Simulate values from fitted models — get

Simulate responses from fitted statistical models.

Usage

get_simulated(x, ...)

# S3 method for class 'lm'
get_simulated(
  x,
  data = NULL,
  iterations = 1,
  include_data = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'glmmTMB'
get_simulated(
  x,
  data = NULL,
  iterations = 1,
  include_data = FALSE,
  seed = NULL,
  centrality = NULL,
  re.form = NULL,
  ...
)

# S3 method for class 'merMod'
get_simulated(
  x,
  data = NULL,
  iterations = 1,
  include_data = FALSE,
  seed = NULL,
  use.u = FALSE,
  re.form = NA,
  newparams = NULL,
  family = NULL,
  cluster.rand = stats::rnorm,
  allow.new.levels = FALSE,
  na.action = stats::na.pass,
  ...
)

# Default S3 method
get_simulated(x, ...)

# S3 method for class 'data.frame'
get_simulated(x, data = NULL, include_data = FALSE, ...)

Arguments

x: A model.
...: Additional arguments passed to the underlying prediction or simulation methods.
data: An optional data frame in which to evaluate predictions before simulation. This can be a data grid created with get_datagrid().
iterations: Number of response vectors to simulate.
include_data: Logical, if TRUE, data is returned alongside with the simulated draws. If data = NULL, the data used to fit the model is included.
seed: An optional integer random seed.
centrality: Function, indicating how to summarize aggregated simulated values when data is a data grid. Only applies to models from package glmmTMB, because a special handling for the data argument is required here. simulate.glmmTMB() always returns simulated draws for the full data that was used to fit the model. If a data grid is passed via data, get_simulated.glmmTMB() internally, first, filters the results using datawizard::data_match() with the model data and data. This may return more than one row of simulated draws per group defined in data, thus, in a second step, the filtered data are aggregated by the groups defined in data. Resulting estimates of simulated values are summarized using centrylity, which, by default is the mode for categorical, ordinal or count response variables, or the mean otherwise.
re.form: For glmmTMB and merMod models, random effects formula passed to simulation (NULL, NA or ~0).
use.u: For merMod models, logical indicating whether to condition on the current conditional modes of the random effects when simulating.
newparams: For merMod models, optional list or vector of alternative parameter values (e.g., fixed- and random-effect parameters) to be used for simulation instead of those from the fitted model.
family: For merMod models, optional family object or function to override the model's family when simulating responses.
cluster.rand: For merMod models, function used to generate random draws for the random effects (e.g., rnorm).
allow.new.levels: For merMod models, logical indicating whether new levels of grouping variables are allowed in data when simulating.
na.action: For merMod models, function specifying how to handle missing values in data before simulation (e.g., "na.pass").

Value

A data frame with one column per simulation (iter_1, iter_2, ...). The attribute seed contains information about the RNG state used.

Examples

data(mtcars)
m <- lm(mpg ~ wt + cyl, data = mtcars)

# Simulations on the original data
get_simulated(m, iterations = 2, seed = 123)
#>                        iter_1    iter_2
#> Mazda RX4           20.840115 24.577394
#> Mazda RX4 Wag       20.874462 23.720068
#> Datsun 710          30.254034 28.361449
#> Hornet 4 Drive      20.561548 22.148611
#> Hornet Sportabout   16.978906 18.069150
#> Valiant             24.002184 19.439769
#> Duster 360          17.415541 15.446567
#> Merc 240D           20.227816 22.499015
#> Merc 230            21.840014 21.819848
#> Merc 280            18.518303 19.128717
#> Merc 280C           22.805397 16.413623
#> Merc 450SE          15.560473 20.205474
#> Merc 450SL          16.750563 18.823037
#> Merc 450SLC         15.846207 12.678428
#> Cadillac Fleetwood   9.444167  9.836885
#> Lincoln Continental 14.903996  9.117924
#> Chrysler Imperial   11.846395 12.570728
#> Fiat 128            21.585623 26.420892
#> Honda Civic         30.302404 29.152061
#> Toyota Corolla      26.585748 27.726353
#> Toyota Corona       23.047681 25.679265
#> Dodge Challenger    15.832026 19.905588
#> AMC Javelin         14.028630 16.083242
#> Camaro Z28          13.499129 19.264131
#> Pontiac Firebird    13.749816 11.378167
#> Fiat X1-9           23.149939 28.981555
#> Porsche 914-2       28.977433 27.144399
#> Lotus Europa        29.220929 29.381574
#> Ford Pantera L      14.586336 18.483250
#> Ferrari Dino        25.019688 20.510776
#> Maserati Bora       17.327085 15.376616
#> Volvo 142E          24.026578 22.168971

# Simulations on a data grid
dg <- get_datagrid(m, wt = c(2, 3), cyl = c(4, 6))
get_simulated(dg, m, iterations = 2, seed = 123)
#>       iter_1   iter_2
#> 1  27.388111 29.92209
#> 2  26.848084 26.68147
#> 3  30.056194 28.35244
#> 4  24.847144 26.92073
#> 5  23.613179 25.39065
#> 6  26.296615 23.66125
#> 7  21.691686 21.93047
#> 8  15.872139 18.96124
#> 9  15.971816 16.94976
#> 10 15.203005 15.37038
#> 11 28.954400 24.02788
#> 12 25.347306 23.88965
#> 13 24.067583 19.78967
#> 14 21.934703 27.21935
#> 15 18.838510 23.36710
#> 16 23.465496 15.99397
#> 17 18.770925 16.45827
#> 18 11.055293 14.90647
#> 19 16.520474 16.72230
#> 20 12.117759 13.11761
#> 21 20.054307 23.44636
#> 22 20.848234 21.33459
#> 23 17.388724 19.91294
#> 24 16.763494 22.14884
#> 25 15.645253 16.67038
#> 26 11.531367 19.75554
#> 27 16.628128 10.50065
#> 28 13.482812 14.59003
#> 29  8.781957 12.02214
#> 30 13.535258 10.87050