lrm.fit(x, y, offset, initial, est, maxit=12, eps=.025,
tol=1E-7, trace=FALSE, penalty.matrix, weights, normwt)
x
to fit in the model (default is all columns of x
).
Specifying est=c(1,2,5)
causes columns 1,2, and 5 to have
parameters estimated. The score vector u
and covariance matrix var
c12
). Specifying maxit=1
causes logist to compute statistics at initial estimates..025
.TRUE
to print -2 log likelihood, step-halving
fraction, and rank of variance matrix at each iterationlrm
y
) of possibly fractional case weightsTRUE
to scale weights
so they sum to the length of
y
; useful for sample surveys as opposed to the default of
frequency weightingy
in order of increasing y
penalty.matrix
is present, the $\chi^2$,
d.f., and P-value are not corrected for the effective d.f.TRUE
if convergence failed (and maxiter>1
)var
is not the
improved sandwich-type estimator (which lrm
does compute).X
fitted (intercepts are not counted)lrm
, glm
, matinv
, solvet
, cr.setup
#Fit an additive logistic model containing numeric predictors age,
#blood.pressure, and sex, assumed to be already properly coded and
#transformed
#
# fit <- lrm.fit(cbind(age,blood.pressure,sex), death)
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