NKnots | R Documentation |
Calculates AIC and BIC for the selection of knots in a spline over values (potentially including polynomials) up to a user-defined maximum.
NKnots(
form,
var,
data,
degree = 3,
min.knots = 1,
max.knots = 10,
includePoly = FALSE,
plot = FALSE,
criterion = c("AIC", "BIC", "CV"),
cvk = 10,
cviter = 10
)
form |
A formula detailing the model for which smoothing is to be evaluated. |
var |
A character string identifying the variable for which smoothing is to be evaluated. |
data |
Data frame providing values of all variables in |
degree |
Degree of polynomial in B-spline basis functions. |
min.knots |
Minimum number of internal B-spline knots to be evaluated. |
max.knots |
Maximum number of internal B-spline knots to be evaluated. |
includePoly |
Include linear and polynomial models up to, and including
|
plot |
Logical indicating whether a plot should be returned. |
criterion |
Statistical criterion to minimize in order to find the best number of knots - AIC, BIC or Cross-validation. |
cvk |
Number of groups for cross-validation |
cviter |
Number of iterations of cross-validation to average over. 10 is the default but in real-world applications, this should be somewhere around 200. |
A plot, if plot=TRUE
, otherwise a data frame with the degrees
of freedom and corresponding fit measure.
Dave Armstrong
data(Prestige, package="carData")
NKnots(prestige ~ education + type, var="income", data=na.omit(Prestige), plot=FALSE)
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