Analyses sensibilité

[RCura]

Indicateurs pour l'instant :

Sur données standardisées :

standardised_data <- filtered_data %>%
   group_by(Indicateur) %>%
   mutate(StdResult = scale(Resultat,  center = TRUE, scale = TRUE)) %>%
   ungroup()

• med_sd_intra & Q1_sd_intra :

standardised_data %>%
   group_by(param, valeur, Indicateur) %>%
   summarise(sd_intra =  sd(StdResult, na.rm = TRUE)) %>%
   group_by(param) %>%
   summarise(med_sd_intra = mean(sd_intra, na.rm = TRUE),
             Q1_sd_intra = quantile(sd_intra, na.rm = TRUE, probs = .25))

• avg_sd_avg & Q3_sd_avg :

standardised_data %>%
   group_by(param, valeur, Indicateur) %>%
   summarise(avg = mean(StdResult, na.rm = TRUE)) %>%
   group_by(param, Indicateur) %>%
   summarise(sd_avg = sd(avg, na.rm = TRUE)) %>%
   group_by(param) %>%
   summarise(avg_sd_avg = mean(sd_avg, na.rm = TRUE),
             Q3_sd_avg =  quantile(sd_avg, na.rm = TRUE, probs = .75))

• avg_ecart & Q1_ecart :

filtered_data %>%
   spread(key = Indicateur, value = Resultat) %>%
   select(-param, -valeur, -seed) %>%
   scale(scale = TRUE, center = Objectifs %>% arrange(RealVar) %>% pull(Objectif)) %>%
   as.data.frame() %>%
   bind_cols(filtered_data %>%
               spread(key = Indicateur, value = Resultat) %>%
               select(param, valeur, seed)) %>%
   select(param, valeur, seed, everything()) %>%
   gather(key = Indicateur, value = StdResultat, -param, -valeur, -seed) %>%
   group_by(param) %>%
   summarise(avg_ecart =  mean(abs(StdResultat), na.rm = TRUE),
             Q1_ecart =  quantile(abs(StdResultat), na.rm = TRUE, probs =  .25))

• percent_failing_seeds :

filtered_data %>%
   group_by(param, valeur, Indicateur) %>%
   summarise(nb_failing_seeds = sum(is.na(Resultat), na.rm = TRUE)) %>%
   ungroup() %>%
   group_by(param) %>%
   summarise(percent_failing_seeds = mean(nb_failing_seeds) * 100 / n())

• nb_failing_values

filtered_data %>%
   filter(Indicateur == "Agrégats") %>%
   group_by(param, valeur) %>%
   summarise(nb_failing_seeds = sum(is.na(Resultat), na.rm = TRUE)) %>%
   mutate(is_failing = ifelse(nb_failing_seeds > 0, TRUE, FALSE)) %>%
   ungroup() %>%
   group_by(param) %>%
   summarise(nb_failing_values = sum(is_failing))