Implements Firth's bias-Reduced penalized-likelihood logistic regression.

1 2 3 |

`formula` |
a formula object, with the response on the left of the operator, and the
model terms on the right. The response must be a vector with 0 and 1 or FALSE and
TRUE for the outcome, where the higher value (1 or TRUE) is modeled. It is possible
to include contrasts, interactions, nested effects, cubic or polynomial splines and all
S features as well, e.g. |

`data` |
a data.frame where the variables named in the formula can be found, i. e. the variables containing the binary response and the covariates. |

`pl` |
specifies if confidence intervals and tests should be based on the profile penalized
log likelihood ( |

`alpha` |
the significance level (1- |

`control` |
Controls Newton-Raphson iteration. Default is |

`plcontrol` |
Controls Newton-Raphson iteration for the estimation of the profile likelihood confidence intervals.
Default is |

`firth` |
use of Firth's penalized maximum likelihood ( |

`init` |
specifies the initial values of the coefficients for the fitting algorithm. |

`weights` |
specifies case weights. Each line of the input data set is multiplied by the corresponding element of |

`plconf` |
specifies the variables (as vector of their indices) for which profile likelihood confidence intervals should be computed. Default is to compute for all variables. |

`dataout` |
If TRUE, copies the |

`...` |
Further arguments to be passed to logistf. |

`logistf`

is the main function of the package. It fits a logistic regression model applying Firth's correction to the likelihood.
The following generic methods are available for `logistf`

's output
object: `print`

, `summary`

, `coef`

, `vcov`

, `confint`

, `anova`

, `extractAIC`

, `add1`

, `drop1`

, `profile`

, `terms`

, `nobs`

.
Furthermore, `forward`

and `backward`

functions perform convenient variable selection. Note that `anova`

, `extractAIC`

, `add1`

, `drop1`

, `forward`

and `backward`

are based on penalized likelihood ratios.

The object returned is of the class logistf and has the following attributes:

`coefficients` |
the coefficients of the parameter in the fitted model. |

`alpha` |
the significance level (1- the confidence level) as specified in the input. |

`terms` |
the column names of the design matrix |

`var` |
the variance-covariance-matrix of the parameters. |

`df` |
the number of degrees of freedom in the model. |

`loglik` |
a vector of the (penalized) log-likelihood of the full and the restricted models. |

`iter` |
the number of iterations needed in the fitting process. |

`n` |
the number of observations. |

`y` |
the response-vector, i. e. 1 for successes (events) and 0 for failures. |

`formula` |
the formula object. |

`call` |
the call object. |

`terms` |
the model terms (column names of design matrix). |

`linear.predictors` |
a vector with the linear predictor of each observation. |

`predict` |
a vector with the predicted probability of each observation. |

`hat.diag` |
a vector with the diagonal elements of the Hat Matrix. |

`conv` |
the convergence status at last iteration: a vector of length 3 with elements: last change in log likelihood, max(abs(score vector)), max change in beta at last iteration. |

`method` |
depending on the fitting method ‘Penalized ML’ or ‘Standard ML’. |

`method.ci` |
the method in calculating the confidence intervals, i.e. ‘profile likelihood’ or ‘Wald’, depending on the argument pl. |

`ci.lower` |
the lower confidence limits of the parameter. |

`ci.upper` |
the upper confidence limits of the parameter. |

`prob` |
the p-values of the specific parameters. |

`pl.iter` |
only if pl==TRUE: the number of iterations needed for each confidence limit. |

`betahist` |
only if pl==TRUE: the complete history of beta estimates for each confidence limit. |

`pl.conv` |
only if pl==TRUE: the convergence status (deviation of log likelihood from target value, last maximum change in beta) for each confidence limit. |

If `dataout=TRUE`

, additionally:

`data` |
a copy of the input data set |

`weights` |
the weights variable (if applicable) |

Georg Heinze and Meinhard Ploner

Firth D (1993). Bias reduction of maximum likelihood estimates. *Biometrika*
80, 27–38.

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. http://www.meduniwien.ac.at/user/georg.heinze/techreps/tr2_2004.pdf

Heinze G (2006). A comparative investigation of methods for logistic regression
with separated or nearly separated data. *Statistics in Medicine* 25: 4216-4226.

Venzon DJ, Moolgavkar AH (1988). A method for computing profile-likelihood
based confidence intervals. *Applied Statistics* 37:87-94.

`drop1.logistf`

`add1.logistf`

`anova.logistf`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
data(sex2)
fit<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sex2)
summary(fit)
nobs(fit)
drop1(fit)
plot(profile(fit,variable="dia"))
extractAIC(fit)
fit1<-update(fit, case ~ age+oc+vic+vicl+vis)
extractAIC(fit1)
anova(fit,fit1)
data(sexagg)
fit2<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sexagg, weights=COUNT)
summary(fit2)
# simulated SNP example
# not run
set.seed(72341)
snpdata<-rbind(
matrix(rbinom(2000,2,runif(2000)*0.3),100,20),
matrix(rbinom(2000,2,runif(2000)*0.5),100,20))
colnames(snpdata)<-paste("SNP",1:20,"_",sep="")
snpdata<-as.data.frame(snpdata)
for(i in 1:20) snpdata[,i]<-as.factor(snpdata[,i])
snpdata$case<-c(rep(0,100),rep(1,100))
fitsnp<-logistf(data=snpdata, formula=case~1, pl=FALSE)
add1(fitsnp)
fitf<-forward(fitsnp)
fitf
``` |

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