)
 # scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)
 
-main_lm <- glm(type ~ year + weighted_probs,
+main_lm <- glm(type ~ year + probabilities,
   data = scaled_iscb, # %>% mutate(year = as.factor(year))
   family = "binomial"
 )
 
 broom::tidy(main_lm)
 inte_lm <- glm(
-  # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
+  # type ~ scale(year, scale = F) * scale(probabilities, scale = F),
   # type ~ s_year * s_prob,
-  type ~ year * weighted_probs,
+  type ~ year * probabilities,
   data = scaled_iscb, # %>% mutate(year = as.factor(year))
   family = "binomial"
 )
 broom::tidy(inte_lm)
 anova(main_lm, inte_lm, test = "Chisq")
 # mean(scaled_iscb$year)
-# mean(scaled_iscb$weighted_probs)
+# mean(scaled_iscb$probabilities)
 ```
 
 The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`).
 Interaction terms do not predict `type` over and above the main effect of gender probability and year.
 
 ```{r include=FALSE, eval=FALSE}
-# inte_lm <- glm(type ~ (year * weighted_probs),
+# inte_lm <- glm(type ~ (year * probabilities),
 #    data = iscb_lm,
 #    family = 'binomial')
 ```

11.visualize-name-origins.Rmd

@@ -96,17 +96,12 @@ iscb_pubmed_oth <- iscb_nat_df %>%
     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities)) %>%
-  group_by(type, year, region) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-  )
+  group_by(type, year, region)
 
 iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
   summarise(
-    mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities),
+    se_prob = sd(probabilities)/sqrt(n()),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
   )
@@ -127,7 +122,7 @@ for (conf in my_confs) {
   iscb_nat[[i]] <- iscb_pubmed_oth %>%
     filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>%
     group_by(type, year, region, journal) %>%
-    summarise(mean_prob = mean(weighted_probs), .groups = "drop")
+    summarise(mean_prob = mean(probabilities), .groups = "drop")
 }
 ```
 
@@ -169,7 +164,7 @@ fig_4b <- iscb_pubmed_sum_oth %>%
 
 fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
 fig_4
-ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5)
+ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5, dpi = 600)
 ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)
 ```
 
@@ -185,17 +180,17 @@ iscb_lm <- iscb_pubmed_oth %>%
     type = as.factor(type) %>% relevel(ref = "Pubmed authors")
   )
 main_lm <- function(regioni) {
-  glm(type ~ year + weighted_probs,
+  glm(type ~ year + probabilities,
     data = iscb_lm %>%
       filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
 
 inte_lm <- function(regioni) {
-  glm(type ~ year * weighted_probs,
+  glm(type ~ year * probabilities,
     data = iscb_lm %>%
-      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
@@ -215,7 +210,7 @@ Interaction terms do not predict `type` over and above the main effect of name o
 ```{r echo = F}
 get_p <- function(i, colu) {
   broom::tidy(main_list[[i]]) %>%
-    filter(term == "weighted_probs") %>%
+    filter(term == "probabilities") %>%
     pull(colu)
 }
 
@@ -326,21 +321,16 @@ iscb_pubmed_oth_lag <- iscb_nat_df %>%
     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities), year(year) >= 2002) %>%
-  group_by(type, year, region) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-  )
+  group_by(type, year, region) 
 
 iscb_lm_lag <- iscb_pubmed_oth_lag %>%
   ungroup() %>%
   mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors"))
 
 main_lm <- function(regioni) {
-  glm(type ~ year + weighted_probs,
+  glm(type ~ year + probabilities,
     data = iscb_lm_lag %>%
-      filter(region == regioni, !is.na(weighted_probs)),
+      filter(region == regioni, !is.na(probabilities)),
     family = "binomial"
   )
 }

12.analyze-affiliation.Rmd

@@ -293,7 +293,7 @@ enrichment_plot_right <- plot_obs_exp_right %>%
 enrichment_plot <- cowplot::plot_grid(enrichment_plot_left, enrichment_plot_right,
                                 rel_widths = c(1, 1.3))
 enrichment_plot
-ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5)
+ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5, dpi = 600)
 
 ```
 

14.us-name-origin.Rmd

@@ -70,17 +70,12 @@ iscb_pubmed_oth <- iscb_nat_df %>%
     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities)) %>%
-  group_by(type, year, region) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-  )
+  group_by(type, year, region)
 
 iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
   summarise(
-    mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities),
+    se_prob = sd(probabilities)/sqrt(n()),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
   )
@@ -119,7 +114,7 @@ fig_us_name_originb <- iscb_pubmed_sum_oth %>%
 
 fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
 fig_us_name_origin
-ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5)
+ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5, dpi = 600)
 ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)
 ```
 
@@ -134,17 +129,17 @@ iscb_lm <- iscb_pubmed_oth %>%
     type = relevel(as.factor(type), ref = "Pubmed authors")
   )
 main_lm <- function(regioni) {
-  glm(type ~ year + weighted_probs,
+  glm(type ~ year + probabilities,
     data = iscb_lm %>%
-      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
 
 inte_lm <- function(regioni) {
-  glm(type ~ weighted_probs * year,
+  glm(type ~ probabilities * year,
     data = iscb_lm %>%
-      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
@@ -165,7 +160,7 @@ Interaction terms do not predict `type` over and above the main effect of name o
 ```{r echo = F}
 get_exp <- function(i, colu) {
   broom::tidy(main_list[[i]]) %>%
-    filter(term == "weighted_probs") %>%
+    filter(term == "probabilities") %>%
     pull(colu)
 }
 

_output.yaml

@@ -3,3 +3,4 @@ html_document:
   toc: true
   toc_float: true
   code_download: true
+  dpi: 600