Trimmed Constraint Matrices
Deletes statistically nonsignficant regression coefficients via their constraint matrices, for future refitting.
trim.constraints(object, sig.level = 0.05, max.num = Inf, ...)
object |
Some VGAM object, especially having
class |
sig.level |
Significance levels, with values in [0, 1].
Columns of constraint matices whose p-values are larger than
this argument are deleted.
With terms that generate more than one column of the
|
max.num |
Numeric, positive and integer-valued.
Maximum number of regression coefficients allowable for deletion.
This allows one to limit the number of deleted coefficients.
For example,
if |
... |
Unused but for provision in the future. |
This utility function is intended to simplify an existing
vglm object having
variables (terms) that affect unnecessary parameters.
Suppose the explanatory variables in the formula
includes a simple numeric covariate called x2.
This variable will affect every linear predictor if
zero = NULL in the VGAM family function.
This situation may correspond to the constraint matrices having
unnecessary columns because their regression coefficients are
statistically nonsignificant.
This function attempts to delete those columns and
return a possibly simplified list of constraint matrices
that can make refitting a simpler model easy to do.
P-values obtained from summaryvglm
(with HDEtest = FALSE for increased speed)
are compared to sig.level to test for
statistical significance.
One can think of this function as facilitating
backward elimination,
especially if max.num = 1,
however usually more than one regression coefficient is deleted
here by default.
A list of possibly simpler constraint matrices
that can be fed back into the model using the
constraints argument
(usually zero = NULL is needed to avoid a warning).
Consequently, they are required to be of the "term"-type.
After the model is refitted, applying
summaryvglm should result in
regression coefficients that are ‘all’ statistically
significant.
This function has not been tested thoroughly.
One extreme is that a term is totally deleted because
none of its regression coefficients are needed,
and that situation has not yet been finalized.
Ideally, object only contains terms where at least
one regression coefficient has a p-value less than
sig.level.
For ordered factors and other situations, deleting
certain columns may not make sense and destroy interpretability.
As stated above, max.num may not work properly
when there are terms that
generate more than one column of the LM model matrix.
However, this limitation may change in the future.
This function is experimental and may be replaced by some other function in the future. This function does not use S4 object oriented programming but may be converted to such in the future.
T. W. Yee
## Not run: data("xs.nz", package = "VGAMdata")
fit1 <-
vglm(cbind(worry, worrier) ~ bs(age) + sex + ethnicity + cat + dog,
binom2.or(zero = NULL), data = xs.nz, trace = TRUE)
summary(fit1, HDEtest = FALSE) # 'cat' is not significant at all
dim(constraints(fit1, matrix = TRUE))
(tclist1 <- trim.constraints(fit1)) # No 'cat'
fit2 <- # Delete 'cat' manually from the formula:
vglm(cbind(worry, worrier) ~ bs(age) + sex + ethnicity + dog,
binom2.or(zero = NULL), data = xs.nz,
constraints = tclist1, trace = TRUE)
summary(fit2, HDEtest = FALSE) # A simplified model
dim(constraints(fit2, matrix = TRUE)) # Fewer regression coefficients
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.