Total Moose Workflow — total • moosecounter

`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)