Simulating responses from baseline-category and conditional logit models
simulate.RdThe simulate() methods allow to simulate responses from models
fitted with mclogit() and mblogit(). Currently only
models without random effects are supported for this.
Usage
# S3 method for class 'mblogit'
simulate(object, nsim = 1, seed = NULL, ...)
# S3 method for class 'mclogit'
simulate(object, nsim = 1, seed = NULL, ...)
# These methods are currently just 'stubs', causing an error
# message stating that simulation from models with random
# effects are not supported yet
# S3 method for class 'mmblogit'
simulate(object, nsim = 1, seed = NULL, ...)
# S3 method for class 'mmclogit'
simulate(object, nsim = 1, seed = NULL, ...)Arguments
- object
an object from the relevant class
- nsim
a number, specifying the number of simulated responses for each observation.
- seed
an object specifying if and how the random number generator should be initialized ('seeded'). The interpetation of this argument follows the default method, see
link[stats]{simulate}- ...
other arguments, ignored.
Value
The result of the simulate method for objects
created by mclogit is a data frame with one variable for
each requested simulation run (their number is given by the
nsim= argument). The contents of the columns are counts (or
zero-one values), with group-wise multinomial distribution (within
choice sets) just like it is assumed for the original response.
The shape of the result of the simulate method
for objects created by mblogit is also a data frame.
The variables within the data frame have a mode or shape that
corresponds to the response to which the model was fitted. If the
response is a matrix of counts, then the variables in the data frame
are also matrices of counts. If the response is a factor and
mblogit was called with an argument
from.table=FALSE, the variables in the data frame are factors
with the same factor levels as the response to which the model was
fitted. If instead the function was called with
from.table=TRUE, the variables in the data frame are counts,
which represent frequency weights that would result from applying
as.data.frame to a contingency table of simulated
frequency counts.
Examples
library(MASS)
(house.mblogit <- mblogit(Sat ~ Infl + Type + Cont,
data = housing,
weights=Freq,
from.table=TRUE))
#>
#> Iteration 1 - deviance = 38.84842 - criterion = 0.992521
#> Iteration 2 - deviance = 38.66222 - criterion = 0.004803721
#> Iteration 3 - deviance = 38.6622 - criterion = 3.782555e-07
#> Iteration 4 - deviance = 38.6622 - criterion = 3.666163e-15
#> converged
#>
#> Call: mblogit(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq,
#> from.table = TRUE)
#>
#> Coefficients:
#> Predictors
#> Logit eqn. (Intercept) InflMedium InflHigh TypeApartment TypeAtrium
#> Medium/Low -0.4192 0.4464 0.6649 -0.4357 0.1314
#> High/Low -0.1387 0.7349 1.6126 -0.7356 -0.4080
#> Predictors
#> Logit eqn. TypeTerrace ContHigh
#> Medium/Low -0.6666 0.3609
#> High/Low -1.4123 0.4818
#>
#> Null Deviance: 262.1
#> Residual Deviance: 38.66
sm <- simulate(house.mblogit,nsim=7)
housing.long <- housing[rep(seq.int(nrow(housing)),housing$Freq),]
(housel.mblogit <- mblogit(Sat ~ Infl + Type + Cont,
data=housing.long))
#>
#> Iteration 1 - deviance = 3474.691 - criterion = 0.5057269
#> Iteration 2 - deviance = 3470.086 - criterion = 0.001326883
#> Iteration 3 - deviance = 3470.084 - criterion = 7.526912e-07
#> Iteration 4 - deviance = 3470.084 - criterion = 1.308345e-12
#> converged
#>
#> Call: mblogit(formula = Sat ~ Infl + Type + Cont, data = housing.long)
#>
#> Coefficients:
#> Predictors
#> Logit eqn. (Intercept) InflMedium InflHigh TypeApartment TypeAtrium
#> Medium/Low -0.4192 0.4464 0.6649 -0.4357 0.1314
