seasplot | R Documentation |
Plot seasonal trends by combining annual data
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,
...
)
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 |
span |
numeric indicating the smoothing parameter for the loess fit, passed to |
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 |
grids |
logical indicating if grid lines are present |
logspace |
logical indicating if plots are in log space |
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.
dynaplot
, fitmoplot
, gridplot
, and sliceplot
produce similar graphics except variation in the same month across years is emphasized.
# load a fitted tidal object
data(tidfit)
# plot using defaults
# defaults to all quantiles for tidal object
seasplot(tidfit)
# tidalmean object
seasplot(tidfitmean)
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