Hello,
I need help with the specification of my model. I would like to analyze my data by age groups, gender, income and area character (Gebietstyp). However, I need help to integrate them in the code. So far I have always received errors.
I already tried to integrate the age. Alter = age
Can you please help me to find the problem?
Thank you very much,
Nina
choiceAnalysis_settings <- list(
alternatives = c(altA=1, altB=2, statusquo=3),
avail = list(altA = 1, altB = 1, statusquo = 1),
choiceVar = database$choice,
explanators = database[, c("Gebietstyp")]
)
apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database)
## Parameter
apollo_beta <- c(
b10 = 0,
b11 = 0,
b12 = 0,
b10_shift_Alter = 0,
b11_shift_Alter = 0,
b12_shift_Alter = 0,
b20 = 0,
b21 = 0,
b22 = 0,
b23 = 0,
b20_shift_Alter = 0,
b21_shift_Alter = 0,
b22_shift_Alter = 0,
b23_shift_Alter = 0,
b30 = 0,
b30_shift_Alter = 0,
b40 = 0,
b41 = 0,
b42 = 0,
b40_shift_Alter = 0,
b41_shift_Alter = 0,
b42_shift_Alter = 0,
b50 = 0,
b50_shift_Alter = 0,
b60 = 0)
apollo_fixed = c("b12", "b23", "b42")
apollo_inputs <- apollo_validateInputs()
apollo_probabilities <- function(apollo_beta, apollo_inputs, functionality="estimate"){
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
## Modell
P <- list()
b10_value = b10 + b10_shift_Alter * Alter
b11_value = b11 + b11_shift_Alter * Alter
b12_value = b12 + b12_shift_Alter * Alter
b20_value = b20 + b20_shift_Alter * Alter
b21_value = b21 + b21_shift_Alter * Alter
b22_value = b22 + b22_shift_Alter * Alter
b23_value = b23 + b23_shift_Alter * Alter
b30_value = b30 + b30_shift_Alter * Alter
b40_value = b40 + b40_shift_Alter * Alter
b41_value = b41 + b41_shift_Alter * Alter
b42_value = b42 + b42_shift_Alter * Alter
b50_value = b50 + b50_shift_Alter * Alter
V <- list()
V[["altA"]] = (b10_value*(Parkraumangebot_1==0) + b11_value*(Parkraumangebot_1==1) + b12_value*(Parkraumangebot_1==2) +
b20_value*(Alternativnutzung_1==0) + b21_value*(Alternativnutzung_1==1) + b22_value*(Alternativnutzung_1==2) + b23_value*(Alternativnutzung_1==3) +
b30_value*Entfernung_1 +
b40_value*(Verkehrsberuhigung_1==0) + b41_value*(Verkehrsberuhigung_1==1) + b42_value*(Verkehrsberuhigung_1==2) +
b50_value*(Stellplatzreduktion_1)
)
V[["altB"]] = (b10_value*(Parkraumangebot_2==0) + b11_value*(Parkraumangebot_2==1) + b12_value*(Parkraumangebot_2==2) +
b20_value*(Alternativnutzung_2==0) + b21_Value*(Alternativnutzung_2==1) + b22_value*(Alternativnutzung_2==2) + b23_value*(Alternativnutzung_2==3) +
b30_value*Entfernung_2 +
b40_value*(Verkehrsberuhigung_2==0) + b41_value*(Verkehrsberuhigung_2==1) + b42_value*(Verkehrsberuhigung_2==2) +
b50_value*(Stellplatzreduktion_2)
)
V[["statusquo"]] = b60
mnl_settings = list(
alternatives = c(altA = 1, altB = 2, statusquo = 3),
avail = 1,
choiceVar = choice,
V = V
)
P[["model"]] <- apollo_mnl(mnl_settings, functionality)
P <- apollo_panelProd(P, apollo_inputs, functionality)
P <- apollo_prepareProb(P, apollo_inputs, functionality)
return (P)
}
#### Modell Schaetzung
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)
apollo_modelOutput(model, list(printPVal = TRUE))
# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name) ----
# ----------------------------------------------------------------- #
apollo_saveOutput(model)
Error:
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)
Error in apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, :
object 'apollo_inputs' not found
> apollo_modelOutput(model, list(printPVal = TRUE))
Error in apollo_modelOutput(model, list(printPVal = TRUE)) :
object 'model' not found
Important: Read this before posting to this forum
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MNL model with socio demographics
-
stephanehess
- Site Admin
- Posts: 977
- Joined: 24 Apr 2020, 16:29
Re: MNL model with socio demographics
Hi
Can you please post your entire code? Also, it looks like the error message
is not the first error message you get as it can't find apollo_inputs so you likely have an error message earlier on to
Stephane
Can you please post your entire code? Also, it looks like the error message
Code: Select all
Error in apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, :
object 'apollo_inputs' not foundStephane
Re: MNL model with socio demographics
Hi Stephan,
thank you very much for your quick response.
