VGAM (version 1.1-14)

smartpred: Smart Prediction

Description

Data-dependent parameters in formula terms can cause problems in when predicting. The smartpred package saves data-dependent parameters on the object so that the bug is fixed. The lm and glm functions have been fixed properly. Note that the VGAM package by T. W. Yee automatically comes with smart prediction.

Usage

sm.bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, 
      Boundary.knots = range(x))
sm.ns(x, df = NULL, knots = NULL, intercept = FALSE,
      Boundary.knots = range(x))
sm.poly(x, ..., degree = 1, coefs = NULL, raw = FALSE) 
sm.scale(x, center = TRUE, scale = TRUE)

Value

The usual value returned by

bs,

ns,

poly and

scale, When used with functions such as vglm

the data-dependent parameters are saved on one slot component called

smart.prediction.

Details

R version 1.6.0 introduced a partial fix for the prediction problem because it does not work all the time, e.g., for terms such as I(poly(x, 3)), poly(c(scale(x)), 3), bs(scale(x), 3), scale(scale(x)). See the examples below. Smart prediction, however, will always work.

The basic idea is that the functions in the formula are now smart, and the modelling functions make use of these smart functions. Smart prediction works in two ways: using smart.expression, or using a combination of put.smart and get.smart.

See Also

get.smart.prediction, get.smart, put.smart, smart.expression, smart.mode.is, setup.smart, wrapup.smart. For vgam in VGAM, sm.ps is important. Commonly used data-dependent functions include scale, poly, bs, ns. In R, the functions bs and ns are in the splines package, and this library is automatically loaded in because it contains compiled code that bs and ns call.

The functions vglm, vgam, rrvglm and cqo in T. W. Yee's VGAM package are examples of modelling functions that employ smart prediction.

Examples

# Create some data first
n <- 20
set.seed(86)  # For reproducibility of the random numbers
ldata <- data.frame(x2 = sort(runif(n)), y = sort(runif(n)))
library("splines")  # To get ns() in R

# This will work for R 1.6.0 and later
fit <- lm(y ~ ns(x2, df = 5), data = ldata)
if (FALSE) {
plot(y ~ x2, data = ldata)
lines(fitted(fit) ~ x2, data = ldata)
new.ldata <- data.frame(x2 = seq(0, 1, len = n))
points(predict(fit, new.ldata) ~ x2, new.ldata, type = "b", col = 2, err = -1)
}

# The following fails for R 1.6.x and later. It can be
# made to work with smart prediction provided
# ns is changed to sm.ns and scale is changed to sm.scale:
fit1 <- lm(y ~ ns(scale(x2), df = 5), data = ldata)
if (FALSE) {
plot(y ~ x2, data = ldata, main = "Safe prediction fails")
lines(fitted(fit1) ~ x2, data = ldata)
points(predict(fit1, new.ldata) ~ x2, new.ldata, type = "b", col = 2, err = -1)
}

# Fit the above using smart prediction
if (FALSE) {
library("VGAM")  # The following requires the VGAM package to be loaded 
fit2 <- vglm(y ~ sm.ns(sm.scale(x2), df = 5), uninormal, data = ldata)
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plot(y ~ x2, data = ldata, main = "Smart prediction")
lines(fitted(fit2) ~ x2, data = ldata)
points(predict(fit2, new.ldata, type = "response") ~ x2, data = new.ldata,
       type = "b", col = 2, err = -1)
}