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mnl_lc.R
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#selecionando diretorio
setwd("C:\\Users\\calde\\Google Drive\\Mestrado 2019_Gabriel Caldeira\\Artigos\\1_RRM Latent Class\\modelo")
library(apollo)
library(tidyverse)
rm(list = ls())
parallel::detectCores() #cores available
#ler o banco de dados quando tiver o final
database <- read.csv(file ='database_final_JTBS.csv')
###Preparing environment
apollo_initialise()
###Options controlling the running of the code
apollo_control = list(
modelName ="mnl_lc 13",
modelDescr = "modelo 4 com variaveis socio no modelo de escolha e de aloca??o usando fun??o de parametros inicias",
indivID ="id_pess",
mixing = F,
nCores = 10
)
#parametros a serem estimados
apollo_beta <- c(asc1 = 0,
asc2 = 0,
asc3 = 0,
#class 1
b_ttauto_1 = -0.07,
b_ttbus_1 = -0.01,
b_ttmetro_1 = -0.03,
b_co_1 = -0.6,
#class 2
b_ttauto_2 = -0.06,
b_ttbus_2 = -0.01,
b_ttmetro_2 = -0.03,
b_co_2 = -0.5,
#class membership
s_1 = 0,
s_2 = 0,
b_pico_al = 0,
b_idade_al = 0,
b_grau1_al = 0,
b_grau2_al = 0,
b_sex_al = 0,
b_inc_al = 0,
b_nomorad_al = 0,
b_totviag_al = 0,
#sociodemographics
b_pico = 0,
b_idade= 0,
b_grau1 = 0,
b_grau2 = 0,
b_sex_bus = 0,
b_sex_metro = 0,
b_inc_bus = 0,
b_inc_metro = 0,
b_nomorad_bus = 0,
b_nomorad_metro = 0,
b_totviag_bus = 0,
b_totviag_metro = 0
)
#parametros fixos
apollo_fixed <- c('asc1','s_1',
'b_pico_al',
'b_idade_al',
'b_grau1_al',
'b_grau2_al',
'b_sex_al',
'b_inc_al',
'b_nomorad_al',
'b_totviag_al',
'b_pico',
'b_idade',
'b_grau1',
'b_grau2',
'b_sex_bus',
'b_sex_metro',
'b_inc_bus',
'b_inc_metro',
'b_nomorad_bus',
'b_nomorad_metro',
'b_totviag_bus',
'b_totviag_metro'
)
#lendo parametros de outro modelo
setwd('C:\\Users\\calde\\Google Drive\\Mestrado 2019_Gabriel Caldeira\\JTBS\\modelo\\resultados\\mnl_lc')
apollo_beta <- apollo_readBeta(apollo_beta,apollo_fixed,'mnl_lc 12',overwriteFixed = FALSE)
###Grouping latent class parameters
apollo_lcPars = function(apollo_beta, apollo_inputs){
lcpars = list()
lcpars[['b_ttauto']] = list(b_ttauto_1,b_ttauto_2)
lcpars[['b_ttbus']] = list(b_ttbus_1,b_ttbus_2)
lcpars[['b_ttmetro']] = list(b_ttmetro_1,b_ttmetro_2)
lcpars[['b_co']] = list(b_co_1,b_co_2)
###Class membership probabilities based on s_1, s_2: use of MNL fomrula
V=list()
V[["class_1"]] = s_1
V[["class_2"]] = s_2 + b_idade_al*idade + b_pico_al*pico_manha +
b_sex_al*(sexo == 2) + b_inc_al*lnrenda + b_nomorad_al*no_morad + b_totviag_al*tot_viag + b_grau1_al*grau1 + b_grau2_al*grau2
###Settings for class membership probabilities
mnl_settings = list(
alternatives = c(class_1=1, class_2=2),
avail = 1,
choiceVar = NA, ###No choice variable as only the formula of MNL is used
V = V
)
###Class membership probabilities
lcpars[["pi_values"]] = apollo_mnl(mnl_settings, functionality="raw")
return(lcpars)
}
###busca pelos dados de entrada necessarios
apollo_inputs = apollo_validateInputs()
#cosntruindo a funcao de verossimilhanca
apollo_probabilities <- function(apollo_beta,apollo_inputs,functionality = 'estimate'){
###Attaches parameters and data so that variables can be referred by name
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
###Create list for probabilities
P = list()
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(auto=3, bus=2, metro=1),
avail = list(auto=1, bus=1, metro=1),
choiceVar = choice
)
### Compute class-specific utilities
V=list()