#> High/Low -0.1387 0.7349 1.6126 -0.7356 -0.4080
#> Predictors
#> Logit eqn. TypeTerrace ContHigh
#> Medium/Low -0.6666 0.3609
#> High/Low -1.4123 0.4818
#>
#> Null Deviance: 3694
#> Residual Deviance: 3470
sml <- simulate(housel.mblogit,nsim=7)
housing.table <- xtabs(Freq~.,data=housing)
housing.mat <- memisc::to.data.frame(housing.table)
head(housing.mat)
#> Infl Type Cont Low Medium High
#> 1 Low Tower Low 21 21 28
#> 2 Medium Tower Low 34 22 36
#> 3 High Tower Low 10 11 36
#> 4 Low Apartment Low 61 23 17
#> 5 Medium Apartment Low 43 35 40
#> 6 High Apartment Low 26 18 54
(housem.mblogit <- mblogit(cbind(Low,Medium,High) ~
Infl + Type + Cont,
data=housing.mat))
#>
#> Iteration 1 - deviance = 38.84842 - criterion = 0.992521
#> Iteration 2 - deviance = 38.66222 - criterion = 0.004803721
#> Iteration 3 - deviance = 38.6622 - criterion = 3.782555e-07
#> Iteration 4 - deviance = 38.6622 - criterion = 3.666163e-15
#> converged
#>
#> Call: mblogit(formula = cbind(Low, Medium, High) ~ Infl + Type + Cont,
#> data = housing.mat)
#>
#> Coefficients:
#> Predictors
#> Logit eqn. (Intercept) InflMedium InflHigh TypeApartment TypeAtrium
#> Medium/Low -0.4192 0.4464 0.6649 -0.4357 0.1314
#> High/Low -0.1387 0.7349 1.6126 -0.7356 -0.4080
#> Predictors
#> Logit eqn. TypeTerrace ContHigh
#> Medium/Low -0.6666 0.3609
#> High/Low -1.4123 0.4818
#>
#> Null Deviance: 262.1
#> Residual Deviance: 38.66
smm <- simulate(housem.mblogit,nsim=7)
str(sm)
#> 'data.frame': 72 obs. of 7 variables:
#> $ sim_1: int 30 19 21 24 24 44 10 9 38 55 ...
#> $ sim_2: int 33 19 18 23 18 51 7 6 44 54 ...
#> $ sim_3: int 31 16 23 25 26 41 7 13 37 46 ...
#> $ sim_4: int 28 14 28 22 32 38 4 7 46 56 ...
#> $ sim_5: int 32 22 16 27 17 48 8 11 38 58 ...
#> $ sim_6: int 28 23 19 33 18 41 9 13 35 57 ...
#> $ sim_7: int 23 18 29 17 24 51 5 11 41 59 ...
#> - attr(*, "seed")= int [1:626] 10403 205 1980538363 -125047968 -820381145 -1073960099 -33499050 1164085917 1218464490 -1045685882 ...
str(sml)
#> 'data.frame': 1681 obs. of 7 variables:
#> $ sim_1: Factor w/ 3 levels "Low","Medium",..: 2 3 3 3 3 1 3 1 1 1 ...
#> $ sim_2: Factor w/ 3 levels "Low","Medium",..: 1 3 1 1 1 2 1 1 1 2 ...
#> $ sim_3: Factor w/ 3 levels "Low","Medium",..: 1 1 2 1 3 2 3 2 3 1 ...
#> $ sim_4: Factor w/ 3 levels "Low","Medium",..: 1 2 2 2 3 2 3 3 1 3 ...
#> $ sim_5: Factor w/ 3 levels "Low","Medium",..: 1 3 1 2 1 1 3 3 1 2 ...
#> $ sim_6: Factor w/ 3 levels "Low","Medium",..: 2 2 1 3 3 3 3 1 1 3 ...
#> $ sim_7: Factor w/ 3 levels "Low","Medium",..: 2 1 3 1 1 3 2 2 2 3 ...
#> - attr(*, "seed")= int [1:626] 10403 19 1492790388 -1629574902 -1817687082 -2080485428 -622102478 -1390801052 -1280540579 249411485 ...
str(smm)
#> 'data.frame': 24 obs. of 7 variables:
#> $ sim_1: int [1:24, 1:3] 26 23 2 53 45 20 15 5 3 22 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ sim_2: int [1:24, 1:3] 30 24 10 55 41 25 19 9 3 17 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ sim_3: int [1:24, 1:3] 23 25 10 54 46 32 13 8 7 22 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ sim_4: int [1:24, 1:3] 22 30 9 52 46 32 13 9 3 15 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ sim_5: int [1:24, 1:3] 22 23 3 57 45 27 8 11 4 21 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ sim_6: int [1:24, 1:3] 24 22 8 49 36 29 14 10 5 19 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ sim_7: int [1:24, 1:3] 35 25 16 53 43 23 18 9 5 20 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> - attr(*, "seed")= int [1:626] 10403 554 1019603289 -1305277535 -470137223 -57077173 1775921955 -181041711 861366705 -1087987206 ...
head(smm[[1]])
#> Low Medium High
#> [1,] 26 17 27
#> [2,] 23 28 41
#> [3,] 2 7 48
#> [4,] 53 20 28
#> [5,] 45 36 37
#> [6,] 20 22 56