multifunc (version 0.9.4)

sAICfun: 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.

Value

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

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.

Examples

# NOT RUN {
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
#########
# }