My entire code ist:
rm(list = ls())
library(data.table)
library(gsubfn)
library(tidyverse)
modelData <- fread("daten_erhebung.csv", na ="NA")
modelData <- data.table(modelData)
modelData <- modelData[ , .(lfdn,
PkwFahrer,
PkwMitfahrer,
Carsharing,
Motorrad,
ÖV,
Fahrrad,
Fuß,
PkwVerf,
PkwimHH,
Garage,
EigStellpl,
Tiefgarage,
Anwohnerparkhaus,
Straßenraum,
Sonstiges,
DauerSuche,
EntfernungStellw,
Zufriedenheit,
Einordnung,
ProbAnzahlStell,
ProbAufenthalt,
ProbKfz,
ProbRadFuß,
ProbSonstiges,
KommSonstiges,
keineProb,
Stadttyp,
Gebietstyp,
cs1,
cs1pkw,
cs1carsharing,
cs2,
cs2pkw,
cs2carsharing,
cs3,
cs3pkw,
cs3carsharing,
cs4,
cs4pkw,
cs4carsharing,
cs5,
cs5pkw,
cs5carsharing,
cs6,
cs6pkw,
cs6carsharing,
cs7,
cs7pkw,
cs7carsharing,
cs8,
cs8pkw,
cs8carsharing,
cs9,
cs9pkw,
cs9carsharing,
v_91,
v_92,
v_93,
v_94,
v_95,
v_96,
v_97,
v_130,
v_123,
v_124,
v_125,
v_126,
v_129,
v_208,
v_209,
Alter,
Geschlecht,
Bildungsabschluss,
Erwerbstätigkeit,
Erwerbst_sonstiges,
AnzahlPimHH,
KinderimHH,
Einkommen,
Bundesland)]
attributlevel <- read.csv2("Attributlevel_Hauptbefragung.csv")
attributlevel <- data.table(attributlevel)
attributlevel <- attributlevel[, choice_situtation:= as.factor(Frage)]
#Interne testlaeufe ausfiltern
#modelData <- modelData[v_312 != -77]
#from wide to long
modelData <- melt(modelData, measure.vars = c("cs1",
"cs2",
"cs3",
"cs4",
"cs5",
"cs6",
"cs7",
"cs8",
"cs9"),
variable.name = "choice_situation", value.name = "choice")
modelData <- modelData[choice != -77]
modelData <- data.table(modelData)
modelData[ , choice_situation_int := as.integer(ifelse(choice_situation == "cs1" , 1,
ifelse(choice_situation == "cs2" , 2,
ifelse(choice_situation == "cs3" , 3,
ifelse(choice_situation == "cs4" , 4,
ifelse(choice_situation == "cs5" , 5,
ifelse(choice_situation == "cs6" , 6,
ifelse(choice_situation == "cs7" , 7,
ifelse(choice_situation == "cs8" , 8,
ifelse(choice_situation == "cs9" , 9,0))))))))))]
modelData <- merge(modelData, attributlevel, by.x = c("choice_situation_int"), by.y = c("Frage"), all.x = TRUE)
write.csv(modelData, "daten_erhebung_prepared.csv")
###################################### Modelschätzung ############################################
library(apollo)
apollo_initialise()
apollo_control = list(
modelName ="MNL_heterogeneity_level_1",
modelDescr ="MNL model with socio-demographics on choice data",
indivID ="lfdn"
)
## Daten
database = read.csv("daten_erhebung.csv", header = TRUE)
database <- database %>% arrange(lfdn)
#database = subset(database,database$RP==1)
## Choice Analyse
choiceAnalysis_settings <- list(
alternatives = c(altA=1, altB=2, statusquo=3),
avail = list(altA = 1, altB = 1, statusquo = 1),
choiceVar = database$choice,
explanators = database[, c("Alter")]
)
apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database)
## Parameter
apollo_beta <- c(
b10 = 0,
b11 = 0,
b12 = 0,
b10_shift_Alter = 0,
b11_shift_Alter = 0,
b12_shift_Alter = 0,
b20 = 0,
b21 = 0,
b22 = 0,
b23 = 0,
b20_shift_Alter = 0,
b21_shift_Alter = 0,
b22_shift_Alter = 0,
b23_shift_Alter = 0,
b30 = 0,
b30_shift_Alter = 0,
b40 = 0,
b41 = 0,
b42 = 0,
b40_shift_Alter = 0,
b41_shift_Alter = 0,
b42_shift_Alter = 0,
b50 = 0,
b50_shift_Alter = 0,
b60 = 0)
apollo_fixed = c("b12", "b23", "b42")
apollo_inputs <- apollo_validateInputs()
apollo_probabilities <- function(apollo_beta, apollo_inputs, functionality="estimate"){
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
## Modell
P <- list()
b10_value = b10 + b10_shift_Alter * Alter
b11_value = b11 + b11_shift_Alter * Alter
b12_value = b12 + b12_shift_Alter * Alter
b20_value = b20 + b20_shift_Alter * Alter
b21_value = b21 + b21_shift_Alter * Alter
b22_value = b22 + b22_shift_Alter * Alter
b23_value = b23 + b23_shift_Alter * Alter
b30_value = b30 + b30_shift_Alter * Alter
b40_value = b40 + b40_shift_Alter * Alter
b41_value = b41 + b41_shift_Alter * Alter
b42_value = b42 + b42_shift_Alter * Alter
b50_value = b50 + b50_shift_Alter * Alter
V <- list()
V[["altA"]] = (b10_value*(Parkraumangebot_1==0) + b11_value*(Parkraumangebot_1==1) + b12_value*(Parkraumangebot_1==2) +
b20_value*(Alternativnutzung_1==0) + b21_value*(Alternativnutzung_1==1) + b22_value*(Alternativnutzung_1==2) + b23_value*(Alternativnutzung_1==3) +
b30_value*Entfernung_1 +
b40_value*(Verkehrsberuhigung_1==0) + b41_value*(Verkehrsberuhigung_1==1) + b42_value*(Verkehrsberuhigung_1==2) +
b50_value*(Stellplatzreduktion_1)
)
V[["altB"]] = (b10_value*(Parkraumangebot_2==0) + b11_value*(Parkraumangebot_2==1) + b12_value*(Parkraumangebot_2==2) +
b20_value*(Alternativnutzung_2==0) + b21_Value*(Alternativnutzung_2==1) + b22_value*(Alternativnutzung_2==2) + b23_value*(Alternativnutzung_2==3) +
b30_value*Entfernung_2 +
b40_value*(Verkehrsberuhigung_2==0) + b41_value*(Verkehrsberuhigung_2==1) + b42_value*(Verkehrsberuhigung_2==2) +
b50_value*(Stellplatzreduktion_2)
)
V[["statusquo"]] = b60
mnl_settings = list(
alternatives = c(altA = 1, altB = 2, statusquo = 3),
avail = 1,
choiceVar = choice,
V = V
)
P[["model"]] <- apollo_mnl(mnl_settings, functionality)
P <- apollo_panelProd(P, apollo_inputs, functionality)
P <- apollo_prepareProb(P, apollo_inputs, functionality)
return (P)
}
#### Modell Schaetzung
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)
apollo_modelOutput(model, list(printPVal = TRUE))
# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name) ----
# ----------------------------------------------------------------- #
apollo_saveOutput(model)
thank you very much for your quick response.
