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If TRUE the plot showing the relative contribution of the independent variables in the multiple regression model is shown (only if the number of independent variables is greater than two). It is a slow process and, therefore, it is advisable to use a small number of variables (maximum 15).
Plot with the relative contribution of each independent variable of the argument CoVa. It is the number of bootstrap runs requested on boot.relimp (default: b=1000). Make sure to set this to a lower number, if you are simply testing code.
Maps. It is possible to multiply or divide the dataset by a value. For a vector with two values, the first may be 0 (divide) or 1 (multiply), and the second number is the value of the division or multiplication.
It is possible to apply a logarithmic transformation to the dataset. For a vector with two values, the first may be 0 (do not log transform) or 1 (log transformation), and the second number is the value to be added in case of log transformation.
The default is \"hier.part\" in which the contribution of all independent variables to the regression model is calculated by the hierarchical partitioning method (Chevan & Sutherland, 1991) and subsequently between the variables that are above VIF threshold, eliminating those that contribute less to the regression model. Therefore, among the auto-correlated variables, those that best explain the observed variability in the dependent variable are selected. The problem is that one can only use this method if the number of dependent variables, including those log-transformed and untransformed, is not greater than 12. If the number is greater, then the \"vifstep\" method is automatically applied.
If the argument stepwise=TRUE is selected, it means that a regression is performed by steps using Akaike Information Criterion (AIC) to define which are the variables that are excluded from the regression model, since they do not contribute to explain the dependent variable. The criterion of Akaike (AIC) is used to decide how many explanatory variables are chosen, and what they should be. The method of Akaike values the goodness of the model by setting a penalty because of their complexity, so a simpler model is preferable to another with more independent variables that only explains a small additional portion of the variability of the dependent variable. In general AIC=2k-2ln(L), where k is the number of explanatory variables or parameters of the model and L the likelihood or probability associated with the sample used in accordance with model, so that AIC is smaller than the lower the number of variables and the greater the likelihood; between alternative models the one with the lowest value of AIC must be chosen. At each step the variable that, when it is incorporated into the model, makes smaller the value AIC is chosen, until any unused variables allows to reduce it.
In addition to the variables mentioned above, the mean area of occupancy was also included (AOO index, see Guisande et al. 2013 for further details). In this example, the number of variables used was lower than it in Guisande et al. (2013) to shorten the execution of the script. 153554b96e
https://es.outlawai.com/group/supporters/discussion/40b617a6-a0f3-484c-9253-1fe578af5386
https://fr.buddhaceo.org/group/mysite-200-group/discussion/18b5f3c9-f1de-435d-abff-472b2565f2e6