Compute GLM on community data (version 0.0.2)

Input metrics file:

Metrics data file, with location, year, and metrics informations that can be used as interest variable.

Unitobs informations file:

Unitobs file, with all informations available about unitobs.

Response variable from metrics file:

Choose the field of the response variable.

Separation factor of your analysis from unitobs file:

Choose the field of the separation factor, for each level of this factor, one GLM will be computed.

Explanatory variables:

Choose the explanatory variables you want to include in your analysis.

Random effect ?:

Allocate a random effect on site or year makes your model more reliable as random events on a peculiar site or year can affect populations, it takes account of pseudoreplication. However, avoid applying it on a less than 10 levels variable (less than 10 different sites and/or year).

Specify advanced parameters:

Compute GLM on community data with selected interest variables

What it does

This tool from PAMPA toolsuite computes Generalized Linear Models on community data.

It allows user to choose composition of the model :

Response variable among numeric or integer variables of the input file
Explanatory variables among year, site and/or habitat
Allocation of random effect on year and/or site
(optional) Separation factor to compute several GLMs

Input description

A tabular file with community data. Must at least contain two or three columns depending on the case :

['year' and 'location'] or ['observation.unit']
At least one community metric

observation.unit	metric1	metric2	...
year_locationID	2	0.4	...
...	...	...	...

year	location	metric1	metric2	...
2000	locationID	2	0.4	...
...	...	...	...	...

The first input may be extracted from the 'Calculate community metrics' tool.

A tabular file with observation unit data which contains at least as much columns as used explanatory variables and separation factor in addition with the 'observation.unit' column.

observation.unit	site	year	habitat	...
year_locationID	site ID	2000	habitatID	...
...	...	...	...	...

The site may represent the same entity as the location or represent an aggregation of several locations

Output

Three text files :

A first tabular file with GLM results. When a separation factor is selected, one analysis is computed for every level and the last analysis is on the whole dataset.
A second text file with simple statistics on the whole dataset.
A third text file with ratings of your analysis based on several criterias.

Source

Derived from PAMPA scripts (https://wwz.ifremer.fr/pampa/Meth.-Outils/Outils) written by Yves Reecht.