V[['auto']] = asc1 + b_ttauto[[1]]*tt_auto + b_co[[1]]*co_auto + b_pico*pico_manha
V[['bus']] = asc2 + b_ttbus[[1]]*tt_bus_otp + b_co[[1]]*co_bus + b_idade*idade +
b_sex_bus*(sexo == 2) + b_inc_bus*lnrenda + b_nomorad_bus*no_morad + b_totviag_bus*tot_viag + b_grau1*grau1 + b_grau2*grau2
V[['metro']] = asc3 + b_ttmetro[[1]]*tt_metro + b_co[[1]]*co_metro + b_idade*idade +
b_sex_metro*(sexo == 2) + b_inc_metro*lnrenda + b_nomorad_metro*no_morad + b_totviag_metro*tot_viag + b_grau1*grau1 + b_grau2*grau2
###Calculating probabilities based on MNL function for class 1
mnl_settings$V = V
P[[1]] = apollo_mnl(mnl_settings, functionality)
### Compute class-specific regrets
R=list()
R[['auto']] = asc1 -
log(1 + exp(b_ttbus[[2]]*tt_bus_otp - b_ttauto[[2]]*tt_auto)) -
log(1 + exp(b_ttmetro[[2]]*tt_metro - b_ttauto[[2]]*tt_auto)) -
log(1 + exp(b_co[[2]]*co_metro - b_co[[2]]*co_auto)) -
log(1 + exp(b_co[[2]]*co_bus - b_co[[2]]*co_auto)) +
b_pico*pico_manha
R[['bus']] = asc2 -
log(1 + exp((b_ttauto[[2]]*tt_auto - b_ttbus[[2]]*tt_bus_otp))) -
log(1 + exp((b_ttmetro[[2]]*tt_metro - b_ttbus[[2]]*tt_bus_otp))) -
log(1 + exp(b_co[[2]]*co_auto - b_co[[2]]*co_bus)) -
log(1 + exp(b_co[[2]]*(co_metro - co_bus))) +
b_idade*idade + b_sex_bus*(sexo == 2) + b_inc_bus*lnrenda + b_nomorad_bus*no_morad +
b_totviag_bus*tot_viag + b_grau1*grau1 + b_grau2*grau2
R[['metro']] = asc3 -
log(1 + exp(b_ttauto[[2]]*tt_auto - b_ttmetro[[2]]*tt_metro)) -
log(1 + exp(b_ttbus[[2]]*tt_bus_otp - b_ttmetro[[2]]*tt_metro)) -
log(1 + exp(b_co[[2]]*co_auto - b_co[[2]]*co_metro)) -
log(1 + exp(b_co[[2]]*(co_bus - co_metro))) +
b_idade*idade + b_sex_metro*(sexo == 2) + b_inc_metro*lnrenda + b_nomorad_metro*no_morad +
b_totviag_metro*tot_viag + b_grau1*grau1 + b_grau2*grau2
###Calculating probabilities based on MNL function for class 2
mnl_settings$V = R
P[[2]] = apollo_mnl(mnl_settings, functionality)
###Calculating choice probabilities using class membership and conditional probabilities
lc_settings = list(inClassProb = P, classProb=pi_values)
P[["model"]] = apollo_lc(lc_settings, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
### Optional: searching for starting value
apollo_beta = apollo_searchStart(apollo_beta,
apollo_fixed,
apollo_probabilities,
apollo_inputs,
searchStart_settings=list(nCandidates=20))
# ################################################################# #
#### Estimacao do modelo ####
# ################################################################# #
model_lc <- apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs,
estimate_settings = list(maxIterations=250) )
###Display the output to console with p-values
apollo_modelOutput(model_lc,modelOutput_settings = list(printPVal=T ,printT1=T,
printDiagnostics = T))
# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name) ----
# ----------------------------------------------------------------- #
setwd('C:\\Users\\calde\\Google Drive\\Mestrado 2019_Gabriel Caldeira\\JTBS\\modelo\\resultados\\mnl_lc')
apollo_saveOutput(model_lc, saveOutput_settings = list(printPVal=T, printT1=T,
printDiagnostics = T)) #ver saveoutput list para mais configuracoes
#VTTS RUM
setwd('C:\\Users\\calde\\Google Drive\\Mestrado 2019_Gabriel Caldeira\\Artigos\\JTBS\\modelo\\resultados\\mnl_lc')
model_lc <- apollo_loadModel('mnl_lc 12')
vtts_auto <- model_lc$estimate[['b_ttauto_1']]/model_lc$estimate[['b_co_1']]*60
