sAICfun: sAICfun

In jebyrnes/multifunc: Analysis of Ecological Drivers on Ecosystem Multifunctionality

sAICfun

Description

sAICfun examines which species have an effect on which function using a stepwise AIC approach

Usage

sAICfun(
  response,
  species,
  data,
  positive.desired = TRUE,
  method = "lm",
  combine = "+",
  ...
)

Arguments

response	Name of the response column
species	Vector of column names of species
data	data frame with species presence/abscence of values of functions
positive.desired	Is a positive effect the desired sign. Defaults to TRUE
method	Fitting function for statistical models. Defaults to lm.
combine	How are species combined in the model? Defaults to "+" for additive combinations.
...	Other arguments to be supplied to fitting function.

Details

sAICfun takes a dataset, response, and function, and then uses a stepAIC approach to determine the best model. From that it extracts the species with a positive, negative, and neutral effect on that function.

Value

Returns list of species with positive negative or neutral contributions, the relevant coefficient and effect matrices, and response name

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

spList <- sAICfun("biomassY3", species, germany)
# " spList
res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars



#########
# sAICfun takes a dataset, response, and function, and then uses a stepAIC approach
# to determine the best model.  From that it extracts the species with a positive,
# negative, and neutral effect on that function
#########

jebyrnes/multifunc documentation built on May 25, 2022, 9:43 p.m.