logistf
function in the R package ‘logistf’Adapted from logistf
in the R package ‘logistf’, this is
the same as logistf
except that it provides more decimal places
of p-value that would be useful for Genome-Wide Association Study (GWAS)
or Phenome Wide Association Study (PheWAS).
Logistf(
formula = attr(data, "formula"),
data = sys.parent(),
pl = TRUE,
alpha = 0.05,
control,
plcontrol,
firth = TRUE,
init,
weights,
plconf = NULL,
dataout = TRUE,
...
)
same as logistf
except for providing more decimal places of p-value.
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.
Y ~ X1*X2 + ns(X3, df=4)
. From version 1.10, you may also include
offset() terms.
a data.frame where the variables named in the formula can be found, i. e. the variables containing the binary response and the covariates.
specifies if confidence intervals and tests should be based on the
profile penalized log likelihood (pl=TRUE
, the default) or on the Wald
method (pl=FALSE
).
the significance level (1-\(\alpha\) the confidence level, 0.05 as default).
Controls Newton-Raphson iteration. Default is
control=logistf.control(maxstep, maxit, maxhs, lconv, gconv, xconv
)
Controls Newton-Raphson iteration for the estimation of the
profile likelihood confidence intervals. Default is
plcontrol=logistpl.control(maxstep, maxit,
maxhs, lconv, xconv, ortho, pr)
use of Firth's 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.
specifies the initial values of the coefficients for the fitting algorithm.
specifies case weights. Each line of the input data set is
multiplied by the corresponding element of weights
.
specifies the variables (as vector of their indices) for which profile likelihood confidence intervals should be computed. Default is to compute for all variables.
If TRUE, copies the data
set to the output object.
Further arguments to be passed to logistf.
Leena Choi leena.choi@vanderbilt.edu and Cole Beck cole.beck@vumc.org
same as those provided in the R package ‘logistf’.
data(dataPheWAS)
fit <- Logistf(X264.3 ~ exposure + age + race + gender, data=dd)
summary(fit)
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