tests/testthat/_snaps/model_fitting.md
In loelschlaeger/RprobitB: Bayesian Probit Choice Modeling
setting prior parameter works
Code
prior
Output
$eta
[1] 0
$Psi
[,1]
[1,] 1
$delta
[1] 1
$xi
[1] 0 0
$D
[,1] [,2]
[1,] 1 0
[2,] 0 1
$nu
[1] 4
$Theta
[,1] [,2]
[1,] 1 0
[2,] 0 1
$kappa
[1] 4
$E
[,1] [,2]
[1,] 1 0
[2,] 0 1
$zeta
[1] NA
$Z
[1] NA
attr(,"class")
[1] "RprobitB_prior" "list"
setting of initial Gibbs values works
Code
init
Output
$alpha0
[1] 0
$z0
[1] 1 1
$m0
[1] 1 1
$b0
[,1] [,2]
[1,] 0 0
[2,] 0 0
$Omega0
[,1] [,2]
[1,] 1 1
[2,] 0 0
[3,] 0 0
[4,] 1 1
$beta0
[,1] [,2]
[1,] 0 0
[2,] 0 0
$U0
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0 0 0 0 0 0
[2,] 0 0 0 0 0 0
$Sigma0
[,1] [,2]
[1,] 1 0
[2,] 0 1
$d0
[1] NA
RprobitB_latent_class setting works
Code
RprobitB_latent_classes(list(C = 2))
Output
Latent classes
C = 2
Code
(out <- RprobitB_latent_classes(list(weight_update = TRUE, dp_update = TRUE)))
Output
Latent classes
DP-based update: TRUE
Weight-based update: TRUE
Initial classes: 1
Maximum classes: 10
Updating buffer: 100
Minimum class weight: 0.01
Maximum class weight: 0.99
Mimumum class distance: 0.1
Code
str(out)
Output
List of 9
$ weight_update: logi TRUE
$ dp_update : logi TRUE
$ C : num 1
$ Cmax : num 10
$ buffer : num 100
$ epsmin : num 0.01
$ epsmax : num 0.99
$ distmin : num 0.1
$ class_update : logi TRUE
- attr(*, "class")= chr "RprobitB_latent_classes"
building of RprobitB_normalization works
Code
RprobitB_normalization(level = "B", scale = "price := -1", form = form, re = re,
alternatives = alternatives, base = "B")
Output
Level: Utility differences with respect to alternative 'B'.
Scale: Coefficient of effect 'price' (alpha_1) fixed to -1.
Gibbs sampling works
Code
print(model)
Output
Probit model 'choice ~ a | b | c'.
Code
summary(model)
Output
Probit model
Formula: choice ~ a | b | c
R: 2000, B: 1000, Q: 1
Level: Utility differences with respect to alternative 'B'.
Scale: Coefficient of the 1. error term variance fixed to 1.
Gibbs sample statistics
true mean sd R^
alpha
1 -0.49 -0.50 0.04 1.00
2 -0.28 -0.31 0.04 1.00
3 0.14 0.15 0.04 1.00
4 0.85 0.88 0.06 1.00
5 -0.64 -0.52 0.05 1.01
Sigma
1,1 1.00 1.00 0.00 1.00
Code
print(coef(model))
Output
Estimate (sd)
1 a -0.50 (0.04)
2 b_A -0.31 (0.04)
3 ASC_A 0.15 (0.04)
4 c_A 0.88 (0.06)
5 c_B -0.52 (0.05)
Ordered probit model estimation works
Code
print(model)
Output
Probit model 'opinion_on_sth ~ age + gender'.
Code
summary(model)
Output
Probit model
Formula: opinion_on_sth ~ age + gender
R: 1000, B: 500, Q: 1
Level: Fixed first utility threshold to 0.
Scale: Error term variance fixed to 1.
Gibbs sample statistics
true mean sd R^
alpha
1 -0.95 -1.07 0.05 1.00
2 -0.55 -0.88 0.08 1.00
Sigma
1,1 1.00 1.00 0.00 NaN
d
1 0.91 0.45 0.03 1.87
2 0.20 -0.40 0.07 1.33
3 0.90 0.46 0.07 1.12
Code
print(coef(model))
Output
Estimate (sd)
1 age -1.07 (0.05)
2 gender -0.88 (0.08)
Ranked probit model estimation works
Code
print(model)
Output
Probit model 'product ~ price'.
Code
summary(model)
Output
Probit model
Formula: product ~ price
R: 1000, B: 500, Q: 1
Level: Utility differences with respect to alternative 'C'.
Scale: Coefficient of the 1. error term variance fixed to 1.
