summarylr {glmglrt}

Summarizes a glm, adding a column of GLRT or Rao score P-values

Description

summarylr is an improved summary function for standard glm (stats package) adding LRT or Rao score P-values

Usage

summarylr(
  object,
  dispersion = NULL,
  correlation = FALSE,
  symbolic.cor = FALSE,
  ...,
  force = FALSE,
  debuglevel = level_warning,
  method = "LRT",
  keep.wald = FALSE
)

Arguments

object

glm object; as obtained by calling stats::glm or MASS::glm.nb.

dispersion

the dispersion parameter for the family used. Either a single numerical value or NULL (the default), when it is inferred from object (see stats::summary.glm).

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed (see stats::summary.glm).

symbolic.cor

logical; if TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers (see stats::summary.glm).

...

Additional arguments to be passed to stats::summary.glm

force

logical; if TRUE, force computation of P-values in case of convergence problems.

debuglevel

integer value; set to 0 (default) to disable warnings, 1 to enable warnings and 2 to enable warnings and notes.

method

NULL or character vector of length 0, 1 or 2; may be code"LRT" or "Rao" or c("LRT", "Rao") to compute specified P-values. You can set method=NULL to compute no additional P-values.

keep.wald

logical; if TRUE, the standard Wald's P-values are kept in the output of print.summary.glmglrt. Even if keep.wald=FALSE, the standard wald P-values are not erased from the summary.glmglrt object. They are only hidden by print.summary.glmglrt.

Details

This function works the same as the standard summary.glm function but provides additionnal parameters The core parameter method="LRT" makes summarylr adds a column LRT P-value to the output. This P-value is computed by repeatdly fitting the model dropping one coefficient at a time and using the anova.glm(test="Chisq") function to perform generalized likelihood ratio test by approximation of the deviance difference to a chi-square distribution. This provides P-values less biased than the standard Wald P-values that summary provides. Moreover, this LRT method is consistent with the profile likelihood confidence intervals that confint.glm provides. The option method="Rao" generates Rao's score P-values. method="Chisq" is synonymous to method="LRT". For exhaustivity, the option method="Wald" (synonym "wald", "F") generates Wald's P-values. Several methods can be used, e.g. method=c("LRT","Rao") computes both LRT and Rao P-values. New methods may be added in the future.

Extra parameters are passed-through to the summary.glm function.

Value

It returns a summary object of type summary.glmglrt that gets pretty printed by link[glmglrt:print.summary.glmglrt]{print.summary.glmglrt} The return value is an S3 object compatible with stats::summary.glm but with an additional field $extra field having sub-fields. $extra$pvalues is a numeric matrix with columns "LRT P-value" and/or "Rao P-value", containing the relevant P-values. As new columns may be added in future, you should rely on column names rather than column indexes. Only P-values of methods requested in the method parameter are stored in this matrix. $extra$debuglevel is equal to the debuglevel passed to summarylr. $extra$keep.wald is equal to the keep.wald passed to summarylr. In case of convergence problems, the field $extra$problem_of_convergence will be added. It will be a character string with the value "general" (because model$converged = FALSE), "all" (because all coefficients have huge variances) or "specific" (because at least one coefficient has a huge variance). Other problem strings may be added in the future. If weights are specified in a way that make P-values invalid, the field $extra$problem_weights will be added as a character string describing the problem. Actually, the only known problem is "non-constant".

See Also

Other Extended GLM summary functions: override_summary(), print.summary.glmglrt()

Examples

summarylr(glm(family="binomial", cbind(5,3)~1))
data(mtcars)
# do not properly converge (warnings)
mtcars$outcome = mtcars$disp > median(mtcars$disp)
mod=glm(family=binomial(log), data=mtcars,outcome ~ 0+qsec+wt,start=c(-0.1,0.3))
summarylr(mod) # warns that P-values are not computed because model did not converge
summarylr(mod, force=TRUE) # compute P-values anyway !
# also works with negative binomial models
summarylr(MASS::glm.nb(data=mtcars, I(cyl*gear) ~ 1+wt,link="sqrt"),test="LRT")