glm_ai_plus: Fit a Generalized Linear Models on abundance data

In polehalieutique/demerstem: This package aims to falicitate Fish stock assessement for west african partners involved in the EU DEMERSTEM project

Fit a Generalized Linear Models on abundance data

Description

glm_ai_plus returns a GLM model on abundance data. It can run a StepAIC if specified. It runs the GLMs, plot the residuals analysis graphs and return the GLM outputs.

Usage

glm_ai_plus(tableau_ab, parameters, formula_select, summary = FALSE, type = 2)

Arguments

tableau_ab	table of abundance data, extracted from the output of table_pres_abs
parameters	list of parameters to test
formula_select	if "auto", the function select which formula as the lowest AIC. Else, run the selected formula.
summary	To show residuals plots.
type	To study marginal effects with Anova type III in case of interaction

Value

glm_ai_plus can either return the best GLM model based on AIC comparison, or return the outputs of the GLM based on the formula given in formula_select

Examples

data(tableau_sc)
list_param <- c("annee", "saison")
table_ex <- table_pres_abs(tableau_sc, esp="PSEUDOTOLITHUS ELONGATUS", list_param=c("annee", "saison", "strate"), espece_id='nom_taxonomique', var_eff_list=c("surface_chalutee"), catch_col='total_capture', limit=0.0001)
table_ex_abundance <- filter(table_ex, presence==1)
param <- param_use(table_ex_abundance, list_param)
for (i in 1:length(param)){
table_ex_abundance[,param[i]] <- as.factor(table_ex_abundance[,param[i]])
table_ex_abundance[,param[i]] <- droplevels(table_ex_abundance[,param[i]])
contrasts(table_ex_abundance[,param[i]]) <- contr.sum(levels(table_ex_abundance[,param[i]]))
}
glm_ai_plus(table_ex_abundance, param, formula_select = "auto")

polehalieutique/demerstem documentation built on Aug. 4, 2024, 5:12 a.m.