fitted.brmcoda {multilevelcoda} | R Documentation |
Expected Values of the Posterior Predictive Distribution
Description
Compute posterior draws of the expected value of the posterior predictive
distribution of a brmsfit
model in the brmcoda
object.
Can be performed for the data used to fit the model (posterior
predictive checks) or for new data. By definition, these predictions have
smaller variance than the posterior predictions performed by the
predict.brmcoda
method. This is because only the
uncertainty in the expected value of the posterior predictive distribution is
incorporated in the draws computed by fitted
while the
residual error is ignored there. However, the estimated means of both methods
averaged across draws should be very similar.
Usage
## S3 method for class 'brmcoda'
fitted(object, scale = c("linear", "response"), summary = TRUE, ...)
Arguments
object |
An object of class |
scale |
Specifically for models with compositional responses,
either |
summary |
Should summary statistics be returned
instead of the raw values? Default is |
... |
Further arguments passed to |
Value
An array
of predicted mean response values.
If summary = FALSE
the output resembles those of
posterior_epred.brmsfit
.
If summary = TRUE
the output depends on the family: For categorical
and ordinal families, the output is an N x E x C array, where N is the
number of observations, E is the number of summary statistics, and C is the
number of categories. For all other families, the output is an N x E
matrix. The number of summary statistics E is equal to 2 +
length(probs)
: The Estimate
column contains point estimates (either
mean or median depending on argument robust
), while the
Est.Error
column contains uncertainty estimates (either standard
deviation or median absolute deviation depending on argument
robust
). The remaining columns starting with Q
contain
quantile estimates as specified via argument probs
.
In multivariate models, an additional dimension is added to the output which indexes along the different response variables.
See Also
Examples
## fit a model
if(requireNamespace("cmdstanr")){
## compute composition and ilr coordinates
cilr <- complr(data = mcompd, sbp = sbp,
parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
idvar = "ID", total = 1440)
## fit a model
m1 <- brmcoda(complr = cilr,
formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
chain = 1, iter = 500,
backend = "cmdstanr")
## compute expected predictions
epred <- fitted(m1)
head(epred)
## fit a model with compositional outcome
m2 <- brmcoda(complr = cilr,
formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
chain = 1, iter = 500,
backend = "cmdstanr")
## expected predictions on compositional scale
epredcomp <- fitted(m2, scale = "response")
head(epredcomp)
}