fitmoplot function

Plot the fitted results for a tidal object by month

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

fitmoplot(dat_in, ...)

## S3 method for class '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 class '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

Returns

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)
## Not run:
 
# 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)    
## End(Not run)

See Also

fitplot, prdnrmplot, sliceplot