sm.logit {sm} | R Documentation |
This function estimates the regression curve using the local likelihood approach for a vector of binomial observations and an associated vector of covariate values.
sm.logit(x, y, N=rep(1, length(y)), h, ngrid=25, eval.points, add=F, display="estimate", xlab, ylab, pch=1, col=2, ...)
x |
vector of the covariate values |
y |
vector of the response values; they must be nonnegative integers. |
h |
the smoothing parameter; it must be positive. |
N |
a vector containing the binomial denominators. If missing, it is assumed to contain all 1's. |
ngrid |
the number of points where the regression curve must be estimated
(only used if eval.points is not given).
|
eval.points |
the vector of points on the x axis where the regression must be
estimated. If the parameter eval.points is not given, this vector
is chosen to be formed by ngrid equally spaced points between
min(x) and max(x) .
|
add |
if graphical output is produced, this parameter controls whether a new plot is created, or graphical output is added to the existing one. |
display |
controls the type of graphical output; possible values are
"estimate" (default), "se" , `"none".
|
xlab |
label of the x-axis |
ylab |
label of the y-axis |
pch |
plotting character of the raw observed frequency. |
col |
colour used for plotting curves and points |
... |
additional graphical parameters |
see Sections 3.4 and 5.4 of the reference below.
A list containing vectors with the evalutation points, the corresponding probability estimates, the linear predictors, the upper and lower points of the variability bands (on the probability scale) and the standard errors on the linear predictor scale.
graphical output will be produced, depending on the value of the
display parameter, unless this is set to "none"
.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.logit.bootstrap
, sm.poisson
, sm.poisson.bootstrap
# the next example assumes that all binomial denominators are 1's sm.logit(dose, failure, h=0.5) # in the next example, (some of) dose levels are replicated sm.logit(dose, failure, n.trials, h=0.5)