logistftest {logistf} | R Documentation |
This function performs a penalized likelihood ratio test on some (or all) selected factors. The resulting object is of the class logistftest and includes the information printed by the proper print method.
logistftest(formula=attr(data, "formula"), data=sys.parent(), test, values, maxit = 25, maxhs=5, epsilon = .0001, maxstep = 10, firth=TRUE, beta0)
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 model outcome, where the higher value (1 or TRUE) is modeled. It's possible
to include contrasts, interactions, nested effects, cubic or polynomial splines and all
S features as well, e.g. Y ~ X1*X2 + ns(X3, df=4) .
|
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. |
test |
righthand formula of parameters to test (e.g. ~ B +
D - 1 ). As default all parameter apart from the intercept are tested.
If the formula includes -1, the intercept is omitted from testing.
As alternative to the formula one can give the indexes of the
ordered effects to test (a vector of integers). To test only the
intercept specify test = ~ - . or test = 1 .
|
values |
null hypothesis values, default values are 0. For
testing the specific hypothesis B1=1, B4=2, B5=0 we specify test= ~
B1 + B4 + B5 - 1 and values=c(1, 2, 0) . |
maxit |
maximum number of iterations (default value is 25) |
maxhs |
maximum number of step-halvings per iterations (default value is 5) |
epsilon |
specifies the maximum allowed change in penalized log likelihood to declare convergence. Default value is 0.0001. |
maxstep |
specifies the maximum change of (standardized) parameter values allowed in one iteration. Default value is 5. |
firth |
use of Firth's (1993) penalized maximum likelihood (firth=TRUE , default) or the
standard maximum likelihood method (firth=FALSE ) for the logistic regression. Note
that by specifying pl=TRUE and firth=FALSE (and probably a lower number of iterations)
one obtains profile likelihood confidence intervals for maximum likelihood logistic
regression parameters. |
beta0 |
specifies the initial values of the coefficients for the fitting algorithm. |
This function performs a penalized likelihood ratio test on some (or all) selected factors. The resulting object is of the class logistftest and includes the information printed by the proper print method. Further documentation can be found in Heinze & Ploner (2004).
The object returned is of the class logistf and has the following attributes:
testcov |
a vector of the fixed values of each covariate; NA stands for a parameter which is not tested. |
loglik |
a vector of the (penalized) log-likelihood of the full and the restricted models. If the argument beta0 not missing, the full model isn't evaluated. |
df: the number of degrees of freedom in the model. |
|
prob |
the p-value of the test. |
call |
the call object |
method |
depending on the fitting method ‘Penalized ML’ or ‘Standard ML’. |
beta |
the coefficients on the restricted solution. |
Firth D (1993). Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38.
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
logistf, logistfplot
data(sex2) logistftest(case ~ age+oc+vic+vicl+vis+dia, sex2, test = ~ vic + vicl - 1, values = c(2, 0))