View source: R/like_inference.R
array_bic_aic | R Documentation |
Calculate the AIC and BIC for Kronecker structured covariance models, assuming the array normal distribution.
array_bic_aic( sig_squared, p, mode_ident = NULL, mode_diag = NULL, mode_unstructured = NULL )
sig_squared |
A numeric. The MLE of sigma^2 in the array normal model (the 'variance' form of the total variation parameter). |
p |
A vector of integers. The dimension of the data array (including replication modes). |
mode_ident |
A vector of integers. The modes assumed to have identity covariances. |
mode_diag |
A vector of integers. The modes assumed to have diagional covariances. |
mode_unstructured |
A vector of integers. The modes of assumed to have unstructured covariances. |
The AIC and BIC depend only on the data through the MLE of the total variation parameter. Given this, the dimension of the array, and a specification of which modes are the identity and which are unstructured, this function will calculate the AIC and BIC.
AIC
A numeric. The AIC of the model.
BIC
A numeric. The BIC of the model.
David Gerard.
holq
for obtaining sig_squared
.
# Generate random array data with first mode having unstructured covariance # second having diagonal covariance structure and third mode having identity # covariance structure. set.seed(857) p <- c(4, 4, 4) Z <- array(stats::rnorm(prod(p)), dim = p) Y <- atrans(Z, list(tensr:::rwish(diag(p[1])), diag(1:p[2]), diag(p[3]))) # Use holq() to fit various models. false_fit1 <- holq(Y, mode_rep = 1:3) ## identity for all modes false_fit2 <- holq(Y, mode_rep = 2:3) ## unstructured first mode true_fit <- holq(Y, mode_rep = 3, mode_diag = 2) ## correct model # Get AIC and BIC values. false_aic1 <- array_bic_aic(false_fit1$sig ^ 2, p, mode_ident = 1:length(p)) false_aic2 <- array_bic_aic(false_fit2$sig ^ 2, p, mode_ident = 2:length(p), mode_unstructured = 1) true_aic <- array_bic_aic(true_fit$sig ^ 2, p, mode_ident = 2:length(p), mode_diag = 1) # Plot the results. plot(c(false_aic1$AIC, false_aic2$AIC, true_aic$AIC), type = "l", xaxt = "n", xlab = "Model", ylab = "AIC", main = "AIC") axis(side = 1, at = 1:3, labels = c("Wrong Model 1", "Wrong Model 2", "Right Model")) plot(c(false_aic1$BIC, false_aic2$BIC, true_aic$BIC), type = "l", xaxt = "n", xlab = "Model", ylab = "BIC", main = "BIC") axis(side = 1, at = 1:3, labels = c("Wrong Model 1", "Wrong Model 2", "Right Model"))
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