penaltyLearning (version 2020.5.13)

targetIntervalResidual: targetIntervalResidual

Description

Compute residual of predicted penalties with respect to target intervals. This function is useful for visualizing the errors in a plot of log(penalty) versus a feature.

Usage

targetIntervalResidual(target.mat, 
    pred)

Arguments

target.mat

n x 2 numeric matrix: target intervals of log(penalty) values that yield minimal incorrect labels.

pred

numeric vector: predicted log(penalty) values.

Value

numeric vector of n residuals. Predictions that are too high (above target.mat[,2]) get positive residuals (too few changepoints), and predictions that are too low (below target.mat[,1]) get negative residuals.

Examples

# NOT RUN {
library(penaltyLearning)
library(data.table)
data(neuroblastomaProcessed, envir=environment())
## The BIC model selection criterion is lambda = log(n), where n is
## the number of data points to segment. This implies log(lambda) =
## log(log(n)), which is the log2.n feature.
row.name.vec <- grep(
  "^(4|520)[.]",
  rownames(neuroblastomaProcessed$feature.mat),
  value=TRUE)
feature.mat <- neuroblastomaProcessed$feature.mat[row.name.vec, ]
target.mat <- neuroblastomaProcessed$target.mat[row.name.vec, ]
pred.dt <- data.table(
  row.name=row.name.vec,
  target.mat,
  feature.mat[, "log2.n", drop=FALSE])
pred.dt[, pred.log.lambda := log2.n ]
pred.dt[, residual := targetIntervalResidual(
  cbind(min.L, max.L),
  pred.log.lambda)]
library(ggplot2)
limits.dt <- pred.dt[, data.table(
  log2.n,
  log.penalty=c(min.L, max.L),
  limit=rep(c("min", "max"), each=.N))][is.finite(log.penalty)]
ggplot()+
  geom_abline(slope=1, intercept=0)+
  geom_point(aes(
    log2.n,
    log.penalty,
    fill=limit),
    data=limits.dt,
    shape=21)+
  geom_segment(aes(
    log2.n, pred.log.lambda,
    xend=log2.n, yend=pred.log.lambda-residual),
    data=pred.dt,
    color="red")+
  scale_fill_manual(values=c(min="white", max="black"))

# }