simulate
Simulate Responses
Simulate one or more responses from the distribution corresponding to a fitted model object.
Usage
simulate(object, nsim = 1, seed = NULL, ...)
Arguments
- object
- an object representing a fitted model.
- nsim
- number of response vectors to simulate. Defaults to
1
. - seed
- an object specifying if and how the random number
generator should be initialized (
seeded ). For the "lm" method, eitherNULL
or an integer that will be used in a call toset.seed
before simulating the response vectors. If set, the value is saved as the"seed"
attribute of the returned value. The default,NULL
will not change the random generator state, and return.Random.seed
as the"seed"
attribute, seeValue . - ...
- additional optional arguments.
Details
This is a generic function. Consult the individual modeling functions for details on how to use this function.
Package "lm"
objects which is used
for lm
and glm
fits. There is a method
for fits from glm.nb
in package "lm"
method.
The methods for linear models fitted by lm
or glm(family
= "gaussian")
assume that any weights which have been supplied are
inversely proportional to the error variance. For other GLMs the
(optional) simulate
component of the family
object is used---there is no appropriate simulation method for
For binomial and Poisson GLMs the dispersion is fixed at one. Integer prior weights $w_i$ can be interpreted as meaning that observation $i$ is an average of $w_i$ observations, which is natural for binomials specified as proportions but less so for a Poisson, for which prior weights are ignored with a warning.
For a gamma GLM the shape parameter is estimated by maximum likelihood
(using function gamma.shape
in package
For an inverse gaussian GLM the model assumed is
$IG(\mu_i, \lambda w_i)$ (see
rinvGauss
from the
Value
- Typically, a list of length
nsim
of simulated responses. Where appropriate the result can be a data frame (which is a special type of list). For the"lm"
method, the result is a data frame with an attribute"seed"
. If argumentseed
isNULL
, the attribute is the value of.Random.seed
before the simulation was started; otherwise it is the value of the argument with a"kind"
attribute with valueas.list(RNGkind())
.
See Also
RNG
about random number generation in R,
fitted.values
and residuals
for related methods;
glm
, lm
for model fitting.
There are further examples in the
Examples
library(stats)
x <- 1:5
mod1 <- lm(c(1:3, 7, 6) ~ x)
S1 <- simulate(mod1, nsim = 4)
## repeat the simulation:
.Random.seed <- attr(S1, "seed")
identical(S1, simulate(mod1, nsim = 4))
S2 <- simulate(mod1, nsim = 200, seed = 101)
rowMeans(S2) # should be about the same as
fitted(mod1)
## repeat identically:
(sseed <- attr(S2, "seed")) # seed; RNGkind as attribute
stopifnot(identical(S2, simulate(mod1, nsim = 200, seed = sseed)))
## To be sure about the proper RNGkind, e.g., after
RNGversion("2.7.0")
## first set the RNG kind, then simulate
do.call(RNGkind, attr(sseed, "kind"))
identical(S2, simulate(mod1, nsim = 200, seed = sseed))
## Binomial GLM examples
yb1 <- matrix(c(4, 4, 5, 7, 8, 6, 6, 5, 3, 2), ncol = 2)
modb1 <- glm(yb1 ~ x, family = binomial)
S3 <- simulate(modb1, nsim = 4)
# each column of S3 is a two-column matrix.
x2 <- sort(runif(100))
yb2 <- rbinom(100, prob = plogis(2*(x2-1)), size = 1)
yb2 <- factor(1 + yb2, labels = c("failure", "success"))
modb2 <- glm(yb2 ~ x2, family = binomial)
S4 <- simulate(modb2, nsim = 4)
# each column of S4 is a factor