Plot a tidal object to view response variable observations, predictions, and normalized results.
fitplot(dat_in, ...)
# S3 method for tidal
fitplot(
dat_in,
tau = NULL,
predicted = TRUE,
annuals = TRUE,
logspace = TRUE,
dt_rng = NULL,
col_vec = NULL,
grids = TRUE,
min_mo = 9,
mo_strt = 10,
pretty = TRUE,
lwd = 1,
size = 2,
alpha = 1,
...
)
# S3 method for tidalmean
fitplot(
dat_in,
predicted = TRUE,
annuals = TRUE,
logspace = TRUE,
dt_rng = NULL,
col_vec = NULL,
grids = TRUE,
min_mo = 9,
mo_strt = 10,
pretty = TRUE,
lwd = 1,
size = 2,
alpha = 1,
...
)
input tidal or tidalmean object
arguments passed to other methods
numeric vector of quantiles to plot, defaults to all in object if not supplied
logical indicating if standard predicted values are plotted, default TRUE
, otherwise normalized predictions are plotted
logical indicating if plots are annual aggregations of results
logical indicating if plots are in log space
Optional chr string indicating the date range of the plot. Must be two values in the format 'YYYY-mm-dd' which is passed to as.Date
.
chr string of plot colors to use, passed to gradcols
. Any color palette from RColorBrewer can be used as a named input. Palettes from grDevices must be supplied as the returned string of colors for each palette.
logical indicating if grid lines are present
numeric value from one to twelve indicating the minimum number of months with observations for averaging by years, applies only if annuals = TRUE
. See annual_agg
.
numeric indicating month to start aggregation years, defaults to October for USGS water year from October to September, applies only if annuals = TRUE
. See annual_agg
.
logical indicating if my subjective idea of plot aesthetics is applied, otherwise the ggplot
default themes are used
numeric value indicating width of lines
numeric value indicating size of points
numeric value indicating transparency of points or lines
A ggplot
object that can be further modified
## load a fitted tidal object
data(tidfit)
# plot using defaults
fitplot(tidfit)
# get the same plot but use default ggplot settings
fitplot(tidfit, pretty = FALSE)
# plot in log space
fitplot(tidfit, logspace = TRUE)
# plot specific quantiles
fitplot(tidfit, tau = c(0.1, 0.9))
# plot the normalized predictions
fitplot(tidfit, predicted = FALSE)
# plot as monthly values
fitplot(tidfit, annuals = FALSE)
# format the x-axis is using annual aggregations
library(ggplot2)
fitplot(tidfit, annual = TRUE) +
scale_x_date(limits = as.Date(c('2000-01-01', '2012-01-01')))
# modify the plot as needed using ggplot scales, etc.
fitplot(tidfit, pretty = FALSE, linetype = 'dashed') +
theme_classic() +
scale_y_continuous(
'Chlorophyll',
limits = c(0, 50)
) +
scale_colour_manual(
'Predictions',
labels = c('lo', 'md', 'hi'),
values = c('red', 'green', 'blue'),
guide = guide_legend(reverse = TRUE)
)
# plot a tidalmean object
data(tidfitmean)
fitplot(tidfitmean)