mosaic (version 0.8-1)

FunctionsFromData: Create function from data

Description

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

Usage

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, ...)

Arguments

formula

a formula. Only one quantity is allowed on the left-hand side, the output quantity

data

a data frame

method

a method for splining. See spline.

monotonic

a TRUE/FALSE flag specifying whether the spline should respect monotonicity in the data

span

parameter to smoother. How smooth it should be.

degree

parameter to smoother. 1 is locally linear, 2 is locally quadratic.

...

additional arguments to loess or lm

Details

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. spliner and connector currently work for only one input variable.

See Also

project method for formulas

Examples

data(CPS85)
f <- smoother(wage ~ age, span=.9, data=CPS85)
f(40)
df <- D(f(age) ~ age)
df(40)
g <- linearModel(log(wage) ~ age+educ+1, data=CPS85)
g(age=40, educ=12)
dgdeduc <- D(g(age=age, educ=educ) ~ educ)
dgdeduc(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)