generate_stat_per_rand

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 

generate_stat_per_rand function

<dl><dt>weights</dt>
<dd>the dataframe of weights, one row correspond to a
series of observation</dd>
<dt>statistics_factor_name</dt>
<dd>vector of factor name for the
computation of statistics for each generated matrix</dd>
<dt>weights_factor</dt>
<dd>the dataframe which contains the different
factor linked to the weights</dd>
<dt>randomization_number</dt>
<dd>the number of random abundance matrix to
generate</dd>
<dt>lin_mod</dt>
<dd>Indicates the type of linear model to use for
(SKR): choose "lm" or "mblm"</dd></dl>

Arguments

stats per random generation — generate_stat_per_rand

<dl>

<dt>weights</dt>
<dd>the dataframe of weights, one row correspond to a
series of observation</dd>


<dt>statistics_factor_name</dt>
<dd>vector of factor name for the
computation of statistics for each generated matrix</dd>


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


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


<dt>lin_mod</dt>
<dd>Indicates the type of linear model to use for
(SKR): choose "lm" or "mblm"</dd>

</dl>

stats per random generation

generate_stat_per_rand: stats per random generation

Description

Usage

Arguments