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Computes posterior samples for the posterior predictive distribution.

Source: R/predict.R
predict.tridim_transformation.Rd

Predicted values based on the bi/tridimensional regression model object.

Usage

# S3 method for tridim_transformation
predict(object, newdata = NULL, summary = TRUE, probs = NULL, ...)

Arguments

object

An object of class tridim_transformation

newdata

An optional two column data frame with independent variables. If omitted, the fitted values are used.

summary

Whether summary statistics should be returned instead of raw sample values. Defaults to TRUE

probs

The percentiles used to compute summary, defaults to NULL (no CI).

...

Unused

Value

If summary=FALSE, a numeric matrix iterationsN x observationsN x variablesN. If summary=TRUE, a data.frame with columns "dvindex" with mean for each dependent variable plus optional quantiles columns with names "dvindex_quantile".

See also

fit_transformation

Examples

euc2 <- fit_transformation(depV1+depV2~indepV1+indepV2,
  NakayaData, transformation = 'euclidean')
#> 
#> SAMPLING FOR MODEL 'tridim_transformation' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.097 seconds (Warm-up)
#> Chain 1:                0.091 seconds (Sampling)
#> Chain 1:                0.188 seconds (Total)
#> Chain 1: 

# prediction summary
predictions <- predict(euc2)

# full posterior prediction samples
predictions <- predict(euc2, summary=FALSE)