| sm.ancova {sm} | R Documentation |
This function allows a set of nonparametric regression curves to be compared, both graphically and formally in a hypothesis test. A reference model, used to define the null hypothesis, may be either equality or parallelism.
sm.ancova(x, y, group, h, model = "none", h.alpha = NA, weights=NA,
covar = diag(1/weights), ...)
x |
a vector of covariate values. |
y |
a vector of response values. |
group |
a vector of group indicators. |
h |
the smoothing parameter to be used in the construction of each of the regression curves. |
model |
a character variable which defines the reference model. The values
"none", "equal" and "parallel" are possible.
|
h.alpha |
the value of the smoothing parameter used when estimating the vertical separations of the curves under the parallelism model. |
weights |
case weights; see the documentation of sm.regression
for a full description.
|
covar |
the (estimated) covariance matrix of y. The defaulty value assumes
the data to be independent. Where appropriate, the covariance structure
of y can be estimated by the user, externally to sm.ancova,
and passed through this argument.
|
... |
other optional parameters are passed to the sm.options
function, through a mechanism which limits their effect only to this
call of the function. Those relevant for this function are the following:
display,
ngrid,
eval.points,
xlab,
ylab;
see the documentation of sm.options for their description.
|
see Sections 6.4 and 6.5 of the book by Bowman & Azzalini, and the papers by Young & Bowman listed below. This function is a developed version of code originally written by Stuart Young.
a list containing an estimate of the error standard deviation and, where appropriate, a p-value and reference model. If the parallelism model has been selected then a vector of estimates of the vertical separations of the underlying regression curves is also returned.
a plot on the current graphical device is produced, unless display="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.
Young, S.G. and Bowman, A.W. (1995). Nonparametric analysis of covariance. Biometrics 51, 920–931.
Bowman, A.W. and Young, S.G. (1996). Graphical comparison of nonparametric curves. Applied Statistics 45, 83–98.
sm.regression, sm.density.compare, sm.options
x <- runif(50, 0, 1) y <- 4*sin(6*x) + rnorm(50) g <- rbinom(50, 1, 0.5) sm.ancova(x, y, g, h = 0.15, model = "equal")