Adjust a penalized ANOVA model with Fused-LASSO (or Total Variation) penality, ie. a sum of weighted l1-norm on the difference of each coefficient. See details below.
matrix (or column vector) which rows represent individuals and columns independant variables.
vector or factor giving the class of each
individual. If missing,
list of additional parameters to overwrite the defaults of the fitting procedure. Include :
an object with class
fusedanova, see the
The optimization problem solved by fused-ANOVA is
where Y_ik is the intensity of a continuous random variable for sample i in condition k and beta_k is the mean parameter of condition k. We denote by K the total number of conditions and n_k the number of sample in each condition.
More details related to the weights are coming...
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## Not run: data(aves) fa.laplace <- fusedanova(x=aves$weight, class=aves$family, weights="laplace", gamma=5) plot(fa.laplace, labels=aves$order) fa.ttest <- fusedanova(x=aves$weight, class=aves$family, weights="naivettest") plot(fa.ttest, labels=aves$order) fa.ada <- fusedanova(x=aves$weight, class=aves$family, weights="adaptive", gamma=2) plot(fa.ada, labels=aves$order) ## End(Not run)
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