Joint estimation on two SP datasets. Error: Duplicated componentName found
Posted: 08 Oct 2022, 12:18
Hi Stephane,
I am pooling data from two designs:
-Design 1 has 6 attributes
-Design 2 has the same attributes as Design 1, except for the last one "cost" (i.e. using only 5 attributes)
In my dataset, I have two binary variables to indicate Apollo which dataset I'm using: "Done" for data from Design 1, and "Dtwo" for data from design 2.
To estimate joint coefficients, I'm following this example: http://www.apollochoicemodelling.com/fi ... NL_RP_SP.r
Here is the code:
I'm getting the following error when I run the model: "Error in apollo_mnl(mnl_settings_Dtwo, functionality) :
Duplicated componentName found (MNL). Names must be different for each component." I checked the code and compared it with the example from the link, and can't see what's wrong with my code.
I also checked Chapt7 from the Apollo manual, and tried using the lapply function, but got the following error: "Error in get(as.character(FUN), mode = "function", envir = envir) :
object '∗' of mode 'function' was not found"
I also have one question about scaling: Both datasets are SP data and the choice tasks were practically the same (i.e same levels, same framing). The only difference lies in one attribute (i.e. Design 1 has six and Design 2 has five). Should I still expect scale differences between the two data sources?
I'd appreciate very much your help with this. Thanks!
Best wishes,
Pamela.
I am pooling data from two designs:
-Design 1 has 6 attributes
-Design 2 has the same attributes as Design 1, except for the last one "cost" (i.e. using only 5 attributes)
In my dataset, I have two binary variables to indicate Apollo which dataset I'm using: "Done" for data from Design 1, and "Dtwo" for data from design 2.
To estimate joint coefficients, I'm following this example: http://www.apollochoicemodelling.com/fi ... NL_RP_SP.r
Here is the code:
Code: Select all
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta=c(asc = 0,
byol = 0,
bqol = 0,
bexp_fair = 0,
bexp_good = 0,
bsize = 0,
bequ = 0,
bcost = 0,
mu_Done = 1,
mu_Dtwo = 1
)
### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
##If we want to keep parameters fixed to their starting values during the estimation (eg. asc), we include their names in the character vector apollo_fixed.
## this vector is kept empty (apollo_fixed = c()) if all parameters are to be estimated. Parameters included in apollo_fixed are kept at the value used in apollo_beta, which may not be zero
apollo_fixed = c("mu_Done")
# ################################################################# #
#### GROUP AND VALIDATE INPUTS ####
# ################################################################# #
apollo_inputs = apollo_validateInputs()
# ################################################################# #
#### DEFINE MODEL AND LIKELIHOOD FUNCTION ####
# ################################################################# #
apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
P = list()
### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
V = list()
V[["Alt1"]] = asc + byol*yol1 + bqol*qol1 + bexp_fair*(exp1==1) + bexp_good*(exp1==2) + bsize*size1 + bequ*equ1 + bcost*cost1
V[["Alt2"]] = byol*yol2 + bqol*qol2 + bexp_fair*(exp2==1) + bexp_good*(exp2==2) + bsize*size2 + bequ*equ2 + bcost*cost2
### Compute probabilities for 'Design 1' of the data using MNL model
## Define settings for MNL model component
mnl_settings_Done = list(
alternatives = c(Alt1=1, Alt2=2),
avail = list(Alt1=1, Alt2=1), ##A list containing one element per alternative, using the same names as in alternatives (scalar of 1 because the alternative is always available) alternative is always available.)
choiceVar = chosen_option,
utilities = list(Alt1 = mu_Done*V[["Alt1"]],
Alt2 = mu_Done*V[["Alt2"]]),
rows = (Done==1)
)
## Compute Design 1 probabilities using MNL model
P[["Done"]] = apollo_mnl(mnl_settings_Done, functionality)
### Compute probabilities for 'Design 2' of the data using MNL model
### Define settings for MNL model component
mnl_settings_Dtwo = list(
alternatives = c(Alt1=1, Alt2=2),
avail = list(Alt1=1, Alt2=1),
choiceVar = chosen_option,
utilities = list(Alt1 = mu_Dtwo*V[["Alt1"]],
Alt2 = mu_Dtwo*V[["Alt2"]]),
rows = (Dtwo==1)
)
### Compute Design 2 probabilities using MNL model
P[["Dtwo"]] = apollo_mnl(mnl_settings_Dtwo, functionality)
### Combined model
P = apollo_combineModels(P, apollo_inputs, functionality)
### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
Duplicated componentName found (MNL). Names must be different for each component." I checked the code and compared it with the example from the link, and can't see what's wrong with my code.
I also checked Chapt7 from the Apollo manual, and tried using the lapply function, but got the following error: "Error in get(as.character(FUN), mode = "function", envir = envir) :
object '∗' of mode 'function' was not found"
I also have one question about scaling: Both datasets are SP data and the choice tasks were practically the same (i.e same levels, same framing). The only difference lies in one attribute (i.e. Design 1 has six and Design 2 has five). Should I still expect scale differences between the two data sources?
I'd appreciate very much your help with this. Thanks!
Best wishes,
Pamela.