Plot the fitted results for a tidal object by month

Source: R/fitmoplot.R

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

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,
  ...
)

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

Value

A ggplot object that can be further modified

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


## 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)    
}