vtts_bus <- model_lc$estimate[['b_ttbus_1']]/model_lc$estimate[['b_co_1']]*60
vtts_metro <- model_lc$estimate[['b_ttmetro_1']]/model_lc$estimate[['b_co_1']]*60
vtts_auto
vtts_bus
vtts_metro
#VTTS RRM
dtt_auto <- -model_lc$estimate[['b_ttauto_2']]/(exp(-(model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1) +
(-model_lc$estimate[['b_ttauto_2']]/(exp(-(model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1))
dco_auto <- -model_lc$estimate[['b_co_2']]/(exp(-model_lc$estimate[['b_co_2']]*(database$co_bus - database$co_auto))+1) +
(-model_lc$estimate[['b_co_2']]/(exp(-model_lc$estimate[['b_co_2']]*(database$co_metro - database$co_auto))+1))
vtts_auto <- dtt_auto/dco_auto*60
dtt_bus <- -model_lc$estimate[['b_ttbus_2']]/(exp(-(model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1) +
(-model_lc$estimate[['b_ttbus_2']]/(exp(-(model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1))
dco_bus <- -model_lc$estimate[['b_co_2']]/(exp(-model_lc$estimate[['b_co_2']]*(database$co_auto - database$co_bus))+1) +
(-model_lc$estimate[['b_co_2']]/(exp(-model_lc$estimate[['b_co_2']]*(database$co_metro - database$co_bus))+1))
vtts_bus <- dtt_bus/dco_bus*60
dtt_metro <- -model_lc$estimate[['b_ttmetro_2']]/(exp(-(model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1) +
(-model_lc$estimate[['b_ttmetro_2']]/(exp(-(model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1))
dco_metro <- -model_lc$estimate[['b_co_2']]/(exp(-model_lc$estimate[['b_co_2']]*(database$co_auto - database$co_metro))+1) +
(-model_lc$estimate[['b_co_2']]/(exp(-model_lc$estimate[['b_co_2']]*(database$co_bus - database$co_metro))+1))
vtts_metro <- dtt_metro/dco_metro*60
mean(vtts_auto)
mean(vtts_bus)
mean(vtts_metro)
vtts_lccrrm <- data.frame(vtts_auto,vtts_bus,vtts_metro)
vtts_lccrrm <- vtts_lccrrm %>%
rename('vtts_auto_lccrrm' = 'vtts_auto',
'vtts_bus_lccrrm' = 'vtts_bus',
'vtts_metro_lccrrm' = 'vtts_metro')
vtts_models <- cbind(vtts_models,vtts_lccrrm)
vtts_lccrrm_socio <- data.frame(vtts_auto,vtts_bus,vtts_metro)
vtts_lccrrm_socio <- vtts_lccrrm_socio %>%
rename('vtts_auto_lccrrm_socio' = 'vtts_auto',
'vtts_bus_lccrrm_socio' = 'vtts_bus',
'vtts_metro_lccrrm_socio' = 'vtts_metro')
vtts_models <- cbind(vtts_models,vtts_lccrrm_socio)
##### Elasticities #######
### Use the estimated model to make predictions
predictions_base_rum = apollo_prediction(model_lc, apollo_probabilities, apollo_inputs,prediction_settings = list(modelComponent = 1))
predictions_base_rrm = apollo_prediction(model_lc, apollo_probabilities, apollo_inputs,prediction_settings = list(modelComponent = 2))
### RUM
#tt auto
elast_ttauto <- (1-predictions_base_rum[,'auto'])*model_lc$estimate[['b_ttauto_1']]*database$tt_auto
aggfv_ttauto <- sum(elast_ttauto*(database$fe_via*predictions_base_rum[,'auto']/sum(database$fe_via*predictions_base_rum[,'auto'])))
aggfi_ttauto <- sum(elast_ttauto*(database$fe_pess*predictions_base_rum[,'auto']/sum(database$fe_pess*predictions_base_rum[,'auto'])))
agg_ttauto <- sum(elast_ttauto*(predictions_base_rum[,'auto']/sum(predictions_base_rum[,'auto'])))
#tt bus
elast_ttbus <- (1-predictions_base_rum[,'bus'])*model_lc$estimate[['b_ttbus_1']]*database$tt_bus_otp
aggfv_ttbus <- sum(elast_ttbus*(database$fe_via*predictions_base_rum[,'bus']/sum(database$fe_via*predictions_base_rum[,'bus'])))
aggfi_ttbus <- sum(elast_ttbus*(database$fe_pess*predictions_base_rum[,'bus']/sum(database$fe_pess*predictions_base_rum[,'bus'])))
