wqtrends (version 1.5.1)

show_metseason: Plot period (seasonal) averages from fitted GAM

Description

Plot period (seasonal) averages from fitted GAM

Usage

show_metseason(
  mod,
  metfun = mean,
  doystr = 1,
  doyend = 364,
  yrstr = 2000,
  yrend = 2019,
  yromit = NULL,
  ylab,
  width = 0.9,
  size = 1.5,
  seascol = "deepskyblue3",
  trndcol = "pink",
  nsim = 10000,
  useave = FALSE,
  base_size = 11,
  xlim = NULL,
  ylim = NULL,
  ...
)

Value

A ggplot object

Arguments

mod

input model object as returned by anlz_gam

metfun

function input for metric to calculate, e.g., mean, var, max, etc

doystr

numeric indicating start Julian day for extracting averages

doyend

numeric indicating ending Julian day for extracting averages

yrstr

numeric for starting year for trend model, see details

yrend

numeric for ending year for trend model, see details

yromit

optional numeric vector for years to omit from the plot, see details

ylab

chr string for y-axis label

width

numeric for width of error bars

size

numeric for point size

seascol

chr string for color of the seasonal averages

trndcol

chr sting for color of the trend line

nsim

numeric indicating number of random draws for simulating uncertainty

useave

logical indicating if anlz_avgseason is used for the seasonal metric calculation, see details

base_size

numeric indicating base font size, passed to theme_bw

xlim

optional numeric vector of length two for x-axis limits

ylim

optional numeric vector of length two for y-axis limits

...

additional arguments passed to metfun, e.g., na.rm = TRUE

Details

Setting yrstr or yrend to NULL will suppress plotting of the trend line for the meta-analysis regression model.

The optional yromit vector can be used to omit years from the plot and trend assessment. This may be preferred if seasonal estimates for a given year have very wide confidence intervals likely due to limited data, which can skew the trend assessments.

Set useave = T to speed up calculations if metfun = mean. This will use anlz_avgseason to estimate the seasonal summary metrics using a non-stochastic equation.

Examples

library(dplyr)

# data to model
tomod <- rawdat %>%
  filter(station %in% 34) %>%
  filter(param %in% 'chl') %>% 
  filter(yr > 2015)
  
mod <- anlz_gam(tomod, trans = 'ident')

show_metseason(mod, doystr = 90, doyend = 180, yrstr = 2016, yrend = 2019, 
     ylab = 'Chlorophyll-a (ug/L)')

# \donttest{
# show seasonal metrics without annual trend
show_metseason(mod, doystr = 90, doyend = 180, yrstr = NULL, yrend = NULL, 
     ylab = 'Chlorophyll-a (ug/L)')
     
# omit years from the analysis
show_metseason(mod, doystr = 90, doyend = 180, yrstr = 2016, yrend = 2019,
     yromit = 2017, ylab = 'Chlorophyll-a (ug/L)')
# }