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LC models class probability and confidence interval
Posted: 28 Oct 2024, 15:12
by simbala
Hi Apollo Team,
I have two questions regarding the output, but I couldn't find a solution after reading the manual. Using this LC model as an example
http://www.apollochoicemodelling.com/fi ... variates.r
1. Is there a way to save the estimated mean class probability in the output file? For example, if I have 2 classes with the mean probability of 0.7 and 0.3, how can I save those numbers in the final output file?
2. How can I also get the output for the 95% confidence interval of the estimates? I can figure out a way to output the p-values by adding
Code: Select all
saveOutput_settings = list(printPVal=2)
Is there a similar way to get and save 95% CI as well?
Thank you in advance for your help!
Re: LC models class probability and confidence interval
Posted: 30 Oct 2024, 08:57
by stephanehess
Hi
1. the mean class probabilites are saved in the output file alraedy, see e.g. at
https://apollochoicemodelling.com/files ... output.txt
2. we do not automatically compute CIs. You can compute those easily as the estimate +/- 1.96 * s.e.
Stephane
Re: LC models class probability and confidence interval
Posted: 30 Oct 2024, 19:09
by simbala
Hi Professor Hess,
Thank you! I just checked I used the same code from
https://apollochoicemodelling.com/files ... output.txt, but my output file looked different. I am missing many parts of the output, including summary of model component. I couldn't identify where it might have gone wrong. Could you help me with this issue?
What I got is
Code: Select all
Model run by xx using Apollo 0.3.3 on R 4.4.1 for Windows.
Please acknowledge the use of Apollo by citing Hess & Palma (2019)
DOI 10.1016/j.jocm.2019.100170
www.ApolloChoiceModelling.com
Model name : LC_with_covariates
Model description : LC model with covariates in class allocation model on Swiss route choice data
Model run at : 2024-10-30 14:48:30.092399
Estimation method : bgw
Model diagnosis : Relative function convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definite
maximum eigenvalue : -8.872086
reciprocal of condition number : 5.37477e-05
Number of individuals : 388
Number of rows in database : 3492
Number of modelled outcomes : 3492
Number of cores used : 2
Model without mixing
LL(start) : -1755.5
LL (whole model) at equal shares, LL(0) : -2420.47
LL (whole model) at observed shares, LL(C) : -2420.39
LL(final, whole model) : -1559.01
Rho-squared vs equal shares : 0.3559
Adj.Rho-squared vs equal shares : 0.3509
Rho-squared vs observed shares : 0.3559
Adj.Rho-squared vs observed shares : 0.3518
AIC : 3142.03
BIC : 3215.93
LL(0,Class_1) : -2420.47
LL(final,Class_1) : -1776.33
LL(0,Class_2) : -2420.47
LL(final,Class_2) : -2467.09
Estimated parameters : 12
Time taken (hh:mm:ss) : 00:00:3.93
pre-estimation : 00:00:2.74
estimation : 00:00:0.41
post-estimation : 00:00:0.77
Iterations : 23
Unconstrained optimisation.
Estimates:
Summary of class allocation for model component :
Overview of choices for MNL model component Class_1:
alt1 alt2
Times available 3492.00 3492.00
Times chosen 1734.00 1758.00
Percentage chosen overall 49.66 50.34
Percentage chosen when available 49.66 50.34
Overview of choices for MNL model component Class_2:
alt1 alt2
Times available 3492.00 3492.00
Times chosen 1734.00 1758.00
Percentage chosen overall 49.66 50.34
Percentage chosen when available 49.66 50.34
Classical covariance matrix:
Robust covariance matrix:
Classical correlation matrix:
Robust correlation matrix:
20 most extreme outliers in terms of lowest average per choice prediction:
Changes in parameter estimates from starting values:
Settings and functions used in model definition:
apollo_control
--------------
Value
modelName "example_LC_with_covariates"
modelDescr "LC model with covariates in class allocation model on Swiss route choice data"
indivID "ID"
nCores "2"
outputDirectory "results/"
debug "FALSE"
workInLogs "FALSE"
seed "13"
mixing "FALSE"
HB "FALSE"
noValidation "FALSE"
noDiagnostics "FALSE"
calculateLLC "TRUE"
analyticHessian "FALSE"
memorySaver "FALSE"
panelData "TRUE"
analyticGrad "TRUE"
analyticGrad_manualSet "FALSE"
overridePanel "FALSE"
preventOverridePanel "FALSE"
noModification "FALSE"
Hessian routines attempted
--------------------------
numerical jacobian of LL analytical gradient
Scaling used in computing Hessian
---------------------------------
Value
asc_1 0.03462696
beta_tt_a 0.03724285
beta_tt_b 0.20952989
beta_tc_a 0.04989347
beta_tc_b 0.77576327
beta_hw_a 0.03426097
beta_hw_b 0.05466599
beta_ch_a 0.60949847
beta_ch_b 2.67045554
delta_a 0.20944353
gamma_commute_a 0.43370076
gamma_car_av_a 0.58813204
apollo_lcPars
---------------
function(apollo_beta, apollo_inputs){
lcpars = list()
lcpars[["beta_tt"]] = list(beta_tt_a, beta_tt_b)
lcpars[["beta_tc"]] = list(beta_tc_a, beta_tc_b)
lcpars[["beta_hw"]] = list(beta_hw_a, beta_hw_b)
lcpars[["beta_ch"]] = list(beta_ch_a, beta_ch_b)
### Utilities of class allocation model
V=list()
V[["class_a"]] = delta_a + gamma_commute_a*commute + gamma_car_av_a*car_availability
V[["class_b"]] = delta_b + gamma_commute_b*commute + gamma_car_av_b*car_availability
### Settings for class allocation models
classAlloc_settings = list(
classes = c(class_a=1, class_b=2),
utilities = V
)
lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings)
return(lcpars)
}
<bytecode:0x0000024490a01060>
apollo_probabilities
----------------------
function(apollo_beta, apollo_inputs, functionality="estimate"){
### 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()
### Define settings for MNL model component that are generic across classes
mnl_settings = list(
alternatives = c(alt1=1, alt2=2),
avail = list(alt1=1, alt2=1),
choiceVar = choice
)
### Loop over classes
for(s in 1:2){
### Compute class-specific utilities
V=list()
V[["alt1"]] = asc_1 + beta_tc[[s]]*tc1 + beta_tt[[s]]*tt1 + beta_hw[[s]]*hw1 + beta_ch[[s]]*ch1
V[["alt2"]] = asc_2 + beta_tc[[s]]*tc2 + beta_tt[[s]]*tt2 + beta_hw[[s]]*hw2 + beta_ch[[s]]*ch2
mnl_settings$utilities = V
mnl_settings$componentName = paste0("Class_",s)
### Compute within-class choice probabilities using MNL model
P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality)
}
### Compute latent class model 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)
}
Thank you!
Re: LC models class probability and confidence interval
Posted: 04 Dec 2024, 14:37
by stephanehess
Hi
do you see the estimates on screen if you use apollo_modelOutput?
We've never seen this issue before and it might be specific to your machine
Stephane