predict.evgam | R Documentation |
evgam
objectPredictions from a fitted evgam
object
## S3 method for class 'evgam' predict( object, newdata, type = "link", prob = NULL, se.fit = FALSE, marginal = TRUE, exi = FALSE, trace = 0, ... )
object |
a fitted |
newdata |
a data frame |
type |
a character string giving the type of prediction sought; see Details. Defaults to |
prob |
a scalar or vector of probabilities for quantiles to be estimated if |
se.fit |
a logical: should estimated standard errors be returned? Defaults to |
marginal |
a logical: should uncertainty estimates integrate out smoothing parameter uncertainty? Defaults to |
exi |
a logical: if a dependent GEV is fitted should the independent parameters be returned? Defaults to |
trace |
an integer where higher values give more output. -1 suppresses everything. Defaults to 0 |
... |
unused |
There are five options for type
: 1) "link"
distribution parameters
transformed to their model fitting scale; 2) "response"
as 1), but on their
original scale; 3) "lpmatrix" a list of design matrices; 4) "quantile"
estimates of distribution quantile(s); and 5) "qqplot" a quantile-quantile
plot.
A data frame or list of predictions, or a plot if type == "qqplot"
Youngman, B. D. (2022). evgam: An R Package for Generalized Additive Extreme Value Modules. Journal of Statistical Software. To appear. doi: 10.18637/jss.v103.i03
data(fremantle) fmla_gev <- list(SeaLevel ~ s(Year, k=5, bs="cr"), ~ 1, ~ 1) m_gev <- evgam(fmla_gev, fremantle, family = "gev") # prediction of link GEV parameter for fremantle data predict(m_gev) # predictions for Year 1989 y1989 <- data.frame(Year = 1989) # link GEV parameter predictions predict(m_gev, y1989) # GEV parameter predictions predict(m_gev, y1989, type= "response") # 10-year return level predictions predict(m_gev, y1989, type= "quantile", prob = .9) # 10- and 100-year return level predictions predict(m_gev, y1989, type= "quantile", prob = c(.9, .99))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.