seasyrplot: Plot seasonal model response by years

Description

Plot seasonal model response by years on a common axis

Usage

seasyrplot(dat_in, ...)
# S3 method for tidal
seasyrplot(
  dat_in,
  years = NULL,
  tau = NULL,
  predicted = TRUE,
  logspace = TRUE,
  col_vec = NULL,
  grids = TRUE,
  pretty = TRUE,
  lwd = 0.5,
  alpha = 1,
  ...
)
# S3 method for tidalmean
seasyrplot(
  dat_in,
  years = NULL,
  tau = NULL,
  predicted = TRUE,
  logspace = TRUE,
  col_vec = NULL,
  grids = TRUE,
  pretty = TRUE,
  lwd = 0.5,
  alpha = 1,
  ...
)

Value

A ggplot object that can be further modified

Arguments

dat_in: input tidal or tidalmean object
...: arguments passed to other methods
years: numeric vector of years to plot
tau: numeric vector of quantiles to plot, defaults to all in object if not supplied
predicted: logical indicating if standard predicted values are plotted, default TRUE, otherwise normalized predictions are plotted
logspace: logical indicating if plots are in log space
col_vec: 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.
grids: logical indicating if grid lines are present
pretty: logical indicating if my subjective idea of plot aesthetics is applied, otherwise the ggplot default themes are used
lwd: numeric value indicating width of lines
alpha: numeric value indicating transparency of points or lines

Details

The plot is similar to that produced by seasplot except the model estimates are plotted for each year as connected lines, as compared to loess lines fit to the model results. seasyrplot is also similar to sliceplot except the x-axis and legend grouping variable are flipped. This is useful for evaluating between-year differences in seasonal trends.

Multiple predictions per month are averaged for a smoother plot.

Note that the year variable used for color mapping is treated as a continuous variable although it is an integer by definition.

Examples

Run this code


## load a fitted tidal object
data(tidfit)

# plot using defaults
seasyrplot(tidfit)

# get the same plot but use default ggplot settings
seasyrplot(tidfit, pretty = FALSE)

# plot specific quantiles
seasyrplot(tidfit, tau = c(0.9))

# plot the normalized predictions
seasyrplot(tidfit, predicted = FALSE)

# modify the plot as needed using ggplot scales, etc.

library(ggplot2)

seasyrplot(tidfit, pretty = FALSE, linetype = 'dashed') + 
 theme_classic() + 
 scale_y_continuous(
   'Chlorophyll', 
   limits = c(0, 5)
   )
   
# plot a tidalmean object
data(tidfitmean)

seasyrplot(tidfitmean)