get_weighted_mnts

internal

The “TAD” package compiled an analytical framework based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).

Raphael Martin

Realize the Trait Abundance Distribution

Nathan Rondeau

Yoann Le Bagousse-Pinguet

Raphaël Martin

Lain Pavot

Pierre Liancourt

Nicolas Gross

INRAe/UREP 

get_weighted_mnts function

<dl><dt>weights_factor</dt>
<dd>the dataframe which contains the different
factor linked to the weights</dd>
<dt>trait_data</dt>
<dd>a vector of the data linked to the different factor</dd>
<dt>weighted_moments_file</dt>
<dd>the path and name of the RDS file to
load/save the dataframe which
contains the calculated moments</dd>
<dt>regenerate_weighted_moments_df</dt>
<dd>boolean to specify if
the weighted moments dataframe is computed again</dd>
<dt>randomization_number</dt>
<dd>the number of random abundance matrix to
generate</dd>
<dt>slope_distance</dt>
<dd>slope of the theoretical distribution
law (default: slope = 1 intercept = 1.86 skew-uniform distribution family)</dd>
<dt>intercept_distance</dt>
<dd>intercept of the theoretical distribution
law (default: slope = 1 intercept = 1.86 skew-uniform distribution family)</dd></dl>

Arguments

weighted moments generation — get_weighted_mnts

<dl>

<dt>weights_factor</dt>
<dd>the dataframe which contains the different
factor linked to the weights</dd>


<dt>trait_data</dt>
<dd>a vector of the data linked to the different factor</dd>


<dt>weighted_moments_file</dt>
<dd>the path and name of the RDS file to
load/save the dataframe which
contains the calculated moments</dd>


<dt>regenerate_weighted_moments_df</dt>
<dd>boolean to specify if
the weighted moments dataframe is computed again</dd>


<dt>randomization_number</dt>
<dd>the number of random abundance matrix to
generate</dd>


<dt>slope_distance</dt>
<dd>slope of the theoretical distribution
law (default: slope = 1 intercept = 1.86 skew-uniform distribution family)</dd>


<dt>intercept_distance</dt>
<dd>intercept of the theoretical distribution
law (default: slope = 1 intercept = 1.86 skew-uniform distribution family)</dd>

</dl>

weighted moments generation

get_weighted_mnts: weighted moments generation

Description

Usage

Arguments