View source: R/sAICfun.R View source: R/getRedundancy.R
| sAICfun | R Documentation | 
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 = "+", ... )
| 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  | 
| combine | How are species combined in the model? Defaults to "+" for additive combinations. | 
| ... | Other arguments to be supplied to fitting function. | 
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.
Returns list of species with positive negative or neutral contributions, the relevant coefficient and effect matrices, and response name
Jarrett Byrnes.
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
#########
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