Gibbs sample statistics
true mean sd R^
alpha
1 -0.53 -0.48 0.03 1.02
2 -0.30 -0.28 0.04 1.05
3 0.15 0.16 0.03 1.00
Sigma
1,1 1.00 1.00 0.00 1.00
1,2 -0.24 -0.26 0.04 1.06
2,2 0.58 0.48 0.08 1.07
Code
print(coef(model))
Output
Estimate (sd)
1 price -0.48 (0.03)
2 ASC_A -0.28 (0.04)
3 ASC_B 0.16 (0.03)
computation of sufficient statistics works
Code
ss
Output
$N
[1] 2
$T
[1] 1 2
$J
[1] 3
$P_f
[1] 3
$P_r
[1] 2
$Tvec
[1] 1 2
$csTvec
[1] 0 1
$W
$W[[1]]
v1 ASC_A ASC_B
[1,] -1.113883 1 0
[2,] 1.107852 0 1
$W[[2]]
v1 ASC_A ASC_B
[1,] -0.5546814 1 0
[2,] -0.4088169 0 1
$W[[3]]
v1 ASC_A ASC_B
[1,] -1.41141 1 0
[2,] -1.39625 0 1
$X
$X[[1]]
v2_A v2_B
[1,] -0.3053884 0.0000000
[2,] 0.0000000 -0.3053884
$X[[2]]
v2_A v2_B
[1,] 1.511781 0.000000
[2,] 0.000000 1.511781
$X[[3]]
v2_A v2_B
[1,] 0.3898432 0.0000000
[2,] 0.0000000 0.3898432
$y
[,1] [,2]
[1,] 1 NA
[2,] 1 1
$WkW
[,1] [,2] [,3] [,4]
[1,] 3.540485 0.9634269 0.9634269 3.3439801
[2,] -3.079974 0.0000000 -0.6972149 0.0000000
[3,] 0.000000 -3.0799742 0.0000000 -0.6972149
[4,] -3.079974 -0.6972149 0.0000000 0.0000000
[5,] 3.000000 0.0000000 0.0000000 0.0000000
[6,] 0.000000 3.0000000 0.0000000 0.0000000
[7,] 0.000000 0.0000000 -3.0799742 -0.6972149
[8,] 0.000000 0.0000000 3.0000000 0.0000000
[9,] 0.000000 0.0000000 0.0000000 3.0000000
$XkX
$XkX[[1]]
[,1] [,2] [,3] [,4]
[1,] 0.09326207 0.00000000 0.00000000 0.00000000
[2,] 0.00000000 0.09326207 0.00000000 0.00000000
[3,] 0.00000000 0.00000000 0.09326207 0.00000000
[4,] 0.00000000 0.00000000 0.00000000 0.09326207
$XkX[[2]]
[,1] [,2] [,3] [,4]
[1,] 2.43746 0.00000 0.00000 0.00000
[2,] 0.00000 2.43746 0.00000 0.00000
[3,] 0.00000 0.00000 2.43746 0.00000
[4,] 0.00000 0.00000 0.00000 2.43746
$rdiff
[1] NA
loelschlaeger/RprobitB documentation built on March 1, 2024, 3:32 p.m.
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setting prior parameter works
Code
prior
Output
$eta
[1] 0
$Psi
[,1]
[1,] 1
$delta
[1] 1
$xi
[1] 0 0
$D
[,1] [,2]
[1,] 1 0
[2,] 0 1
$nu
[1] 4
$Theta
[,1] [,2]
[1,] 1 0
[2,] 0 1
$kappa
[1] 4
$E
[,1] [,2]
[1,] 1 0
[2,] 0 1
$zeta
[1] NA
$Z
[1] NA
attr(,"class")
[1] "RprobitB_prior" "list"
setting of initial Gibbs values works
Code
init
Output
$alpha0
[1] 0
$z0
[1] 1 1
$m0
[1] 1 1
$b0
[,1] [,2]
[1,] 0 0
[2,] 0 0
$Omega0
[,1] [,2]
[1,] 1 1
[2,] 0 0
[3,] 0 0
[4,] 1 1
$beta0
[,1] [,2]
[1,] 0 0
[2,] 0 0
$U0
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0 0 0 0 0 0
[2,] 0 0 0 0 0 0
$Sigma0
[,1] [,2]
[1,] 1 0
[2,] 0 1
$d0
[1] NA
RprobitB_latent_class setting works
Code
RprobitB_latent_classes(list(C = 2))
Output
Latent classes
C = 2
Code
(out <- RprobitB_latent_classes(list(weight_update = TRUE, dp_update = TRUE)))
Output
Latent classes
DP-based update: TRUE
Weight-based update: TRUE
Initial classes: 1
Maximum classes: 10
Updating buffer: 100
Minimum class weight: 0.01
Maximum class weight: 0.99
Mimumum class distance: 0.1
Code
str(out)
Output
List of 9
$ weight_update: logi TRUE
$ dp_update : logi TRUE
$ C : num 1
$ Cmax : num 10
$ buffer : num 100
$ epsmin : num 0.01
$ epsmax : num 0.99
$ distmin : num 0.1
$ class_update : logi TRUE
- attr(*, "class")= chr "RprobitB_latent_classes"
building of RprobitB_normalization works
Code
RprobitB_normalization(level = "B", scale = "price := -1", form = form, re = re,
alternatives = alternatives, base = "B")
Output
Level: Utility differences with respect to alternative 'B'.
Scale: Coefficient of effect 'price' (alpha_1) fixed to -1.
Gibbs sampling works
Code
print(model)
Output
Probit model 'choice ~ a | b | c'.
