cpr: Control Polygon Reduction

In dewittpe/cpr: Control Polygon Reduction

Control Polygon Reduction

Description

Run the Control Polygon Reduction Algorithm.

Usage

cpr(x, progress = c("cpr", "influence", "none"), ...)

Arguments

x	a cpr_cp object
progress	controls the level of progress messaging. See Details.
...	not currently used

Details

cpr runs the control polygon reduction algorithm.

The algorithm is generally speaking fast, but can take a long time to run if the number of interior knots of initial control polygon is high. To help track the progress of the execution you can have progress = "cpr" which will show a progress bar incremented for each iteration of the CPR algorithm. progress = "influence" will use a combination of messages and progress bars to report on each step in assessing the influence of all the internal knots for each iteration of the CPR algorithm. See influence_of_iknots for more details.

Value

a list of cpr_cp objects

See Also

influence_of_iknots

Examples

#############################################################################
# Example 1: find a model for log10(pdg) = f(day) from the spdg data set

# need the lme4 package to fit a mixed effect model
require(lme4)

# construct the initial control polygon.  Forth order spline with fifty
# internal knots.  Remember degrees of freedom equal the polynomial order
# plus number of internal knots.
init_cp <- cp(log10(pdg) ~ bsplines(day, df = 24, bknots = c(-1, 1)) + (1|id),
              data = spdg, method = lme4::lmer)
cpr_run <- cpr(init_cp)
plot(cpr_run, color = TRUE)

s <- summary(cpr_run)
s
plot(s, type = "rse")

# preferable model is in index 5 by eye
preferable_cp <- cpr_run[["cps"]][[5]]


#############################################################################
# Example 2: logistic regression
# simulate a binary response Pr(y = 1 | x) = p(x)
p <- function(x) { 0.65 * sin(x * 0.70) + 0.3 * cos(x * 4.2) }

set.seed(42)
x <- runif(2500, 0.00, 4.5)
sim_data <- data.frame(x = x, y = rbinom(2500, 1, p(x)))

# Define the initial control polygon
init_cp <- cp(formula = y ~ bsplines(x, df = 24, bknots = c(0, 4.5)),
              data    = sim_data,
              method  = glm,
              method.args = list(family  = binomial())
              )

# run CPR
cpr_run <- cpr(init_cp)

# preferable model is in index 6
s <- summary(cpr_run)
plot(s, color = TRUE, type = "rse")

plot(
    cpr_run
  , color = TRUE
  , from = 5
  , to = 7
  , show_spline = TRUE
  , show_cp = FALSE
  )


# plot the fitted spline and the true p(x)
sim_data$pred_select_p <- plogis(predict(cpr_run[[7]], newdata = sim_data))
ggplot2::ggplot(sim_data) +
ggplot2::theme_bw() +
ggplot2::aes(x = x) +
ggplot2::geom_point(mapping = ggplot2::aes(y = y), alpha = 0.1) +
ggplot2::geom_line(
    mapping = ggplot2::aes(y = pred_select_p, color = "pred_select_p")
  ) +
ggplot2::stat_function(fun = p, mapping = ggplot2::aes(color = 'p(x)'))

# compare to gam and a binned average
sim_data$x2 <- round(sim_data$x, digits = 1)
bin_average <-
  lapply(split(sim_data, sim_data$x2), function(x) {
           data.frame(x = x$x2[1], y = mean(x$y))
         })
bin_average <- do.call(rbind, bin_average)

ggplot2::ggplot(sim_data) +
ggplot2::theme_bw() +
ggplot2::aes(x = x) +
ggplot2::stat_function(fun = p, mapping = ggplot2::aes(color = 'p(x)')) +
ggplot2::geom_line(
    mapping = ggplot2::aes(y = pred_select_p, color = "pred_select_p")
  ) +
ggplot2::stat_smooth(mapping = ggplot2::aes(y = y, color = "gam"),
                     method = "gam",
                     formula = y ~ s(x, bs = "cs"),
                     se = FALSE,
                     n = 1000) +
ggplot2::geom_line(data = bin_average
                   , mapping = ggplot2::aes(y = y, color = "bin_average"))

dewittpe/cpr documentation built on Feb. 16, 2024, 1:11 p.m.