Conditionally simulate from a model

Conditionally simulate prediction data from a model object.

conditional(object, ...)

# S3 method for class 'splm'
conditional(
  object,
  newdata,
  output = "newdata",
  samples = 1000,
  simulate_covparams = FALSE,
  ...
)

# S3 method for class 'spglm'
conditional(
  object,
  newdata,
  output = "newdata",
  type = c("link", "response", "new"),
  samples = 1000,
  newdata_size,
  ...
)

Arguments

object

A fitted model object.

...

Other arguments. Not used (needed for generic consistency).

newdata

A data frame or sf object in which to look for variables with which to predict. If a data frame, newdata must contain all variables used by formula(object) and all variables representing coordinates. If an sf object, newdata must contain all variables used by formula(object) and coordinates are obtained from the geometry of newdata. If omitted, missing data from the fitted model object are used.

output

The output type, which can be any subset of c("newdata", "beta", "object"). If simulate_covparams = TRUE, the output type can also be any subset of c("cov", "spcov", "randcov"). The default is "newdata". See Details for more.

samples

The number of conditional simulations. The default is 1,000.

simulate_covparams

For splm() model objects, whether to also simulate new covariance parameters for each sample. simulate_covparams requires object$vcov$cov to be specified during model fitting by selecting ddf = "satterthwaite". simulate_covparams = TRUE should generally not be used for sample sizes greater than 500 given its computational inefficiencies. The default is FALSE.

type

For spglm() model objects, the scale of the conditional simulations for newdata. When type = "link", the predicted means on the link scale are returned. When type = "response", the predicted means on the response scale are returned. When type = "new", a new observation is simulated from the appropriate response distribution with mean equal to the mean on the response scale and dispersion equal to the dispersion parameter from object. The default is "link".

newdata_size

The size value for each observation in newdata used when predicting for the binomial family, with a default value of 1.

Value

If output = "newdata", an a x b matrix of conditional simulations for each row in newdata, where a is the number of rows in newdata and b is the number of samples. If output = "beta", an p x b matrix of conditional simulations for each element in coef(object), where p is the number of fixed effects and b is the number of samples. If output = "object", an n x b matrix of observed data values, where n is the number of rows in data and b is the number of samples. If output = "cov"/"spcov"/"randcov" (simulate_covparams = TRUE only), a (covariance parameter) x b matrix of the of conditoinal simulations for each covariance parameter, where b is the number of samples. "cov" returns every covariance parameter, while "spcov"/"randcov" return just the spatial/random-effect covariance parameters, respectively.

If output has more than one element, a list is returned with the respetive elements named according to the relevant output. For example output = c("newdata", "beta") returns a list with elements "newdata" and "beta", each containing the relevant output for output = "newdata" and output = "beta", respectively.

Details

If "newdata" is in output, conditional simulations are returned for each row of newdata. If "beta" is in output, conditional simulations are returned for each fixed effect (i.e., element of coef(object). If "object" is in output, the observed data from object is returned once for each row of newdata. For example, c("newdata", "beta") returns the conditional simulations both for newdata and for the fixed effects. If "cov"/"spcov"/"randcov" is in output (only available when simulate_covparams = TRUE), the simulated covariance parameter draws themselves are returned.

Examples

set.seed(0)
spmod <- splm(sulfate ~ 1, data = sulfate, spcov_type = "exponential")
cond <- conditional(spmod, newdata = sulfate_preds)
predict(spmod, sulfate_preds[20, ], se.fit = TRUE)
#> $fit
#>        1 
#> 23.61902 
#> 
#> $se.fit
#>        1 
#> 3.866808 
#> 
c("fit_cond" = mean(cond[20, ]), "se.fit_cond" = sd(cond[20, ]))
#>    fit_cond se.fit_cond 
#>   23.683964    3.907746 
hist(cond[20, ])