logistf(formula = attr(data, "formula"), data = sys.parent(), pl = TRUE, alpha = 0.05,
control, plcontrol, firth = TRUE, init, weights, plconf=NULL, ...)pl=TRUE, the default) or on the Wald method (pl=FALSE).firth=TRUE, default) or the
standard maximum likelihood method (firth=FALSE) for the logistic regression. Note
that by specifying pl=TRUE and firth=FALSEweights.Version 1.10 improves on previous versions by the possibility to include case weights and offsets, and better control of the iterative fitting algorithm.
The call of the main function of the library follows the structure of the standard functions as lm or glm, requiring a data.frame and a formula for the model specification. The resulting object belongs to the new class logistf, which includes penalized maximum likelihood (`Firth-Logistic'- or `FL'-type) logistic regression parameters, standard errors, confidence limits, p-values, the value of the maximized penalized log likelihood, the linear predictors, the number of iterations needed to arrive at the maximum and much more. Furthermore, specific methods for the resulting object are supplied. Additionally, a function to plot profiles of the penalized likelihood function and a function to perform penalized likelihood ratio tests have been included.
In explaining the details of the estimation process we follow mainly the description in Heinze & Ploner (2003). In general, maximum likelihood estimates are often prone to small sample bias. To reduce this bias, Firth (1993) suggested to maximize the penalized log likelihood $\log L(\beta)^* = \log L(\beta) + 1/2 \log |I(\beta)|$, where $I(\beta)$ is the Fisher information matrix, i. e. minus the second derivative of the log likelihood. Applying this idea to logistic regression, the score function $U(\beta)$ is replaced by the modified score function $U(\beta)^* = U(\beta) + a$, where $a$ has $r$th entry $a_r = 0.5tr{I(\beta)^{-1} [dI(\beta)/d\beta_r]}, r = 1,...,k$. Heinze and Schemper (2002) give the explicit formulae for $I(\beta)$ and $I(\beta)/d \beta_r$.
In our programs estimation of $\beta$ is based on a Newton-Raphson algorithm. Parameter values are initialized usually with 0, but in general the user can specify arbitrary starting values.
With a starting value of $\beta^{(0)}$, the penalized maximum likelihood estimate $\beta$ is obtained iteratively:
$$\beta^{(s+1)}= \beta^{(s)} + I(\beta^{(s)})^{-1} U(\beta^{(s)})^*$$
If the penalized log likelihood evaluated at $\beta^{(s+1)}$ is less
than that evaluated at $\beta^{(s)}$ , then ($\beta^{(s+1)}$ is
recomputed by step-halving. For each entry $r$ of $\beta$ with
$r = 1,...,k$ the absolute step size $|\beta_r^{(s+1)}-\beta_r^s|$
is restricted to a maximal allowed value maxstep. These two means should avoid
numerical problems during estimation. The iterative process is continued
until the parameter estimates converge, i. e., until three criteria are met: the change in log likelihood is less than lconv,
the maximum absolute element of the score vector is less than gconv, the maximum absolute change in beta is less than xconv.
lconv, gconv, xconv can be controlled by control=logistf.control(lconv=..., gconv=..., xconv=...).
Computation of profile penalized likelihood confidence intervals for
parameters (logistpl) follows the algorithm of Venzon and
Moolgavkar (1988). For testing the hypothesis of $\gamma =
\gamma_0$, let the likelihood ratio statistic
$$LR = 2 [ \log L(\gamma, \delta) - \log L(\gamma_0,\delta_{\gamma_0})^*]$$
where $(\gamma, \delta)$ is the joint penalized maximum likelihood estimate of $\beta= (\gamma,\delta)$, and $\delta_{\gamma_0}$ is the penalized maximum likelihood estimate of $\delta$ when $\gamma= \gamma_0$. The profile penalized likelihood confidence interval is the continuous set of values $\gamma_0$ for which $LR$ does not exceed the $(1 - \alpha)100$th percentile of the $\chi^2_1$-distribution. The confidence limits can therefore be found iteratively by approximating the penalized log likelihood function in a neighborhood of $\beta$ by the quadratic function
$$l(\beta+\delta) = l(\beta) + \delta'U^* - 0.5 \delta' I \delta$$
where $U^* = U(\beta)^*$ and $-I = -I(\beta)$.
In some situations computation of profile penalized likelihood confidence intervals may be time consuming since the iterative procedure outlined above has to be repeated for the lower and for the upper confidence limits of each of the k parameters. In other problems one may not be interested in interval estimation, anyway. In such cases, the user can request computation of Wald confidence intervals and P-values, which are based on the normal approximation of the parameter estimates and do not need any iterative estimation process. Standard errors $\sigma_r, r = 1,...,k$, of the parameter estimates are computed as the roots of the diagonal elements of the variance matrix $V(\beta) = I(\beta)^{-1}$ . A $100(1 - \alpha)$ per cent Wald confidence interval for parameter $\beta_r$ is then defined as $[\beta_r + \Psi_{\alpha/2}\sigma_r, \beta_r+\Psi_{1-\alpha/2}\sigma_r]$ where $\Psi_{\alpha}$ denotes the $\alpha$-quantile of the standard normal distribution function. The adequacy of Wald confidence intervals for parameter estimates should be verified by plotting the profile penalized log likelihood (PPL) function. A symmetric shape of the PPL function allows use of Wald intervals, while an asymmetric shape demands profile penalized likelihood intervals (Heinze & Schemper (2002)). Further documentation can be found in Heinze & Ploner (2004).
Heinze G, Schemper M (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine 21: 2409-2419.
Heinze G, Ploner M (2003). Fixing the nonconvergence bug in logistic regression with SPLUS and SAS. Computer Methods and Programs in Biomedicine 71: 181-187.
Heinze G, Ploner M (2004). Technical Report 2/2004: A SAS-macro, S-PLUS library and
R package to perform logistic regression without convergence problems.
Section of Clinical Biometrics, Department of
Medical Computer Sciences, Medical University of Vienna, Vienna, Austria.
Venzon DJ, Moolgavkar AH (1988). A method for computing profile-likelihood based confidence intervals. Applied Statistics 37:87-94.
data(sex2)
fit<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sex2)
summary(fit)
data(sexagg)
fit2<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sexagg, weights=COUNT)
summary(fit2)Run the code above in your browser using DataLab