total: Total Moose Workflow

In psolymos/moosecounter: Adaptive Moose Surveys

Total Moose Workflow

Description

'switch_response' switches between total Moose vs. cows only. This sets the column name for total Moose estimation. 'mc_update_total' Updates/prepares the Moose data set for downstream analyses (i.e. calculates some derived variables, sets a surveyed/unsurveyed indicator, and optionally takes a subset). 'mc_fit_total' fit total Moose abundance models. 'mc_models_total' prints out estimates from the models.

Usage

switch_response(type = "total")

mc_update_total(x, srv = NULL, ss = NULL)

mc_fit_total(
  x,
  vars = NULL,
  zi_vars = NULL,
  dist = "ZINB",
  weighted = FALSE,
  robust = FALSE,
  intercept = c("both", "count", "zero", "none"),
  xv = FALSE,
  ...
)

mc_models_total(ml, x, coefs = TRUE)

mc_predict_total(model_id, ml, x, do_boot = TRUE, do_avg = FALSE)

mc_get_pred(PI, ss = NULL)

pred_density_moose_PI(PI)

mc_plot_residuals(model_id, ml, x)

mc_plot_predpi(PI)

mc_plot_pidistr(PI, id = NULL, plot = TRUE, breaks = "Sturges")

mc_plot_predfit(i, PI, ss = NULL, interactive = FALSE)

Arguments

type	The type of the response, can be '"total"' or '"cows"' for 'switch_response'.
x	A data frame with Moose data, or a data frame from 'mc_update_total()'.
srv	Logical vector, rows of 'x' that are surveyed, falls back to global options when 'NULL'.
ss	Logical vector to subset 'x', default is to take no subset.
vars	column names of 'x' to be used as predictors for the count model.
zi_vars	optional, column names of 'x' to be used as predictors for the zero model.
dist	Count distribution ('P', 'NB', 'ZIP', 'ZINB').
weighted	Logical, to use weighting to moderate influential observations.
robust	Logical, use robust regression approach.
intercept	Which intercepts to keep. Dropped intercepts lead to regression through the origin (at the linear predictor scale).
xv	Logical, should leave-one-out error be calculated.
...	Other arguments passed to 'zeroinfl2()'.
ml	Named list of models.
coefs	logical, return coefficient table too.
model_id	model ID or model IDs (can be multiple from 'names(ml)').
do_boot	Logical, to do bootstrap or not.
do_avg	Logical, to do model averaging or not.
PI	PI object returned by 'mc_predict_total()'
id	Cell ID.
plot	Logical, to plot or just give summary.
breaks	Breaks argument passed to 'graphics::hist()'.
i	Column (variable) name or index.
interactive	Logical, draw interactive plot.

Examples

mc_options(B=10)

x <- read.csv(
    system.file("extdata/MayoMMU_QuerriedData.csv",
        package="moosecounter"))

#switch_response("cows") # for cows only
switch_response("total")

x <- mc_update_total(x)

mc_plot_univariate("Subalp_Shrub_250buf", x, "ZINB")

vars <- c("ELC_Subalpine", "Fire1982_2012", "Fire8212_DEM815",
    "NALC_Needle", "NALC_Shrub", "Subalp_Shrub_250buf",
    "ELCSub_Fire8212DEM815", "SubShrub250_Fire8212DEM815")

mc_plot_multivariate(vars, x)

ML <- list()
ML[["Model 0"]] <- mc_fit_total(x, dist="ZINB")
ML[["Model 1"]] <- mc_fit_total(x, vars[1:2], dist="ZINB")
ML[["Model 2"]] <- mc_fit_total(x, vars[2:3], dist="ZIP")
ML[["Model 3"]] <- mc_fit_total(x, vars[3:4], dist="ZINB")

mc_models_total(ML, x)
mc_plot_residuals("Model 3", ML, x)

PI <- mc_predict_total(
    model_id=c("Model 1", "Model 3"),
    ml=ML,
    x=x,
    do_boot=TRUE, do_avg=TRUE)

mc_get_pred(PI)
pred_density_moose_PI(PI)
mc_plot_predpi(PI)
mc_plot_pidistr(PI)
mc_plot_pidistr(PI, id=2)

psolymos/moosecounter documentation built on Feb. 25, 2024, 4:43 p.m.