Exploration — explore • moosecounter

Functions to explore relationships between total Moose or composition as response vs. environmental predictor variables.

Usage

mc_plot_univariate(
  i,
  x,
  dist = "ZINB",
  base = TRUE,
  type = c("density", "map", "fit"),
  interactive = FALSE
)

mc_plot_multivariate(vars, x, alpha = NULL)

mc_plot_comp(i, x)

Arguments

i: Column name from `x` to be used as a predictor.
x: Data frame with Moose data.
dist: Count distribution (`P`, `NB`, `ZIP`, or `ZINB`).
base: Logical, draw base graphics or ggplot2.
type: Character, type of plot to be drawn (`"density"`, `"map"`, `"fit"`). Base plot can draw all 3, ggplot2 can only draw one at a time.
interactive: Logical, draw interactive plot (not available for base plots).
vars: A vector of column names from `x` to be used as a predictor.
alpha: Alpha level defining `mincriterion = 1 - alpha` for `partykit::ctree()`.

Details

`mc_plot_univariate` implements visual univariate (single predictor) exploration for the total Moose count models.

`mc_plot_multivariate` implements visual multivariate (multiple predictors) exploration based on regression trees (recursive partitioning in a conditional inference framework) for total Moose counts.

`mc_plot_comp` implements visual univariate (single predictor) exploration for the multinomial composition models.

Examples

## Prepare Moose data from Mayo
x <- read.csv(
    system.file("extdata/MayoMMU_QuerriedData.csv",
        package="moosecounter"))
switch_response("total")
x <- mc_update_total(x)

## Univariate exploration for total Moose
mc_plot_univariate("Subalp_Shrub_250buf", x, "ZINB")

## Multivariate exploration for total Moose
vars <- c("ELC_Subalpine", "Fire1982_2012", "Fire8212_DEM815",
    "NALC_Needle", "NALC_Shrub", "Subalp_Shrub_250buf",
    "ELCSub_Fire8212DEM815", "SubShrub250_Fire8212DEM815")
mc_plot_multivariate(vars, x)

## Univariate exploration for composition
mc_plot_comp("Fire8212_DEM815", x)