agg_ttbus <- sum(elast_ttbus*(predictions_base_rum[,'bus']/sum(predictions_base_rum[,'bus'])))
#tt metro
elast_ttmetro <- (1-predictions_base_rum[,'metro'])*model_lc$estimate[['b_ttmetro_1']]*database$tt_metro
aggfv_ttmetro <- sum(elast_ttmetro*(database$fe_via*predictions_base_rum[,'metro']/sum(database$fe_via*predictions_base_rum[,'metro'])))
aggfi_ttmetro <- sum(elast_ttmetro*(database$fe_pess*predictions_base_rum[,'metro']/sum(database$fe_pess*predictions_base_rum[,'metro'])))
agg_ttmetro <- sum(elast_ttmetro*(predictions_base_rum[,'metro']/sum(predictions_base_rum[,'metro'])))
#co auto
elast_coauto <- (1-predictions_base_rum[,'auto'])*model_lc$estimate[['b_co_1']]*database$co_auto
aggfv_coauto <- sum(elast_coauto*(database$fe_via*predictions_base_rum[,'auto']/sum(database$fe_via*predictions_base_rum[,'auto'])))
aggfi_coauto <- sum(elast_coauto*(database$fe_pess*predictions_base_rum[,'auto']/sum(database$fe_pess*predictions_base_rum[,'auto'])))
agg_coauto <- sum(elast_coauto*(predictions_base_rum[,'auto']/sum(predictions_base_rum[,'auto'])))
#co bus
elast_cobus <- (1-predictions_base_rum[,'bus'])*model_lc$estimate[['b_co_1']]*database$co_bus
aggfv_cobus <- sum(elast_cobus*(database$fe_via*predictions_base_rum[,'bus']/sum(database$fe_via*predictions_base_rum[,'bus'])))
aggfi_cobus <- sum(elast_cobus*(database$fe_pess*predictions_base_rum[,'bus']/sum(database$fe_pess*predictions_base_rum[,'bus'])))
agg_cobus <- sum(elast_cobus*(predictions_base_rum[,'bus']/sum(predictions_base_rum[,'bus'])))
#co metro
elast_cometro <- (1-predictions_base_rum[,'metro'])*model_lc$estimate[['b_co_1']]*database$co_metro
aggfv_cometro <- sum(elast_cobus*(database$fe_via*predictions_base_rum[,'metro']/sum(database$fe_via*predictions_base_rum[,'metro'])))
aggfi_cometro <- sum(elast_cobus*(database$fe_pess*predictions_base_rum[,'metro']/sum(database$fe_pess*predictions_base_rum[,'metro'])))
agg_cometro <- sum(elast_cobus*(predictions_base_rum[,'metro']/sum(predictions_base_rum[,'metro'])))
#enumerado
agg_coauto
agg_cobus
agg_cometro
agg_ttauto
agg_ttbus
agg_ttmetro
#viagem
aggfv_coauto
aggfv_cobus
aggfv_cometro
aggfv_ttauto
aggfv_ttbus
aggfv_ttmetro
#individuo
aggfi_coauto
aggfi_cobus
aggfi_cometro
aggfi_ttauto
aggfi_ttbus
aggfi_ttmetro
############### PAPER #######
#### RRM
#tt auto
elast_ttauto <- (model_lc$estimate[['b_ttauto_2']]/(exp(-(model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1) +
model_lc$estimate[['b_ttauto_2']]/(exp(-(model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1) +
(-model_lc$estimate[['b_ttauto_2']]/(exp(-(model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1))*predictions_base_rrm[,'auto'] +
(-model_lc$estimate[['b_ttauto_2']]/(exp(-(model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1))*predictions_base_rrm[,'auto'] +
model_lc$estimate[['b_ttauto_2']]/(exp((model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1)*predictions_base_rrm[,'bus'] +
model_lc$estimate[['b_ttauto_2']]/(exp((model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttauto_2']]*database$tt_auto))+1)*predictions_base_rrm[,'metro'])*database$tt_auto
aggfv_ttauto <- sum(elast_ttauto*(database$fe_via*predictions_base_rrm[,'auto']/sum(database$fe_via*predictions_base_rrm[,'auto'])))
aggfi_ttauto <- sum(elast_ttauto*(database$fe_pess*predictions_base_rrm[,'auto']/sum(database$fe_pess*predictions_base_rrm[,'auto'])))
