drop1.HLfit: Drop all possible single fixed-effect terms from a model

In spaMM: Mixed-Effect Models, with or without Spatial Random Effects

Drop all possible single fixed-effect terms from a model

Description

Drop single terms from the model. The drop1 method for spaMM fit objects is conceived to replicate the functionality, output format and details of pre-existing methods for similar models. Results for LMs and GLMs should replicate base R drop1 results, with some exceptions:
* somewhat stricter default check of non-default scope argument;
* Because the dispersion estimates for Gamma GLMs differ between stats::glm and spaMM fits (see Details in method), some tests may differ too; results from spaMM REML fits being closer than ML fits to those from glm() fits;
* AIC values reported in tables are always the marginal AIC as computed by AIC.HLfit, while drop1.glm may report confusing (to me, at least) values (see AIC.HLfit) for reasons that seem to go beyond differences in dispersion estimates.

For LMMs, ANOVA tables are provided by interfacing lmerTest::anova (with non-default type).

For other classes of models, a table of likelihood ratio tests is returned, each test resulting from a call to LRT.

Usage

## S3 method for class 'HLfit'
drop1(object, scope, method="", check_marg=NULL, check_time=60, ...)

Arguments

object	Fit object returned by a spaMM fitting function.
scope	Default “scope” (terms to be tested) is determined by applying stats::drop.scope on fixed-effect terms. Non-default scope can be specified a formula giving the terms to be considered for dropping. It is also possible to specify them as a character vector, but then oen has to make sure that the elements are consistent with term labels produced by terms, as inconsistent elements will be ignored.
method	Only non-default value is "LRT" which forces evaluation of a likelihood ratio tests by LRT, instead of specific methods for specific classes of models.
check_marg	NULL or boolean: whether effects should be checked for marginality. By default, this check is performed when a non-default scope is specified, and then no test is reported for terms that do not satisfy the marginality condition. If check_marg=FALSE, marginality is not checked and tests are always performed.
check_time	numeric: whether to output some information when the execution time of drop1 may be of the order of the time specified by check_time, or more. This is checked only when random effect are present. Such output can thus be suppressed by check_time=Inf.
...	Further arguments passed from or to methods, or to LRT.

Details

As for the ANOVA-table functionality, it has been included here mainly to provide access to F tests (including, for LMMs, the “Satterthwaite method”, using pre-existing procedures as template or backend for expediency and familiarity. The procedures for specific classes of models have various limitations, e.g., none of them handle models with variable dispersion parameter. For classes of models not well handled by these procedures (by design or due to the experimental nature of the recent implementation), method="LRT" can still be applied (and will be applied by default for GLMMs).

Value

The return format is that of the function called (lmerTest::drop1 for LMMs), or emulated (base drop1 methods for LMs or GLMs), or is a data frame whose rows are each the result of calling LRT.

See Also

as_LMLT for the interface to lmerTest::drop1.

Examples

data("wafers")
#### GLM

wfit <- fitme(y ~ X1+X2+X1*X3+X2*X3+I(X2^2),  family=Gamma(log), data=wafers)
drop1(wfit, test = "F")              
drop1(wfit, test = "F", scope= ~ X1 +  X1 * X3 )  # note the message!

#### LMM
if(requireNamespace("lmerTest", quietly=TRUE)) {
  lmmfit <- fitme(y ~X1+X2+X1*X3+X2*X3+I(X2^2)+(1|batch),data=wafers)
  drop1(lmmfit) # => Satterthwaite method here giving p-values quite close to 
                #    traditional t-tests given by:
  summary(lmmfit, details=list(p_value=TRUE)) 
}

#### GLMM 
wfit <- fitme(y ~ X1+X2+X1*X3+X2*X3+I(X2^2)+(1|batch), family=Gamma(log),
              rand.family=inverse.Gamma(log), resid.model = ~ X3+I(X3^2) , data=wafers)
drop1(wfit)              
drop1(wfit, scope= ~ X1 +  X1 * X3 )  # note the message!

spaMM documentation built on Aug. 30, 2023, 1:07 a.m.