R: Plot partial dependency (PD) line plots

plot_pds {ebirdst}

R Documentation

Plot partial dependency (PD) line plots

Description

For a given eBird Status and Trends species, produce a line plot showing the partial dependence (PD) relationship for a given predictor. Two options for smoothing are provided.

Usage

plot_pds(
  pds,
  predictor,
  ext,
  bootstrap_smooth = TRUE,
  show_stixel_pds = FALSE,
  show_quantiles = FALSE,
  n_bs = 100,
  ss_equivalent = 10,
  k = 25,
  ci_alpha = 0.05,
  gbm_n_trees = 500,
  ylim = NULL,
  plot = TRUE
)

Arguments

`pds`	data frame; partial dependence data from `load_pds()`.
`predictor`	character; single predictor name to plot PD for. For a full list of predictors, and their associated definitions, see ebirdst_predictors.
`ext`	ebirdst_extent object; the spatiotemporal extent over which to calculate PDs. This is required, since results are less meaningful over large spatiotemporal extents.
`bootstrap_smooth`	logical; the ideal visualization of the PD data is a pointwise GAM smoothing of the individual stixel PD values. This argument specifies whether this should be done directly on the full PD dataset (`bootstrap_smooth = FALSE`) or by subsampling and bootstrapping. The latter approach deals with the randomness in the data and can be more efficient for large datasets.
`show_stixel_pds`	logical; whether to plot the individual stixel PD values as semi-transparent lines.
`show_quantiles`	logical; adds a band for the upper (90th) and lower (10th) quantiles of the individual stixel PD values. These are calculated using quantile regression.
`n_bs`	int; number of GAM bootstrap iterations when estimating PD confidence intervals. Ignored if `bootstrap_smooth = FALSE`.
`ss_equivalent`	int; when bootstrapping to estimate PD confidence intervals, this argument specifies the size of the subsample of the original data. In particular, `ss_equivalent` should be an integer representing the equivalent sampling size when averaging this number of PD estimates.
`k`	integer; number of knots to use in the GAM when smooth the PD relationship.
`ci_alpha`	numeric; alpha level of confidence intervals. Default is 0.05.
`gbm_n_trees`	integer; number of trees to fit in the GBM when estimating quantiles. Ignored if `show_quantiles = FALSE`. Default is 500.
`ylim`	numeric; 2-element vector to pre-define the y-limits of plotting. In the format `c(ymin, ymax)`.
`plot`	logical; whether to plot the PD relationships or just return data.

Value

Plots the smoothed partial dependence relationship for the specified predictor and returns a data frame of the smoothed curve with confidence intervals.

Examples

## Not run: 
# download example data
path <- ebirdst_download("example_data", tifs_only = FALSE)
# or get the path if you already have the data downloaded
path <- get_species_path("example_data")

# load predictor dependence data
pds <- load_pds(path)

# define a spatiotemporal extent to plot data from
bb_vec <- c(xmin = -86, xmax = -83, ymin = 41.5, ymax = 43.5)
e <- ebirdst_extent(bb_vec, t = c("05-01", "05-31"))

# for testing, run with 5 bootstrap iterations for speed
# in practice, best to run with the default number of iterations (100)
pd_smooth <- plot_pds(pds, "solar_noon_diff", ext = e, n_bs = 5)
dplyr::glimpse(pd_smooth)

## End(Not run)

[Package ebirdst version 0.3.5 Index]