agg_ttauto <- sum(elast_ttauto*(predictions_base_rrm[,'auto']/sum(predictions_base_rrm[,'auto'])))
#bus
elast_ttbus <- (model_lc$estimate[['b_ttbus_2']]/(exp(-(model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1) +
model_lc$estimate[['b_ttbus_2']]/(exp(-(model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1) +
(-model_lc$estimate[['b_ttbus_2']]/(exp(-(model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1))*predictions_base_rrm[,'bus'] +
(-model_lc$estimate[['b_ttbus_2']]/(exp(-(model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1))*predictions_base_rrm[,'bus'] +
model_lc$estimate[['b_ttbus_2']]/(exp((model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1)*predictions_base_rrm[,'auto'] +
model_lc$estimate[['b_ttbus_2']]/(exp((model_lc$estimate[['b_ttmetro_2']]*database$tt_metro - model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp))+1)*predictions_base_rrm[,'metro'])*database$tt_bus_otp
aggfv_ttbus <- sum(elast_ttbus*(database$fe_via*predictions_base_rrm[,'bus']/sum(database$fe_via*predictions_base_rrm[,'bus'])))
aggfi_ttbus <- sum(elast_ttbus*(database$fe_pess*predictions_base_rrm[,'bus']/sum(database$fe_pess*predictions_base_rrm[,'bus'])))
agg_ttbus <- sum(elast_ttbus*(predictions_base_rrm[,'bus']/sum(predictions_base_rrm[,'bus'])))
#metro
elast_ttmetro <- (model_lc$estimate[['b_ttmetro_2']]/(exp(-(model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1) +
model_lc$estimate[['b_ttmetro_2']]/(exp(-(model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1) +
(-model_lc$estimate[['b_ttmetro_2']]/(exp(-(model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1))*predictions_base_rrm[,'metro'] +
(-model_lc$estimate[['b_ttmetro_2']]/(exp(-(model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1))*predictions_base_rrm[,'metro'] +
model_lc$estimate[['b_ttmetro_2']]/(exp((model_lc$estimate[['b_ttauto_2']]*database$tt_auto - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1)*predictions_base_rrm[,'auto'] +
model_lc$estimate[['b_ttmetro_2']]/(exp((model_lc$estimate[['b_ttbus_2']]*database$tt_bus_otp - model_lc$estimate[['b_ttmetro_2']]*database$tt_metro))+1)*predictions_base_rrm[,'bus'])*database$tt_metro
aggfv_ttmetro <- sum(elast_ttmetro*(database$fe_via*predictions_base_rrm[,'metro']/sum(database$fe_via*predictions_base_rrm[,'metro'])))
aggfi_ttmetro <- sum(elast_ttmetro*(database$fe_pess*predictions_base_rrm[,'metro']/sum(database$fe_pess*predictions_base_rrm[,'metro'])))
agg_ttmetro <- sum(elast_ttmetro*(predictions_base_rrm[,'metro']/sum(predictions_base_rrm[,'metro'])))
#co auto
elast_coauto <- (model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_bus - model_lc$estimate[['b_co_2']]*database$co_auto))+1) +
model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_metro - model_lc$estimate[['b_co_2']]*database$co_auto))+1) +
(-model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_bus - model_lc$estimate[['b_co_2']]*database$co_auto))+1))*predictions_base_rrm[,'auto'] +
(-model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_metro - model_lc$estimate[['b_co_2']]*database$co_auto))+1))*predictions_base_rrm[,'auto'] +
model_lc$estimate[['b_co_2']]/(exp((model_lc$estimate[['b_co_2']]*database$co_bus - model_lc$estimate[['b_co_2']]*database$co_auto))+1)*predictions_base_rrm[,'bus'] +
