fix: mean response and performance values standardized across functions

[laresbernardo]

As described in #1279

[laresbernardo]

Validated matching values across different approaches and sources. Any model should work to replicate.

library(dplyr)

start_date <- min(InputCollect$dt_mod$ds)
end_date <- max(InputCollect$dt_mod$ds)

# Gather data needed
dt_mod <- select(InputCollect$dt_mod, c("ds", InputCollect$all_media)) %>%
  filter(ds >= start_date, ds <= end_date)
periods <- nrow(dt_mod)
df <- filter(OutputCollect$mediaVecCollect, solID %in% select_model, .data$type == "decompMedia") %>%
  filter(ds >= start_date, ds <= end_date)

# Mean response for total modeling window
(mean_resp <-  select(df, InputCollect$paid_media_spends) %>%
  summarise_all(function(x) sum(x, na.rm = TRUE)/periods))
# Mean spend for total modeling window
(mean_spends <- select(dt_mod, InputCollect$paid_media_spends) %>%
  summarise_all(function(x) sum(x, na.rm = TRUE)/periods))
# Mean performance
(perf <- mean_resp/mean_spends)

# Check performance values from one-pager (match!)
op <- robyn_onepagers(InputCollect, OutputCollect, select_model, export = FALSE)
op[[1]][[2]][[1]]$layers[[3]]$data %>% select(rn, value)

# Check values from lares::robyn_performance() (match!)
lares::robyn_performance(InputCollect, OutputCollect, solID = select_model) %>%
  filter(type == "Paid") %>%
  select(channel, performance, spend, response) %>%
  mutate(response = response / periods,
         spend = spend / periods)

# Check match with robyn_write() -> fixed in fix_ba_perf branch
(exp <- robyn_write(
  InputCollect, OutputCollect,
  select_model = select_model, export = FALSE)$ExportedModel$summary %>%
    select(variable, mean_response, mean_spend, performance) %>%
    filter(!is.na(performance)))

# Different source, same results (xDecompAgg & mediaVecCollect:decompMedia - match)
(df2 <- filter(OutputCollect$xDecompAgg, .data$solID == select_model) %>%
    filter(.data$rn %in% InputCollect$paid_media_spends) %>%
    group_by(.data$solID, .data$rn) %>%
    summarise(spend = sum(.data$total_spend),
              response = sum(.data$xDecompAgg),
              mean_spend = .data$spend / periods,
              mean_resp = .data$response / periods,
              performance = sum(.data$xDecompAgg) / sum(.data$total_spend),
              .groups = "drop"))

### NOW, BUDGET ALLOCATOR

# Spend matches, response vales were very far off, and in consequence, performance off - fixed!
select(AllocatorCollect1$dt_optimOut, initResponseUnit, initSpendUnit, initRoiUnit)

[gufengzhou]

LGTM