Description Usage Arguments Details Value
Calculate GIC for GIC.FuncompCGL, select beta coefficients vector and df via BIC, AIC, GIC criterion
1 2 3 4 |
p |
number of compositional predictors |
df_list |
vector of df's used in |
GIC_obj |
an object of |
GIC_arg |
argument list used to fit |
cut_type |
cut method for none zero group - default value is |
lower_tri |
lower percentage boundary used in cut none zero groups. |
GIC_type |
variations of GIC's, AIC and BIC. See details. |
method_type |
mehod used to fitted |
y, Zc |
when |
refit |
logical, whehter refit likelihood when small magnitude groups are cut to zeros'
Default value is |
$$GIC(λ) = log(MSE) + Selection * alpha
$$, for normal error.
If include linear constraints, like in log contract and constraints group lasso model,
selection = None zero group - 1; otherwise in naive method without consideration of
linear constraints, selection = None zero group.
alpha_GIC1 = log(max(p*df, n)) * df / n
alpha_GIC2 = log(max(p*df, n)) * df * log(log(n)) / n
alpha_GIC3 = log(p*df) *df /n
alpha_GIC4 = log(p*df) * df * log(log(n)) / n
alpha_BIC = log(n) * df / n
alpha_AIC = 2 * df / n.
cut_off = 'Curve'
, calculate L2 function norm for curves for each compositional covariates
, across df
and lambda
. Consider those betas' with L2 curve norm smaller than
sum(L2_j)*lower_tri as none-selected.
cut_off = 'Matrix'
calculate L2 norm for coefficient matrix by rows, across
df
and lambda
, condiser these betas' with vector L2 norm that is smaller than
srqt(sum(L2_j^2)) * lower_tri.
cut_off = 'Strict'
, strict none zero group, whenever there is none zero entries considered
as selected.
beta |
selected beta vector. |
k_opt |
selected |
GIC_curve |
a |
N_zero |
a matrix of numbers of none zero groups selected. |
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