My entire code ist:
rm(list = ls())
library(data.table)
library(gsubfn)
library(tidyverse)
modelData <- fread("daten_erhebung.csv", na ="NA")
modelData <- data.table(modelData)
modelData <- modelData[ , .(lfdn,
PkwFahrer,
PkwMitfahrer,
Carsharing,
Motorrad,
ÖV,
Fahrrad,
Fuß,
PkwVerf,
PkwimHH,
Garage,
EigStellpl,
Tiefgarage,
Anwohnerparkhaus,
Straßenraum,
Sonstiges,
DauerSuche,
EntfernungStellw,
Zufriedenheit,
Einordnung,
ProbAnzahlStell,
ProbAufenthalt,
ProbKfz,
ProbRadFuß,
ProbSonstiges,
KommSonstiges,
keineProb,
Stadttyp,
Gebietstyp,
cs1,
cs1pkw,
cs1carsharing,
cs2,
cs2pkw,
cs2carsharing,
cs3,
cs3pkw,
cs3carsharing,
cs4,
cs4pkw,
cs4carsharing,
cs5,
cs5pkw,
cs5carsharing,
cs6,
cs6pkw,
cs6carsharing,
cs7,
cs7pkw,
cs7carsharing,
cs8,
cs8pkw,
cs8carsharing,
cs9,
cs9pkw,
cs9carsharing,
v_91,
v_92,
v_93,
v_94,
v_95,
v_96,
v_97,
v_130,
v_123,
v_124,
v_125,
v_126,
v_129,
v_208,
v_209,
Alter,
Geschlecht,
Bildungsabschluss,
Erwerbstätigkeit,
Erwerbst_sonstiges,
AnzahlPimHH,
KinderimHH,
Einkommen,
Bundesland)]
attributlevel <- read.csv2("Attributlevel_Hauptbefragung.csv")
attributlevel <- data.table(attributlevel)
attributlevel <- attributlevel[, choice_situtation:= as.factor(Frage)]
#Interne testlaeufe ausfiltern
#modelData <- modelData[v_312 != -77]
#from wide to long
modelData <- melt(modelData, measure.vars = c("cs1",
"cs2",
"cs3",
"cs4",
"cs5",
"cs6",
"cs7",
"cs8",
"cs9"),
variable.name = "choice_situation", value.name = "choice")
modelData <- modelData[choice != -77]
modelData <- data.table(modelData)
modelData[ , choice_situation_int := as.integer(ifelse(choice_situation == "cs1" , 1,
ifelse(choice_situation == "cs2" , 2,
ifelse(choice_situation == "cs3" , 3,
ifelse(choice_situation == "cs4" , 4,
ifelse(choice_situation == "cs5" , 5,
ifelse(choice_situation == "cs6" , 6,
ifelse(choice_situation == "cs7" , 7,
ifelse(choice_situation == "cs8" , 8,
ifelse(choice_situation == "cs9" , 9,0))))))))))]
modelData <- merge(modelData, attributlevel, by.x = c("choice_situation_int"), by.y = c("Frage"), all.x = TRUE)
write.csv(modelData, "daten_erhebung_prepared.csv")
###################################### Modelschätzung ############################################
library(apollo)
apollo_initialise()
apollo_control = list(
modelName ="MNL_heterogeneity_level_1",
modelDescr ="MNL model with socio-demographics on choice data",
indivID ="lfdn"
)
## Daten
database = read.csv("daten_erhebung.csv", header = TRUE)
database <- database %>% arrange(lfdn)
#database = subset(database,database$RP==1)
## Choice Analyse
choiceAnalysis_settings <- list(
alternatives = c(altA=1, altB=2, statusquo=3),
avail = list(altA = 1, altB = 1, statusquo = 1),
choiceVar = database$choice,
explanators = database[, c("Alter")]
)
apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database)
## Parameter
apollo_beta <- c(
b10 = 0,
b11 = 0,
b12 = 0,
b10_shift_Alter = 0,
b11_shift_Alter = 0,
b12_shift_Alter = 0,
b20 = 0,
b21 = 0,
b22 = 0,
b23 = 0,
b20_shift_Alter = 0,
b21_shift_Alter = 0,
b22_shift_Alter = 0,
b23_shift_Alter = 0,
b30 = 0,
b30_shift_Alter = 0,
b40 = 0,
b41 = 0,
b42 = 0,
b40_shift_Alter = 0,
b41_shift_Alter = 0,
b42_shift_Alter = 0,
b50 = 0,
b50_shift_Alter = 0,
b60 = 0)
apollo_fixed = c("b12", "b23", "b42")
apollo_inputs <- apollo_validateInputs()
apollo_probabilities <- function(apollo_beta, apollo_inputs, functionality="estimate"){
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
## Modell
P <- list()
b10_value = b10 + b10_shift_Alter * Alter
b11_value = b11 + b11_shift_Alter * Alter
b12_value = b12 + b12_shift_Alter * Alter
b20_value = b20 + b20_shift_Alter * Alter
b21_value = b21 + b21_shift_Alter * Alter
b22_value = b22 + b22_shift_Alter * Alter
