fitmoplot: Plot the fitted results for a tidal object by month

Description

Plot a tidal object to view the response variable observations, predictions, and normalized results separately for each month.

Usage

fitmoplot(dat_in, ...)
# S3 method for tidal
fitmoplot(
  dat_in,
  month = c(1:12),
  tau = NULL,
  predicted = TRUE,
  logspace = TRUE,
  dt_rng = NULL,
  ncol = NULL,
  col_vec = NULL,
  grids = TRUE,
  pretty = TRUE,
  lwd = 1,
  size = 2,
  alpha = 1,
  ...
)
# S3 method for tidalmean
fitmoplot(
  dat_in,
  month = c(1:12),
  predicted = TRUE,
  logspace = TRUE,
  dt_rng = NULL,
  ncol = NULL,
  col_vec = NULL,
  grids = TRUE,
  pretty = TRUE,
  lwd = 1,
  size = 2,
  alpha = 1,
  ...
)

Value

A ggplot object that can be further modified

Arguments

dat_in: input tidal or tidalmean object
...: arguments passed to other methods
month: numeric indicating months 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
dt_rng: 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.
ncol: numeric argument passed to facet_wrap indicating number of facet columns
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
size: numeric value indicating size of points
alpha: numeric value indicating transparency of points or lines

Details

The plots are similar to those produced by fitplot except the values are faceted by month. This allows an evaluation of trends over time independent of seasonal variation. Multiple observations within each month for each year are averaged for a smoother plot.

Examples

Run this code


## load a fitted tidal object
data(tidfit)

# plot using defaults
fitmoplot(tidfit)
if (FALSE) { 
# get the same plot but use default ggplot settings
fitmoplot(tidfit, pretty = FALSE)

# plot specific quantiles
fitmoplot(tidfit, tau = c(0.1, 0.9))

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

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

library(ggplot2)

fitmoplot(tidfit, pretty = FALSE, linetype = 'dashed') + 
 theme_classic() + 
 scale_y_continuous(
   'Chlorophyll', 
   limits = c(0, 5)
   ) +
 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)

fitmoplot(tidfitmean)    
}