model_lc$estimate[['b_co_2']]/(exp((model_lc$estimate[['b_co_2']]*database$co_metro - model_lc$estimate[['b_co_2']]*database$co_auto))+1)*predictions_base_rrm[,'metro'])*database$co_auto
aggfv_coauto <- sum(elast_coauto*(database$fe_via*predictions_base_rrm[,'auto']/sum(database$fe_via*predictions_base_rrm[,'auto'])))
aggfi_coauto <- sum(elast_coauto*(database$fe_pess*predictions_base_rrm[,'auto']/sum(database$fe_pess*predictions_base_rrm[,'auto'])))
agg_coauto <- sum(elast_coauto*(predictions_base_rrm[,'auto']/sum(predictions_base_rrm[,'auto'])))
#co bus
elast_cobus <- (model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_auto - model_lc$estimate[['b_co_2']]*database$co_bus))+1) +
model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_metro - model_lc$estimate[['b_co_2']]*database$co_bus))+1) +
(-model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_auto - model_lc$estimate[['b_co_2']]*database$co_bus))+1))*predictions_base_rrm[,'bus'] +
(-model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_metro - model_lc$estimate[['b_co_2']]*database$co_bus))+1))*predictions_base_rrm[,'bus'] +
model_lc$estimate[['b_co_2']]/(exp((model_lc$estimate[['b_co_2']]*database$co_auto - model_lc$ estimate[['b_co_2']]*database$co_bus))+1)*predictions_base_rrm[,'auto'] +
model_lc$estimate[['b_co_2']]/(exp((model_lc$estimate[['b_co_2']]*database$co_metro - model_lc$estimate[['b_co_2']]*database$co_bus))+1)*predictions_base_rrm[,'metro'])*database$co_bus
aggfv_cobus <- sum(elast_cobus*(database$fe_via*predictions_base_rrm[,'bus']/sum(database$fe_via*predictions_base_rrm[,'bus'])))
aggfi_cobus <- sum(elast_cobus*(database$fe_pess*predictions_base_rrm[,'bus']/sum(database$fe_pess*predictions_base_rrm[,'bus'])))
agg_cobus <- sum(elast_cobus*(predictions_base_rrm[,'bus']/sum(predictions_base_rrm[,'bus'])))
#co metro
elast_cometro <- (model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_auto - model_lc$estimate[['b_co_2']]*database$co_metro))+1) +
model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_bus - model_lc$estimate[['b_co_2']]*database$co_metro))+1) +
(-model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_auto - model_lc$estimate[['b_co_2']]*database$co_metro))+1))*predictions_base_rrm[,'metro'] +
(-model_lc$estimate[['b_co_2']]/(exp(-(model_lc$estimate[['b_co_2']]*database$co_bus - model_lc$estimate[['b_co_2']]*database$co_metro))+1))*predictions_base_rrm[,'metro'] +
model_lc$estimate[['b_co_2']]/(exp((model_lc$estimate[['b_co_2']]*database$co_auto - model_lc$estimate[['b_co_2']]*database$co_metro))+1)*predictions_base_rrm[,'auto'] +
model_lc$estimate[['b_co_2']]/(exp((model_lc$estimate[['b_co_2']]*database$co_bus - model_lc$estimate[['b_co_2']]*database$co_metro))+1)*predictions_base_rrm[,'bus'])*database$co_metro
aggfv_cometro <- sum(elast_cobus*(database$fe_via*predictions_base_rrm[,'metro']/sum(database$fe_via*predictions_base_rrm[,'metro'])))
aggfi_cometro <- sum(elast_cobus*(database$fe_pess*predictions_base_rrm[,'metro']/sum(database$fe_pess*predictions_base_rrm[,'metro'])))
agg_cometro <- sum(elast_cobus*(predictions_base_rrm[,'metro']/sum(predictions_base_rrm[,'metro'])))
#enumerado
agg_coauto
agg_cobus
agg_cometro
agg_ttauto
agg_ttbus
agg_ttmetro
#viagem
aggfv_coauto
aggfv_cobus
aggfv_cometro
aggfv_ttauto
aggfv_ttbus
aggfv_ttmetro
#individuo
aggfi_coauto
aggfi_cobus
aggfi_cometro
aggfi_ttauto
aggfi_ttbus
aggfi_ttmetro
write_csv(vtts_models,'vtts_models.csv')