b23_value = b23 + b23_shift_Alter * Alter
b30_value = b30 + b30_shift_Alter * Alter
b40_value = b40 + b40_shift_Alter * Alter
b41_value = b41 + b41_shift_Alter * Alter
b42_value = b42 + b42_shift_Alter * Alter
b50_value = b50 + b50_shift_Alter * Alter
V <- list()
V[["altA"]] = (b10_value*(Parkraumangebot_1==0) + b11_value*(Parkraumangebot_1==1) + b12_value*(Parkraumangebot_1==2) +
b20_value*(Alternativnutzung_1==0) + b21_value*(Alternativnutzung_1==1) + b22_value*(Alternativnutzung_1==2) + b23_value*(Alternativnutzung_1==3) +
b30_value*Entfernung_1 +
b40_value*(Verkehrsberuhigung_1==0) + b41_value*(Verkehrsberuhigung_1==1) + b42_value*(Verkehrsberuhigung_1==2) +
b50_value*(Stellplatzreduktion_1)
)
V[["altB"]] = (b10_value*(Parkraumangebot_2==0) + b11_value*(Parkraumangebot_2==1) + b12_value*(Parkraumangebot_2==2) +
b20_value*(Alternativnutzung_2==0) + b21_Value*(Alternativnutzung_2==1) + b22_value*(Alternativnutzung_2==2) + b23_value*(Alternativnutzung_2==3) +
b30_value*Entfernung_2 +
b40_value*(Verkehrsberuhigung_2==0) + b41_value*(Verkehrsberuhigung_2==1) + b42_value*(Verkehrsberuhigung_2==2) +
b50_value*(Stellplatzreduktion_2)
)
V[["statusquo"]] = b60
mnl_settings = list(
alternatives = c(altA = 1, altB = 2, statusquo = 3),
avail = 1,
choiceVar = choice,
V = V
)
P[["model"]] <- apollo_mnl(mnl_settings, functionality)
P <- apollo_panelProd(P, apollo_inputs, functionality)
P <- apollo_prepareProb(P, apollo_inputs, functionality)
return (P)
}
#### Modell Schaetzung
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)
apollo_modelOutput(model, list(printPVal = TRUE))
# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name) ----
# ----------------------------------------------------------------- #
apollo_saveOutput(model)
Last edited by Nina on 06 Nov 2021, 07:07, edited 1 time in total.
-
stephanehess
- Site Admin
- Posts: 977
- Joined: 24 Apr 2020, 16:29
Re: MNL model with socio demographics
Hi
so what happens if you run this in segments, i.e. do you get errors already after
or after
Stephane
so what happens if you run this in segments, i.e. do you get errors already after
Code: Select all
apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database)Code: Select all
apollo_inputs <- apollo_validateInputs()Re: MNL model with socio demographics
I get this error after > apollo_inputs <- apollo_validateInputs()
Error in do.call("cbind", lapply(x, "is.na")) :
variable names are limited to 10000 bytes
How can I solve this Error?
Best regards,
Nina
Error in do.call("cbind", lapply(x, "is.na")) :
variable names are limited to 10000 bytes
How can I solve this Error?
Best regards,
Nina
-
stephanehess
- Site Admin
- Posts: 977
- Joined: 24 Apr 2020, 16:29
Re: MNL model with socio demographics
this is not a problem we've ever encountered before and would seem to have to do with your data. Are you able to share your files with me via e-mail and I'll look into it for you
Re: MNL model with socio demographics
Thank you very much for your offer to view my data.
I just send you my data via email
I just send you my data via email
-
stephanehess
- Site Admin
- Posts: 977
- Joined: 24 Apr 2020, 16:29
Re: MNL model with socio demographics
It looks like this issue arise as the names in the header where not separated into individual names when the file was being read in by R.
-
stephanehess
- Site Admin
- Posts: 977
- Joined: 24 Apr 2020, 16:29
Re: MNL model with socio demographics
Nina
related to my earlier comment about column names, what do you see if you use
colnames(database)
after reading in your database?
Thanks
Stephane
related to my earlier comment about column names, what do you see if you use
colnames(database)
after reading in your database?
Thanks
Stephane