monotonic {mvgam}

Monotonic splines in mvgam

Description

Uses constructors from package splines2 to build monotonically increasing or decreasing splines. Details also in Wang & Yan (2021).

Usage

## S3 method for class 'moi.smooth.spec'
smooth.construct(object, data, knots)

## S3 method for class 'mod.smooth.spec'
smooth.construct(object, data, knots)

## S3 method for class 'moi.smooth'
Predict.matrix(object, data)

## S3 method for class 'mod.smooth'
Predict.matrix(object, data)

Arguments

object

A smooth specification object, usually generated by a term s(x, bs = "moi", ...) or s(x, bs = "mod", ...)

data

a list containing just the data (including any by variable) required by this term, with names corresponding to object$term (and object$by). The by variable is the last element.

knots

a list containing any knots supplied for basis setup — in same order and with same names as data. Can be NULL. See details for further information.

Details

The constructor is not normally called directly, but is rather used internally by mvgam. If they are not supplied then the knots of the spline are placed evenly throughout the covariate values to which the term refers: For example, if fitting 101 data with an 11 knot spline of x then there would be a knot at every 10th (ordered) x value. The spline is an implementation of the closed-form I-spline basis based on the recursion formula given by Ramsay (1988), in which the basis coefficients must be constrained to either be non-negative (for monotonically increasing functions) or non-positive (monotonically decreasing)

Take note that when using either monotonic basis, the number of basis functions k must be supplied as an even integer due to the manner in which monotonic basis functions are constructed

Value

An object of class "moi.smooth" or "mod.smooth". In addition to the usual elements of a smooth class documented under smooth.construct, this object will contain a slot called boundary that defines the endpoints beyond which the spline will begin extrapolating (extrapolation is flat due to the first order penalty placed on the smooth function)

Note

This constructor will result in a valid smooth if using a call to gam or bam, however the resulting functions will not be guaranteed to be monotonic because constraints on basis coefficients will not be enforced

Author(s)

Nicholas J Clark

References

Wang, Wenjie, and Jun Yan. "Shape-Restricted Regression Splines with R Package splines2." Journal of Data Science 19.3 (2021).

Ramsay, J. O. (1988). Monotone regression splines in action. Statistical Science, 3(4), 425–441.

Examples

# Simulate data from a monotonically increasing function
set.seed(123123)
x <- runif(80) * 4 - 1
x <- sort(x)
f <- exp(4 * x) / (1 + exp(4 * x))
y <- f + rnorm(80) * 0.1
plot(x, y)

# A standard TRPS smooth doesn't capture monotonicity
mod_data <- data.frame(y = y, x = x)
mod <- gam(y ~ s(x, k = 16),
           data = mod_data,
           family = gaussian())

library(marginaleffects)
plot_predictions(mod,
                 by = 'x',
                 newdata = data.frame(x = seq(min(x) - 0.5,
                                              max(x) + 0.5,
                                              length.out = 100)),
                 points = 0.5)

# Using the 'moi' basis in mvgam rectifies this
mod_data$time <- 1:NROW(mod_data)
mod2 <- mvgam(y ~ s(x, bs = 'moi', k = 18),
             data = mod_data,
             family = gaussian(),
             chains = 2)

plot_predictions(mod2,
                 by = 'x',
                 newdata = data.frame(x = seq(min(x) - 0.5,
                                              max(x) + 0.5,
                                              length.out = 100)),
                 points = 0.5)

plot(mod2, type = 'smooth', realisations = TRUE)

# 'by' terms that produce a different smooth for each level of the 'by'
# factor are also allowed
set.seed(123123)
x <- runif(80) * 4 - 1
x <- sort(x)

# Two different monotonic smooths, one for each factor level
f <- exp(4 * x) / (1 + exp(4 * x))
f2 <- exp(3.5 * x) / (1 + exp(3 * x))
fac <- c(rep('a', 80), rep('b', 80))
y <- c(f + rnorm(80) * 0.1,
       f2 + rnorm(80) * 0.2)
plot(x, y[1:80])
plot(x, y[81:160])

# Gather all data into a data.frame, including the factor 'by' variable
mod_data <- data.frame(y, x, fac = as.factor(fac))
mod_data$time <- 1:NROW(mod_data)

# Fit a model with different smooths per factor level
mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8),
             data = mod_data,
             family = gaussian(),
             chains = 2)

# Visualise the different monotonic functions
plot_predictions(mod, condition = c('x', 'fac', 'fac'),
                 points = 0.5)
plot(mod, type = 'smooth', realisations = TRUE)

# First derivatives (on the link scale) should never be
# negative for either factor level
(derivs <- slopes(mod, variables = 'x',
                 by = c('x', 'fac'),
                 type = 'link'))
all(derivs$estimate > 0)