np.graph: Tool to plot natural cubic splines or P-splines.

In ssym: Fitting Semi-Parametric log-Symmetric Regression Models

Tool to plot natural cubic splines or P-splines.

Description

np.graph displays a graph of a fitted nonparametric effect, either natural cubic spline or P-spline, from an object of class ssym.

Usage

np.graph(object, which, var, exp, simul, obs, xlab, ylab, xlim, ylim, main)

Arguments

object	an object of the class ssym. This object is returned from the call to ssym.l(), ssym.nl() or ssym.l2().
which	an integer indicating the interest submodel. For example, 1 indicates location submodel, and 2 indicates skewness (or relative dispersion) submodel.
var	character. It allows to choosing the nonparametric effect using the name of the associated explanatory variable.
exp	logical. If TRUE, the fitted nonparametric effect is plotted in exponential scale. Default is FALSE.
simul	logical. If TRUE, the fitted nonparametric effect is plotted jointly with their 95\% simultaneous confidence intervals. If TRUE, the fitted nonparametric effect is plotted jointly with their 95\% pointwise confidence intervals. Default is TRUE.
obs	logical. If TRUE, the fitted nonparametric effect is plotted jointly with the observed data. Default is FALSE.
xlab	character. An optional label for the x axis.
ylab	character. An optional label for the y axis.
xlim	numeric. An optional range of values for the x axis.
ylim	numeric. An optional range of values for the y axis.
main	character. An optional overall title for the plot.

Author(s)

Luis Hernando Vanegas <[e-mail]> and Gilberto A. Paula

References

Lancaster, P. and Salkauskas, K. (1986) Curve and Surface Fitting: an introduction. Academic Press, London. Green, P.J. and Silverman, B.W. (1994) Nonparametric Regression and Generalized Linear Models, Boca Raton: Chapman and Hall. Eilers P.H.C. and Marx B.D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science. 11, 89-121.

Examples

#data("Ovocytes", package="ssym")
#fit <- ssym.l(fraction ~ type + psp(time) | type + psp(time), data=Ovocytes,
#              family='Powerexp', xi=-0.55)
#
#par(mfrow = c(1,2))
#np.graph(fit, which=1, xlab="Time", main="Location")
#np.graph(fit, which=2, exp=TRUE, xlab="Time", main="Dispersion")

ssym documentation built on April 22, 2023, 1:13 a.m.