Logistf  R Documentation 
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 pvalue that would be useful for GenomeWide 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, ... )
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 (1α the confidence level, 0.05 as default). 
control 
Controls NewtonRaphson iteration. Default is 
plcontrol 
Controls NewtonRaphson 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. 
same as logistf
except for providing more decimal places of pvalue.
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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