CVNND: Coefficient of variation, mean, minimum and standard... In cati: Community Assembly by Traits: Individuals and Beyond

Description

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ``` 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.

Value

One value corresponding to the metric value.

Author(s)

 ```1 2 3 4 5 6 7``` ```data(finch.ind) CVNND(traits.finch[,1], na.rm = TRUE) CVNND(traits.finch[,1], div_range = TRUE, na.rm = TRUE) CVNND(traits.finch, na.rm = TRUE) CVNND(traits.finch, scale.tr = FALSE, na.rm = TRUE) SDND(traits.finch[,1], na.rm = TRUE) ```