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Predict method for discharge rating curves

Source: R/plm_methods.R
predict.plm0.Rd

Predict the discharge for given stage values based on a discharge rating curve model object.

Usage

# S3 method for class 'plm0'
predict(object, newdata = NULL, wide = FALSE, ...)

# S3 method for class 'plm'
predict(object, newdata = NULL, wide = FALSE, ...)

# S3 method for class 'gplm0'
predict(object, newdata = NULL, wide = FALSE, ...)

# S3 method for class 'gplm'
predict(object, newdata = NULL, wide = FALSE, ...)

Arguments

object

An object of class "plm0", "plm", "gplm0" or "gplm".

newdata

A numeric vector of stage values for which to predict. If omitted, the stage values in the data are used.

wide

A logical value denoting whether to produce a wide prediction output. If TRUE, then the output is a table with median prediction values for an equally spaced grid of stages with 1 cm increments, each row containing predictions in a decimeter range of stages.

...

Not used in this function

Value

An object of class "data.frame" with four columns:

h

The stage.

lower

The 2.5% posterior predictive quantile.

median

The 50% posterior predictive quantile.

upper

The 97.5% posterior predictive quantile.

If wide=TRUE, a matrix as described above (see wide parameter) is returned.

Functions

  • predict(plm0): Predict method for plm0

  • predict(plm): Predict method for plm

  • predict(gplm0): Predict method for gplm0

  • predict(gplm): Predict method for gplm

See also

plm0, plm, gplm0 and gplm for fitting a discharge rating curve and summary.plm0, summary.plm, summary.gplm0 and summary.gplm for summaries. It is also useful to look at plot.plm0, plot.plm, plot.gplm0 and plot.gplm to help visualize all aspects of the fitted discharge rating curve. Additionally, spread_draws and spread_draws help working directly with the MCMC samples.

Examples

# \donttest{
data(krokfors)
set.seed(1)
plm0.fit <- plm0(formula=Q~W,data=krokfors,h_max=10,num_cores=2)
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#> 
#> MCMC sampling completed!
#> 
#> Diagnostics:
#> Acceptance rate: 36.22%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
#predict rating curve on a equally 10 cm spaced grid from 9 to 10 meters
predict(plm0.fit,newdata=seq(9,10,by=0.1))
#>       h     lower    median     upper
#> 1   9.0  2.423761  3.617127  5.355104
#> 2   9.1  2.988084  4.448245  6.528722
#> 3   9.2  3.616549  5.374700  7.940233
#> 4   9.3  4.291666  6.418404  9.515431
#> 5   9.4  5.056743  7.601828 11.323903
#> 6   9.5  5.914344  8.919305 13.169163
#> 7   9.6  6.893257 10.380845 15.570683
#> 8   9.7  7.860588 11.974804 17.826516
#> 9   9.8  9.052186 13.709759 20.592950
#> 10  9.9 10.158058 15.660659 23.351463
#> 11 10.0 11.489777 17.692248 26.874426
# }