Description Usage Arguments Details Value See Also
Makes instantaneous predictions (at the temporal resolution of
newdata
) from a fitted loadInterp
model. See
predictSolute
for details.
1 2 3 4 5 6 7 | ## S3 method for class 'loadInterp'
predictSolute(load.model, flux.or.conc, newdata,
interval = c("none", "confidence", "prediction"), level = 0.95,
lin.or.log = c("linear", "log"), se.fit = FALSE, se.pred = FALSE,
date = FALSE, attach.units = FALSE, agg.by = c("unit", "day", "month",
"water year", "calendar year", "total", "mean water year",
"mean calendar year", "[custom]"), ...)
|
load.model |
A loadInterp object. |
flux.or.conc |
character. Should the predictions be reported as flux rates or concentrations? |
newdata |
|
interval |
character. The type of interval desired. Confidence intervals are not currently available for loadInterp models. |
level |
numeric. Fraction of density distribution to include within confidence or prediction interval |
lin.or.log |
character. Either "linear" or "log" to say whether the
predictions should be converted to log space or not. If converted to log
space, a bias correction will be applied, see |
se.fit |
logical, but should be FALSE because se.fit is not currently available for loadInterp models. |
se.pred |
logical. If TRUE, the output data.frame will include a column named "se.pred" describing the standard error of the prediction for each row of predictors. The values in the se.pred column will be larger than those in the se.fit column, because the se.pred values are standard errors of prediction (SEPs) and take into account not only the parameter uncertainty associated with the model coefficients (also covered by se.fit), but also the random error associated with any given observation (the epsilon term in a typical regression model). |
date |
logical. If TRUE, the output data.frame will include a column named "date" containing the dates of the predictions. |
attach.units |
logical. Should the units be attached to columns in the resulting data.frame? |
agg.by |
character Time period to aggregate results by. |
... |
Additional arguments passed to class-specific implementations of
the |
loadInterps are currently assumed to have normally distributed residuals. An unwitting user might violate this assumption without being caught by the code, so be careful! This assumption is mainly relevant to the calculation of confidence or prediction intervals. Also, where other models such as loadReg and loadLm will retransform predictions back into linear space, loadInterps will not.
A vector of data.frame of predictions, as for the generic
predictSolute
.
Other predictSolute: predictSolute.loadComp
,
predictSolute.loadLm
,
predictSolute.loadModel
,
predictSolute.loadReg2
,
predictSolute
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.