Description Usage Arguments Value Note Author(s) See Also Examples
The function generates Linear Models (LM) with all possible combinations of all variables included in the full model. Each model is a GLM of family gaussian with the response variable modeled by one or more independent variables (Y= a+X1b1+e, Y= a+X1b1+X2b2+e, and so on).
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response |
A data frame containing the response variable. |
variables |
A data frame containing all the independent variables for the models. |
subset |
Maximum number of independent variables to be considered in each model. |
type |
Information criterion to be used "AIC","BIC","AICc","qAIC" and "qAICc" (Default type = "AICc"). |
only_intercept |
Logical argument (TRUE or FALSE) to specify if a model containing only the intercept should be included (Default only_intercept = FALSE). |
importance |
Logical argument (TRUE or FALSE) to specify if the relative importance of variables should be calculated. (Default importance = TRUE). |
... |
Other parameters for the respective functions. In allmodels function the parameters are for the glm function. |
x |
An object of class allmodels. |
n |
Number of model for print. |
object |
An object of class allmodels. |
Envir_class |
The class of each variable. |
N_models |
The number of models. |
IC |
A data frame containing the information criterion, the number of parameters, difference in IC from minimum - IC model and the weights IC. The same that function ICtab. |
Models |
A list with all models. Each model is the class glm. |
ImpValues |
Relative importance of each variable. |
If the model with only the intercept is include, this will be the last model.
Vanderlei Julio Debastiani <vanderleidebastiani@yahoo.com.br>
1 2 3 4 5 6 7 8 | #require(vegan)
#data(mite)
#data(mite.pcnm)
#response<-rowSums(mite)
#Res<-allmodels(response,mite.pcnm,subset=3)
#Res
#summary(Res)
#Res$Models$Model_1157
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