Description Usage Arguments Value See Also Examples
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.
1 | fusedanova(x, class, ...)
|
x |
matrix (or column vector) which rows represent individuals and columns independant variables. |
class |
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
documentation page fusedanova
for
details.
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...
See also fusedanova
,
plot,fusedanova-method
and
cv.fa
.
1 2 3 4 5 6 7 8 9 10 11 12 | ## 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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