This function fits PERMANOVA models for all combinations of variables in a given dataset, and arranges the models by Akaike Information Criterion (AICc) score. The function also calculates the maximum variance inflation factor (max_vif) for each model.
Usage
fit_models(
all_forms,
com_data,
env_data,
method = "bray",
ncores = 2,
log = TRUE,
logfile = "log.txt",
multiple = 100,
strata = NULL,
verbose = FALSE
)Arguments
- all_forms
A data frame generated by
make_models- com_data
A dataset with community presence absense or abundance data, you can also use a dist class file generated from vegan, betapart or other packages
- env_data
A dataset with the variables described in all_froms
- method
method for distance from
vegdist, this will be ignored if com_data is a distance file- ncores
An integer specifying the number of cores to use for parallel processing
- log
logical if true, a log file will be generated
- logfile
the text file that will be generated as a log
- multiple
after how many loops to write a log file
- strata
a block variable similar to the use in
adonis2- verbose
logical, defaults TRUE, sends messages about processing times
Value
A data.frame with fitted models arranged by AICc, including the formula used, the number of explanatory variables, R2, adjusted R2, and the AICc and max VIF.
References
Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26(1), 32-46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x
Examples
# \donttest{
## example with dataframe as community data
library(vegan)
data(dune)
data(dune.env)
AllModels <- make_models(vars = c("A1", "Moisture", "Manure"))
#> 1 of 3 ready 2024-01-22 11:58:04.210656
#> 2 of 3 ready 2024-01-22 11:58:06.963923
#> 3 of 3 ready 2024-01-22 11:58:08.543762
fit_models(
all_forms = AllModels,
com_data = dune,
env_data = dune.env
)
#> Warning: already exporting variable(s): env_data
#> form max_vif AICc k N A1
#> 1 Distance ~ Moisture 0.000000 -30.36319 4 20 NA
#> 2 Distance ~ A1 0.000000 -29.72347 2 20 0.16816657
#> 3 Distance ~ 1 0.000000 -28.52467 1 20 NA
#> 4 Distance ~ A1 + Moisture 3.000000 -28.21149 5 20 0.04230343
#> 5 Distance ~ Manure 0.000000 -25.21490 5 20 NA
#> 6 Distance ~ A1 + Manure 4.000000 -25.18745 6 20 0.12092088
#> 7 Distance ~ Moisture + Manure 4.000000 -22.93984 8 20 NA
#> 8 Distance ~ A1 + Moisture + Manure 4.508169 -18.59078 9 20 0.04145173
#> Moisture Manure Model
#> 1 0.4019903 NA NA
#> 2 NA NA NA
#> 3 NA NA 0
#> 4 0.2761272 NA NA
#> 5 NA 0.3544714 NA
#> 6 NA 0.3072258 NA
#> 7 0.3005223 0.2530035 NA
#> 8 0.2210532 0.2521518 NA
## example with distance as community data
library(betapart)
Distance <- beta.pair.abund(dune)
Distance <- Distance$beta.bray.bal
fit_models(
all_forms = AllModels,
com_data = Distance,
env_data = dune.env
)
#> Warning: already exporting variable(s): env_data
#> form max_vif AICc k N A1
#> 1 Distance ~ Moisture 0.000000 -36.35514 4 20 NA
#> 2 Distance ~ A1 + Moisture 3.000000 -34.09230 5 20 0.03473680
#> 3 Distance ~ A1 0.000000 -33.85715 2 20 0.19124904
#> 4 Distance ~ 1 0.000000 -32.09553 1 20 NA
#> 5 Distance ~ A1 + Manure 4.000000 -29.75194 6 20 0.14829650
#> 6 Distance ~ Moisture + Manure 4.000000 -29.52300 8 20 NA
#> 7 Distance ~ Manure 0.000000 -28.72546 5 20 NA
#> 8 Distance ~ A1 + Moisture + Manure 4.508169 -25.44522 9 20 0.03917375
#> Moisture Manure Model
#> 1 0.4701720 NA NA
#> 2 0.3136598 NA NA
#> 3 NA NA NA
#> 4 NA NA 0
#> 5 NA 0.3095700 NA
#> 6 0.3507105 0.2330610 NA
#> 7 NA 0.3525225 NA
#> 8 0.2415877 0.2374979 NA
# }