Description Usage Arguments Value Author(s) References See Also Examples
The function extract details of information criterion (IC, e.g. AIC, AICc) allowing calculate difference in IC from minimum - IC model (delta), the Akaike weights and the sum of Akaike weights by each explanatory variable (relative importance of each variable).
1 |
x |
A data frame or matrix containing the information criterion for each model. The information criterion in the first column and the models in rows. See examples. |
variables |
A data frame or matrix containing the indication of all independent variables used in each model. The models in row and variables in columns. This must be binary, where 1 indicate presence of variable in respective model and 0 the ausence of the variable in respective model. See examples (default variables = NULL). |
order |
Logical argument (TRUE or FALSE) to specify if informtion criterion is sorted (Default order = TRUE). |
A list with:
call |
The arguments used. |
IC |
A data frame containing the information criterion, difference in IC from minimum - IC model (delta) and the weights IC. |
IValue |
Relative importance of each variable. |
Vanderlei Julio Debastiani <vanderleidebastiani@yahoo.com.br>
Anderson, D.R. 2008. Model Based Inference in the Life Sciences: A Primer on Evidence. Springer-Verlag New York.
1 2 3 4 5 6 7 8 9 10 11 12 | my.aic <- matrix(c(4,2,3,5),4,1)
colnames(my.aic) <- "AIC"
rownames(my.aic) <- rownames(my.aic, do.NULL = FALSE, prefix = "M.")
my.aic
ICdetails(my.aic)
my.models.by.variables<-matrix(c(1,0,0,0,0,1,1,0,0,1,0,1,0,0,0,1),4,4)
colnames(my.models.by.variables) <- colnames(my.models.by.variables,
do.NULL = FALSE, prefix = "Var.")
rownames(my.models.by.variables) <- rownames(my.aic)
my.models.by.variables
ICdetails(my.aic, variables = my.models.by.variables)
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