The condition classes deli raises

Where a caller might reasonably want to answer an error or a warning rather than let it stop the work, deli attaches a condition class to it, so that base::tryCatch(), base::withCallingHandlers(), or rlang::try_fetch() can match on the class rather than on the wording of a message. This page lists those classes and says what each of them reports on.

Details

Every class below belongs to an error unless the entry says otherwise. A condition carrying two of them leads with the narrower one, so a handler for either the family or the particular fault catches it.

A condition a user's own estimating function raises reaches the caller with whatever class that function gave it. Nothing here renames one.

What an estimating function returned

• deli_psi_return_error: the estimating function returned something no sandwich can be built from, either at the starting values or at the point handed to compute_sandwich(). Every refusal of a return carries it, so recognizing one is a match on this rather than on four messages.

• deli_psi_shape_error: the number of estimating equations returned cannot be solved against the number of parameters. Leads deli_psi_return_error.

• deli_psi_list_unsupported: the estimating function returned a list of one element per equation. compute_bread() and compute_sandwich() take that shape, and no fit can solve it, so estimate() refuses it rather than failing on the summed equations. Leads deli_psi_return_error.

The estimating equations deli supplies

• deli_mlogit_binary_outcome: the indicator matrix handed to ee_mlogit() holds two columns. Two categories leave a single non-reference residual whose reference residual is its exact negative, so the equations cancel to zero at every value of the parameters and a fit of them reports the values it started from with no variance. A two-level outcome is logistic regression, which ee_glm() fits with distribution = "binomial".

The formula interface

• deli_formula_ee_lookup_error: .ee is neither a function nor the name of one. Either it arrived as some other type entirely, or it arrived as a character vector naming no one function, because no function of that name was found or because the vector is not of length one. A handler that means to read the name it failed to resolve should confirm it has a character vector first.

• deli_formula_ee_signature_error: the arguments of .ee leave something the interface fills nowhere to go: theta, the model matrix it passes as X, the response it passes by position, or the offset an offset() term in the formula supplies. It also covers an equation that writes formals after its ..., which can be filled by name and by nothing else, so the positional response would fall into the dots and leave them unfilled.

• deli_formula_ee_argument_error: something in the ... the caller wrote cannot be forwarded to .ee. On its own it is a name matching no argument of .ee exactly, which is what keeps an abbreviation from reaching an argument the caller did not mean.

• deli_formula_ee_unnamed_argument: an argument was forwarded with no name, and so by position, into whichever argument the response did not take. Leads deli_formula_ee_argument_error.

• deli_formula_ee_reserved_argument: a name the caller supplied is one the interface fills itself, either theta, X, or the argument the response is passed to. Leads deli_formula_ee_argument_error.

• deli_formula_auto_init_error: the init the formula interface generated itself, one zero per model-matrix column, does not fit the estimating equation.

Solving

• deli_solver_not_converged, a warning: the solver stopped without solving the estimating equations, either because it reported a failure of its own or because the point it returned does not solve them.

• deli_nested_solver_error: a rootSolve::multiroot() solve was asked for while another one was running, which it cannot be. An error rather than a warning, because the solve cannot be attempted at all.

• deli_gmm_moments_rejected, a warning: the Hansen J-statistic of an over-identified GMM fit stands so far out against its reference distribution as to all but rule the fit out, which usually means the moment conditions cannot all hold at one value of the parameters.

• deli_gmm_moments_dependent, a warning: the moment conditions are linearly dependent at the estimated values, so the two-step weight matrix is a pseudo-inverse and what comes back is the fit of the independent conditions alone. It also carries the rarer covariance that has no inverse for want of conditioning rather than of rank, which the message tells apart.

• deli_meat_not_invertible: the covariance of the moment conditions cannot be inverted, so the two-step GMM weight update has no weight matrix to take, and allow_pinv = FALSE refused the pseudo-inverse. The counterpart of deli_bread_not_invertible for the other half of the sandwich.

The sandwich variance

• deli_bread_na, a warning: the bread matrix holds NA, so no inverse of it exists. A fit records no variance and carries on; compute_sandwich() has nothing but a matrix to return and converts it into the error below.

• deli_bread_not_invertible: the bread cannot be inverted and allow_pinv = FALSE refused the pseudo-inverse, or it is rectangular and has no inverse at any rank, or it holds NA.

• deli_summed_equations_error: the summed_equations handed to compute_sandwich() or compute_bread(), or carried as a property of MEstimator() or GMMEstimator(), is not one the bread can be differentiated from. It is neither NULL nor a function, or its return at the point in hand is not numeric, or that return does not hold one value per estimating equation. Every refusal of the argument carries it, as does a check_summed_equations that is not a single TRUE or FALSE.

• deli_summed_equations_disagree: the reduction is a function of the right shape and does not sum the stacked_equations it is paired with, either by differing from their row sums or by returning a value that is not a number where they sum to one. The bread is differentiated from the one and the meat is built from the other, so a sandwich assembled from two systems would carry the shape of a covariance matrix without the meaning, and a fit would report the estimates of the other system as well. Leads deli_summed_equations_error.

Differentiation

• deli_exact_unsupported_function: under deriv_method = "exact", a function reached with a tangent-carrying argument has no rule, either because it hands its argument to compiled code without dispatching or because deli declines to differentiate it. auto_differentiation() names the replacements to use instead.

• deli_exact_unsupported_shape: the estimating equations arrived in a container the summing step has no rule for, either because the tangents survived in a shape it cannot reduce or because a per-equation list does not hold one element per parameter, each of one length. Nothing was lost; the shape is the problem.

• deli_exact_tangent_lost: derivative information is gone or would be, either because a tangent-carrying value was asked to become a plain double or because a result arrived with no tangent and evidence that one had been dropped on the way.

• deli_finite_difference_lost, a warning: a finite-difference step changed the function by less than the floating-point spacing of its values, so an entry of the Jacobian carries none of the digits of the values it came from.

Parameter names

• deli_param_name_collision: filling the unnamed entries of a partly named parameter vector with positional theta_1, theta_2, ... labels would repeat a label another parameter in the same vector already carries.