introduction.R
In bdrc: Bayesian Discharge Rating Curves

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = 'center',
  prompt = TRUE
)

## ----message=FALSE------------------------------------------------------------
library(bdrc)
set.seed(1) #set seed for reproducibility

## ----data---------------------------------------------------------------------
data(krokfors)
head(krokfors)

## ----cache=TRUE---------------------------------------------------------------
gplm.fit <- gplm(Q ~ W, data = krokfors, parallel = TRUE, num_cores = 2)

## ----echo=FALSE---------------------------------------------------------------
cat("\u26A0 Warning: Some chains are not mixing well. Parameters with Rhat > 1.1:\n  - sigma_eta: Rhat = 1.281")

## ----echo=FALSE---------------------------------------------------------------
cat("\u2139 Try re-running the model after inspecting the trace plots, convergence diagnostics plots, and reviewing the data for potential issues.\n")

## -----------------------------------------------------------------------------
summary(gplm.fit)

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit)

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'histogram', param = 'c')

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'histogram', param = 'c', transformed = TRUE)

## ----fig.width=8, fig.height=3------------------------------------------------
plot(gplm.fit, type = 'histogram', param = c('a', 'c'))

## ----fig.width=10, fig.height=6-----------------------------------------------
plot(gplm.fit, type = 'histogram', param = 'hyperparameters')

## ----fig.width=10, fig.height=6-----------------------------------------------
plot(gplm.fit, type = 'histogram', param = 'hyperparameters', transformed = TRUE)

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'f')

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'sigma_eps')

## ----fig.width=8, fig.height=6,results='hide'---------------------------------
plot(gplm.fit, type = 'panel', transformed = TRUE)

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'residuals')

## ----fig.width=10, fig.height=6-----------------------------------------------
plot(gplm.fit, type = 'trace', param = 'c', transformed = TRUE)

## ----fig.width=10, fig.height=6-----------------------------------------------
plot(gplm.fit, type = 'trace', param = 'hyperparameters', transformed = TRUE)

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'r_hat')

## ----fig.width=8, fig.height=6------------------------------------------------
plot(gplm.fit, type = 'autocorrelation')

## ----cache=TRUE,eval=FALSE----------------------------------------------------
# gplm.fit.known_c <- gplm(Q ~ W, krokfors, c_param = 7.65, h_max = 10, parallel = FALSE)

## -----------------------------------------------------------------------------
h_grid <- seq(8, 8.2, by = 0.01)
rating_curve_h_grid <- predict(gplm.fit, newdata = h_grid)
print(rating_curve_h_grid)