These functions create mathematical functions from data, by smoothing, splining, or linear combination (fitting). Each of them takes a formula and a data frame as an argument
spliner(formula, data = NULL, method = "fmm", monotonic = FALSE)
connector(formula, data = NULL, method = "linear")
smoother(formula, data, span = 0.5, degree = 2, ...)
linearModel(formula, data, ...)a formula. Only one quantity is allowed on the left-hand side, the output quantity
a data frame
a method for splining. See spline().
a TRUE/FALSE flag specifying whether the spline should
respect monotonicity in the data
parameter to smoother. How smooth it should be.
parameter to smoother. 1 is locally linear, 2 is locally quadratic.
additional arguments to stats::loess() or stats::lm()
These functions use data to create a mathematical, single-valued function of the inputs.
All return a function whose arguments are the variables used on the right-hand side of the formula.
If the formula involves a transformation, e.g. sqrt(age) or log(income),
only the variable itself, e.g. age or income, is an argument to the function.
linearModel takes a linear combination of the vectors specified on the right-hand side.
It differs from project in that linearModel returns a function
whereas project returns the coefficients. NOTE: An intercept term is not included
unless that is explicitly part of the formula with +1. This conflicts with the
standard usage of formulas as found in lm. Another option for creating
such functions is to combine lm() and makeFun().
spliner and connector currently work for only one input variable.
project() method for formulas
if (require(mosaicData)) {
data(CPS85)
f <- smoother(wage ~ age, span=.9, data=CPS85)
f(40)
g <- linearModel(log(wage) ~ age + educ + 1, data=CPS85)
g(age=40, educ=12)
# an alternative way to define g (Note: + 1 is the default for lm().)
g2 <- makeFun(lm(log(wage) ~ age + educ, data=CPS85))
g2(age=40, educ=12)
x<-1:5; y=c(1, 2, 4, 8, 8.2)
f1 <- spliner(y ~ x)
f1(x=8:10)
f2 <- connector(x~y)
}