Code
summary(model)
Output
Probit model
Formula: choice ~ a | b | c
R: 2000, B: 1000, Q: 1
Level: Utility differences with respect to alternative 'B'.
Scale: Coefficient of the 1. error term variance fixed to 1.
Gibbs sample statistics
true mean sd R^
alpha
1 -0.49 -0.50 0.04 1.00
2 -0.28 -0.31 0.04 1.00
3 0.14 0.15 0.04 1.00
4 0.85 0.88 0.06 1.00
5 -0.64 -0.52 0.05 1.01
Sigma
1,1 1.00 1.00 0.00 1.00
Code
print(coef(model))
Output
Estimate (sd)
1 a -0.50 (0.04)
2 b_A -0.31 (0.04)
3 ASC_A 0.15 (0.04)
4 c_A 0.88 (0.06)
5 c_B -0.52 (0.05)
Ordered probit model estimation works
Code
print(model)
Output
Probit model 'opinion_on_sth ~ age + gender'.
Code
summary(model)
Output
Probit model
Formula: opinion_on_sth ~ age + gender
R: 1000, B: 500, Q: 1
Level: Fixed first utility threshold to 0.
Scale: Error term variance fixed to 1.
Gibbs sample statistics
true mean sd R^
alpha
1 -0.95 -1.07 0.05 1.00
2 -0.55 -0.88 0.08 1.00
Sigma
1,1 1.00 1.00 0.00 NaN
d
1 0.91 0.45 0.03 1.87
2 0.20 -0.40 0.07 1.33
3 0.90 0.46 0.07 1.12
Code
print(coef(model))
Output
Estimate (sd)
1 age -1.07 (0.05)
2 gender -0.88 (0.08)
Ranked probit model estimation works
Code
print(model)
Output
Probit model 'product ~ price'.
Code
summary(model)
Output
Probit model
Formula: product ~ price
R: 1000, B: 500, Q: 1
Level: Utility differences with respect to alternative 'C'.
Scale: Coefficient of the 1. error term variance fixed to 1.
Gibbs sample statistics
true mean sd R^
alpha
1 -0.53 -0.48 0.03 1.02
2 -0.30 -0.28 0.04 1.05
3 0.15 0.16 0.03 1.00
Sigma
1,1 1.00 1.00 0.00 1.00
1,2 -0.24 -0.26 0.04 1.06
2,2 0.58 0.48 0.08 1.07
Code
print(coef(model))
Output
Estimate (sd)
1 price -0.48 (0.03)
2 ASC_A -0.28 (0.04)
3 ASC_B 0.16 (0.03)
computation of sufficient statistics works
Code
ss
Output
$N
[1] 2
$T
[1] 1 2
$J
[1] 3
$P_f
[1] 3
$P_r
[1] 2
$Tvec
[1] 1 2
$csTvec
[1] 0 1
$W
$W[[1]]
v1 ASC_A ASC_B
[1,] -1.113883 1 0
[2,] 1.107852 0 1
$W[[2]]
v1 ASC_A ASC_B
[1,] -0.5546814 1 0
[2,] -0.4088169 0 1
$W[[3]]
v1 ASC_A ASC_B
[1,] -1.41141 1 0
[2,] -1.39625 0 1
$X
$X[[1]]
v2_A v2_B
[1,] -0.3053884 0.0000000
[2,] 0.0000000 -0.3053884
$X[[2]]
v2_A v2_B
[1,] 1.511781 0.000000
[2,] 0.000000 1.511781
$X[[3]]
v2_A v2_B
[1,] 0.3898432 0.0000000
[2,] 0.0000000 0.3898432
$y
[,1] [,2]
[1,] 1 NA
[2,] 1 1
$WkW
[,1] [,2] [,3] [,4]
[1,] 3.540485 0.9634269 0.9634269 3.3439801
[2,] -3.079974 0.0000000 -0.6972149 0.0000000
[3,] 0.000000 -3.0799742 0.0000000 -0.6972149
[4,] -3.079974 -0.6972149 0.0000000 0.0000000
[5,] 3.000000 0.0000000 0.0000000 0.0000000
[6,] 0.000000 3.0000000 0.0000000 0.0000000
[7,] 0.000000 0.0000000 -3.0799742 -0.6972149
[8,] 0.000000 0.0000000 3.0000000 0.0000000
[9,] 0.000000 0.0000000 0.0000000 3.0000000
$XkX
$XkX[[1]]
[,1] [,2] [,3] [,4]
[1,] 0.09326207 0.00000000 0.00000000 0.00000000
[2,] 0.00000000 0.09326207 0.00000000 0.00000000
[3,] 0.00000000 0.00000000 0.09326207 0.00000000
[4,] 0.00000000 0.00000000 0.00000000 0.09326207
$XkX[[2]]
[,1] [,2] [,3] [,4]
[1,] 2.43746 0.00000 0.00000 0.00000
[2,] 0.00000 2.43746 0.00000 0.00000
[3,] 0.00000 0.00000 2.43746 0.00000
[4,] 0.00000 0.00000 0.00000 2.43746
$rdiff
[1] NA
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