Processes four types of information matrices: ones produced by the SparseM package for the orm function in rms version 6.9-0 and earlier, by the Matrix package for version 7.0-0 of rms using a tri-band diagonal matrix for the intercepts, using Matrix for general sparse information matrices for intercepts (when any interval-censored observations exist), or plain matrices. For Matrix, the input information matrix is a list with three elements: a containing in two columns the diagonal and superdiagonal for intercepts (when there is no interval censoring) or a list with three elements row, col, a (when there is interval censoring), b, a square matrix for the covariates, and ab for intercepts x covariates. If nothing else is specified, the assembled information matrix is returned for Matrix, or the original info otherwise. If p=TRUE, the number of parameters in the model (number of rows and columns in the whole information matrix) is returned. If i is given, the i elements of the inverse of info are returned, using efficient calculation to avoid inverting the whole matrix. Otherwise if invert=TRUE or B is given without i, the efficiently (if Matrix or SparseM) inverted matrix is returned, or the matrix multiplication of the inverse and B. If both i and B are given, what is returned is the i portion of the inverse of the information matrix, matrix multiplied by B. This is done inside solve().
an information matrix object
integer vector specifying elements returned from the inverse. You an also specify i='x' to return non-intercepts or i='i' to return intercepts.
set to TRUE to invert info (implied when i or B is given)
multiplier matrix
set to TRUE to just fetch the total number of parameters (intercepts + betas)
tolerance for matrix inversion singularity
set to FALSE to run the solve calculation through try() without aborting; the user will detect that the operation did not success by examinine inherits(result, 'try-error') for being TRUE.
a single integer or a matrix
When only variance-covariance matrix elements corresponding to the non-intercepts are desired, specify
i='x' or i=(k + 1) : nv where nv is the number of intercepts and slopes combined. infoMxop computes the needed covariance matrix very quickly in this case.
When inverting info, if info has a 'scale' attribute with elements mean and sd, the scaling is reversed after inverting info.