#' Estimate BikeTFL (trip frequency and length) Models for households
#'
library(dplyr)
library(purrr)
library(tidyr)
library(splines)
source("data-raw/EstModels.R")
if (!exists("Hh_df"))
source("data-raw/LoadDataforModelEst.R")
#' converting household data.frame to a list-column data frame segmented by
#' metro ("metro" and "non-metro")
Model_df <- Hh_df %>%
nest(-metro) %>%
rename(train=data) %>%
mutate(test=train) # use the same data for train & test
#' model formula for each segment as a tibble (data.frame), also include a
#' `post_func` column with functions de-transforming predictions to the original
#' scale of the dependent variable
Fmlas_df <- tribble(
~metro, ~Step, ~post_func, ~fmla,
"metro", 1, function(y) y, ~pscl::hurdle(BikeTrips ~ AADVMT + Age0to14 + Age65Plus + D1B + D3bpo4 + Workers + LogIncome |
log1p(AADVMT) + HhSize + LifeCycle + Age0to14 + Age65Plus + D2A_EPHHM + D3bpo4 +
FwyLaneMiPC + TranRevMiPC + LogIncome,
data= ., weights=.$hhwgt, na.action=na.exclude),
"metro", 2, function(y) exp(y), ~lm(log(BikeAvgTripDist) ~ AADVMT + VehPerDriver + Age0to14 +
Age65Plus + LogIncome + LifeCycle + D2A_EPHHM +
D1B + D3bpo4 + TranRevMiPC + TranRevMiPC:D4c,
data= ., weights=.$hhwgt, subset=(BikeAvgTripDist > 0), na.action=na.exclude),
"non_metro",1, function(y) y, ~pscl::hurdle(BikeTrips ~ AADVMT + VehPerDriver + HhSize + LifeCycle + Age0to14 + Age65Plus + D1B +
Workers + LogIncome + D3bpo4 |
AADVMT + VehPerDriver + LifeCycle + Age0to14 + Age65Plus + D2A_EPHHM +
D5 + Workers + LogIncome + D3bpo4,
data= ., weights=.$hhwgt, na.action=na.exclude),
"non_metro",2, function(y) exp(y), ~lm(log(BikeAvgTripDist) ~ AADVMT + Age0to14 +
Age65Plus + LogIncome + LifeCycle + D2A_EPHHM + D1B + D5,
data= ., weights=.$hhwgt, subset=(BikeAvgTripDist > 0), na.action=na.exclude)
)
#' call function to estimate models for each segment and add name for each
#' segment
Model_df <- Model_df %>%
EstModelWith(Fmlas_df) %>%
name_list.cols(name_cols=c("metro", "Step"))
#' print model summary and goodness of fit
Model_df$model %>% map(summary)
Model_df #%>%
#dplyr::select(metro, ends_with("rmse"), ends_with("r2")) %>%
#group_by(metro) %>%
#summarize_all(funs(mean))
#' trim model object of information unnecessary for predictions to save space
BikeTFLModel_df <- Model_df %>%
dplyr::select(metro, Step, model, post_func) %>%
mutate(model=map(model, TrimModel))
#' save Model_df to `data/`
usethis::use_data(BikeTFLModel_df, overwrite = TRUE)
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