seasplot(dat_in,...)## S3 method for class 'tidal'seasplot( dat_in, tau =NULL, predicted =TRUE, span =0.4, lwd =1, size =2, alpha =1, col_vec =NULL, grids =TRUE, logspace =TRUE,...)## S3 method for class 'tidalmean'seasplot( dat_in, predicted =TRUE, span =0.4, lwd =1, size =2, alpha =1, col_vec =NULL, grids =TRUE, logspace =TRUE,...)
Arguments
dat_in: Input data object
...: arguments passed to other methods
tau: numeric of quantile to plot
predicted: logical indicating if seasonal smooth is based on model predictions, default TRUE, otherwise the smooth is based on flow-normalized predictions
span: numeric indicating the smoothing parameter for the loess fit, passed to stat_smooth
lwd: numeric value indicating width of lines
size: numeric value indicating size of points
alpha: numeric value indicating transparency of points or lines
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
logspace: logical indicating if plots are in log space
Details
Seasonal variation across all years can be viewed by showing the observed annual data on a common y-axis. The year value is removed from the results such that the y-axis shows only the day of the year. A simple loess (locally estimated) polynomial smooth is added to show the seasonal trend in the results, where the smoother is fit through the model results for the observed data. The fit can be smoothed through the model predictions or the flow-normalized predictions, neither of which are shown on the plot.
Examples
# load a fitted tidal objectdata(tidfit)# plot using defaults# defaults to all quantiles for tidal objectseasplot(tidfit)# tidalmean objectseasplot(tidfitmean)
See Also
dynaplot, fitmoplot, gridplot, and sliceplot produce similar graphics except variation in the same month across years is emphasized.