sAICfun examines which species have an effect on which function using a stepwise AIC approach
sAICfun(
response,
species,
data,
positive.desired = TRUE,
method = "lm",
combine = "+",
...
)Name of the response column
Vector of column names of species
data frame with species presence/abscence of values of functions
Is a positive effect the desired sign. Defaults to TRUE
Fitting function for statistical models. Defaults to lm.
How are species combined in the model? Defaults to "+" for additive combinations.
Other arguments to be supplied to fitting function.
Returns list of species with positive negative or neutral contributions, the relevant coefficient and effect matrices, and response name
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.
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
#########