The **enrichwith** package
provides the `enrich`

method to enrich list-like R objects with new,
relevant components. The resulting objects preserve their class, so
all methods associated with them still apply.

This vignette is a short case study demonstrating how enriched `glm`

objects can be used to implement a *quasi Fisher
scoring* procedure
for computing reduced-bias estimates in generalized linear
models
(GLMs). @kosmidis:10 describe a parallel quasi
Newton-Raphson
procedure.

@heinze:02 used a logistic regression model to analyse data from a study on endometrial cancer. @agresti:15 [Section 5.7] provide details on the data set. Below, we fit a probit regression model with the same linear predictor as the logistic regression model in @heinze:02.

# Get the data from the online supplmementary material of Agresti (2015) endometrial_url <- url("http://www.stat.ufl.edu/~aa/glm/data/Endometrial.dat") endometrial <- read.table(endometrial_url, header = TRUE) modML <- glm(HG ~ NV + PI + EH, family = binomial("probit"), data = endometrial) theta_mle <- coef(modML) summary(modML)

As is the case for the logistic regression in @heinze:02, the maximum
likelihood (ML) estimate of the parameter for `NV`

is actually
infinite. The reported, apparently finite value is merely due to false
convergence of the iterative estimation procedure. The same is true
for the estimated standard error, and, hence the value ```
r
round(coef(summary(modML))["NV", "z value"], 3)
```

for the $z$-statistic
cannot be trusted for inference on the size of the effect for `NV`

.

In categorical-response models like the above, the bias reduction method in @firth:93 has been found to result in finite estimates even when the ML ones are infinite [see, @heinze:02, for logistic regressions; @kosmidis:11, for multinomial regressions; @kosmidis:14a, for cumulative link models].

One of the variants of that bias reduction method is implemented in
the
**brglm** R
package, which estimates binomial-reponse GLMs using iterative ML fits
on binomial pseudo-data [see, @kosmidis:07, Chapter 5, for
details]. The reduced-bias estimates for the probit regression on the
endometrial data can be computed as follows.
``` {r, echo = TRUE, eval = TRUE, messages = FALSE}
library("brglm")
modBR <- brglm(HG ~ NV + PI + EH, family = binomial("probit"), data = endometrial)
theta_brglm <- coef(modBR)
summary(modBR)

The $z$-statistic for `NV` has value `r round(coef(summary(modBR))["NV", "z value"], 3)` when based on the reduced-bias estimates, providing some evidence for the existence of an effect. In the following, we use **enrichwith** to implement two variants of the bias reduction method via a unifying quasi Fisher scoring estimation procedure. ------ # Quasi Fisher scoring for bias reduction Consider a parametric [statistical model](https://en.wikipedia.org/wiki/Statistical_model) $\mathcal{P}_\theta$ with unknown parameter $\theta \in \Re^p$ and the iteration $$\theta^{(k + 1)} := \theta^{(k)} + \left\{i(\theta^{(k)})\right\}^{-1} s(\theta^{(k)}) - c(\theta^{(k)}) b(\theta^{(k)})$$ where $\theta^{(k)}$ is the value of $\theta$ at the $k$th iteration, $s(\theta)$ is the gradient of the log-[likelihood](https://en.wikipedia.org/wiki/Likelihood_function) for $\mathcal{P}_\theta$, $i(\theta)$ is the [expected information](https://en.wikipedia.org/wiki/Fisher_information) matrix, and $b(\theta)$ is the $O(n^{-1})$ term in the expansion of the [bias](https://en.wikipedia.org/wiki/Bias_of_an_estimator) of the [ML estimator](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) of $\theta$ [see, for example, @cox:68]. The above iteration defines a *quasi* Fisher scoring estimation procedure in general, and reduces to exact Fisher scoring for ML estimation when $c(\theta^{(k)}) = 0_p$, where $0_p$ is a vector of $p$ zeros. For either $c(\theta) = I_p$ or $c(\theta) = \left\{i(\theta)\right\}^{-1} j(\theta)$, where $I_p$ is the $p \times p$ identity matrix and $j(\theta)$ is the [observed information](https://en.wikipedia.org/wiki/Observed_information), $\theta^{(\infty)}$ (if it exists) is a reduced-bias estimate, in the sense that it corresponds an estimator with bias of smaller asymptotic order than that of the ML estimator [see, @firth:93; @kosmidis:10]. The **brglm** estimates correspond to $c(\theta) = I_p$. The asymptotic distribution of the reduced-bias estimators is the same to that of the ML estimator [see, @firth:93 for details]. So, the reduced-bias estimates can be readily used to calculate $z$-statistics. ------ # Implementation using **enrichwith** For implementing the iteration for bias reduction, we need functions that can compute the gradient of the log-likelihood, the observed and expected information matrix, and $b(\theta)$ at arbitrary values of $\theta$. The **enrichwith** package can produce those functions for any `glm` object through the `auxiliary_functions` enrichment option (to see all available enrichment options for `glm` objects run `get_enrichment_options(modML)`. ``` {r, echo = TRUE, eval = TRUE} library("enrichwith") enriched_modML <- enrich(modML, with = "auxiliary functions")

