Moose Composition Workflow — comp • moosecounter

Fit composition model for Moose using a multinomial model to capture how predictors affect composition data; calculate prediction intervals based on composition model; and extract useful summaries.

Usage

mc_check_comp(x)

mc_fit_comp(x, vars = NULL)

mc_models_comp(model_list_comp, coefs = TRUE)

mc_predict_comp(
  total_model_id,
  comp_model_id,
  model_list_total,
  model_list_comp,
  x,
  do_avg = FALSE,
  fix_mean = FALSE,
  PI = NULL
)

subset_CPI_data(CPI, ss)

pred_density_moose_CPI(CPI, ...)

Arguments

x: A Moose data frame object.
vars: Column names of `x` to be used as predictors for the composition model.
model_list_comp: Named list of total composition models.
coefs: logical, return coefficient table too.
total_model_id: Model ID or model IDs for total moose model (can be multiple model IDs from `names(model_list_total)`).
comp_model_id: Model ID or model IDs for composition model (single model ID from `names(model_list_comp)`).
model_list_total: Named list of total moose models.
do_avg: Logical, to do model averaging or not.
fix_mean: logical, use the fixed (rounded) mean as the Multinomial size instead of the bootstrap PI counts.
PI: Total Moose PI object.
CPI: Composition PI object.
ss: A subset of rows (logical or numeric vector).
...: Other arguments passed to underlying functions.

Examples

mc_options(B=10)
x <- read.csv(
    system.file("extdata/MayoMMU_QuerriedData.csv",
        package="moosecounter"))
## Prepare Moose data frame object
x <- mc_update_total(x)

## Total moose model list
vars <- c("ELC_Subalpine", "Fire1982_2012", "Fire8212_DEM815",
    "NALC_Needle", "NALC_Shrub", "Subalp_Shrub_250buf",
    "ELCSub_Fire8212DEM815", "SubShrub250_Fire8212DEM815")
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")

## Composition odel list
CML <- list()
CML[['FireDEMSub']] <- mc_fit_comp(x, "Fire8212_DEM815")

## Stats from the models
mc_models_comp(CML)

## Calculate PI
CPI <- mc_predict_comp(
    total_model_id="Model 3",
    comp_model_id='FireDEMSub',
    model_list_total=ML,
    model_list_comp=CML,
    x=x,
    do_avg=FALSE)

## Predict density
pred_density_moose_CPI(CPI)