Coefficient of variation, mean, minimum and standard deviation of the nearest neigbourhood distance.
CVNND.Rd
CVNND : Coefficient of variation of the nearest neigbourhood distance
MNND : Mean of the nearest neigbourhood distance
MinNND : Minimum of the nearest neigbourhood distance
SDNND : Standard deviation of the nearest neigbourhood distance
SDND : Standard deviation of the neigbourhood distance
MND : Mean of the neigbourhood distance
Usage
CVNND(traits, div_range = FALSE, na.rm = FALSE, scale.tr = TRUE,
method.dist = "euclidian")
MNND(traits, div_range = FALSE, na.rm = FALSE, scale.tr = TRUE,
method.dist = "euclidian")
MinNND(traits, div_range = FALSE, na.rm = FALSE, scale.tr = TRUE,
method.dist = "euclidian")
SDNND(traits, div_range = FALSE, na.rm = FALSE, scale.tr = TRUE,
method.dist = "euclidian")
SDND(trait, div_range = FALSE, na.rm = FALSE)
MND(trait, div_range = FALSE, na.rm = FALSE)
Arguments
- traits
Trait vector (uni-trait metric) or traits matrix (Multi-traits metric), traits in column.
- trait
Trait vector
- div_range
Does metric need to be divided by the range? Default is no.
- na.rm
If div_range=TRUE, a logical value indicating whether NA values should be stripped before the computation proceeds.
- scale.tr
Does traits need to be scale before multi-traits metric calculation? Default is yes.
- method.dist
Method to calculate the distance in case of multi-traits metric (function dist). Default is euclidian.
References
Aiba, M., Katabuchi, M., Takafumi, H., Matsuzaki, S.S., Sasaki, T. & Hiura, T. 2013. Robustness of trait distribution metrics for community assembly studies under the uncertainties of assembly processes. Ecology, 94, 2873-2885. Jung, Vincent, Cyrille Violle, Cedric Mondy, Lucien Hoffmann, et Serge Muller. 2010. Intraspecific variability and trait-based community assembly: Intraspecific variability and community assembly. Journal of Ecology 98 (5): 1134-1140.
Examples
data(finch.ind)
CVNND(traits.finch[,1], na.rm = TRUE)
#> [1] 49.62862
CVNND(traits.finch[,1], div_range = TRUE, na.rm = TRUE)
#> [1] 10.65975
CVNND(traits.finch, na.rm = TRUE)
#> [1] 0.6326296
CVNND(traits.finch, scale.tr = FALSE, na.rm = TRUE)
#> [1] 0.7362636
SDND(traits.finch[,1], na.rm = TRUE)
#> [1] 0.1248847