Let's extract the functions from the `enriched_modML`

object.
``` {r, echo = TRUE, eval = TRUE}

gradient <- enriched_modML$auxiliary_functions$score # gradient of the log-likelihood information <- enriched_modML$auxiliary_functions$information # information matrix bias <- enriched_modML$auxiliary_functions$bias # first-order bias

For the more technically minded, note here that the above functions are specific to `modML` in the sense that they look into a special environment for necessary objects like the model matrix, the model weights, the response vector, and so on. This stems from the way **enrichwith** has been implemented. In particular, if `create_enrichwith_skeleton` is used, the user/developer can directly implement enrichment options to enrich objects with functions that directly depend on other components in the object to be enriched. The following code chunk uses `enriched_modML` to implement the quasi Fisher scoring procedure for the analysis of the endometrial cancer data. For `p <- length(theta_mle)` `r p <- length(theta_mle)`, the starting value for the parameter vector is set to `theta_current <- rep(0, p)` `r theta_current <- rep(0, p)`, and the maximum number of iterations to `maxit <- 100` `r maxit <- 100`. As stopping criterion, we use the absolute change in each parameter value with tolerance `epsilon <- 1e-06` `r epsilon <- 1e-06` ``` {r, echo = TRUE, eval = TRUE} # The quasi Fisher scoring iteration using c(theta) = identity for (k in seq.int(maxit)) { s_vector <- gradient(theta_current) i_matrix <- information(theta_current, type = "expected") b_vector <- bias(theta_current) step <- solve(i_matrix) %*% s_vector - b_vector theta_current <- theta_current + step # A stopping criterion if (all(abs(step) < epsilon)) { break } } (theta_e <- drop(theta_current))

The estimation procedure took `r k`

iterations to converge, and, as expected, the estimates are numerically the same to the ones that **brglm** returned.
``` {r, echo = TRUE, eval = TRUE}
all.equal(theta_e, theta_brglm, check.attributes = FALSE, tolerance = epsilon)

A set of alternative reduced-bias estimates can be obtained using $c(\theta) = \left\{i(\theta)\right\}^{-1} j(\theta)$. Starting again at `theta_current <- rep(0, p)` `r theta_current <- rep(0, p)` ``` {r, echo = TRUE, eval = TRUE} # The quasi Fisher scoring iteration using c(theta) = solve(i(theta)) %*% j(theta) for (k in seq.int(maxit)) { s_vector <- gradient(theta_current) i_matrix <- information(theta_current, type = "expected") j_matrix <- information(theta_current, type = "observed") b_vector <- bias(theta_current) step <- solve(i_matrix) %*% (s_vector - j_matrix %*% b_vector) theta_current <- theta_current + step # A stopping criterion if (all(abs(step) < epsilon)) { break } } (theta_o <- drop(theta_current))

The estimation procedure took `r k`

iterations to converge.

The ML estimates and the estimates from the two variants of the bias reduction method are ``` {r, echo = TRUE, eval = TRUE} round(data.frame(theta_mle, theta_e, theta_o), 3)

Note that the reduced-bias estimates have shrunk towards zero. This is typical for reduced-bias estimation in binomial-response GLMs [see, for example, @cordeiro:91, Section 8; @kosmidis:07, Section 5.2; @kosmidis:14a for shrinkage in cumulative link models]. The corresponding $z$-statistics are ``` {r, echo = TRUE, eval = TRUE} se_theta_mle <- sqrt(diag(solve(information(theta_mle, type = "expected")))) se_theta_e <- sqrt(diag(solve(information(theta_e, type = "expected")))) se_theta_o <- sqrt(diag(solve(information(theta_o, type = "expected")))) round(data.frame(z_mle = theta_mle/se_theta_mle, z_br_e = theta_e/se_theta_e, z_br_o = theta_o/se_theta_o), 3)

The two variants for bias reduction result in slightly different reduced-bias estimates and $z$-statistics, though the $z$-statistics from both variants provide some evidence for the existence of an effect for `NV`

.

A general family of bias reduction methods is described in @kosmidis:09.

The quasi Fisher scoring iteration that has been described here is at the core of the **brglm2** R package, which provides various bias reduction methods for GLMs.

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