Nothing
## ----setup, echo = FALSE------------------------------------------------------
knitr::opts_chunk$set(collapse = FALSE,
comment = "#>",
prompt = FALSE,
tidy = FALSE,
echo = TRUE,
message = FALSE,
warning = FALSE,
# Default figure options:
dpi = 100,
fig.align = 'center',
fig.height = 6.0,
fig.width = 6.5,
out.width = "580px")
## ----pkgs, echo = FALSE, message = FALSE, results = 'hide'--------------------
library(FFTrees)
## ----fft-example, results = "hide"--------------------------------------------
# Create an FFTrees object predicting heart disease:
heart.fft <- FFTrees(formula = diagnosis ~.,
data = heartdisease)
## ----fft-plot-1, fig.cap = "**Figure 1**: Example FFT for the `heartdisease` data."----
plot(heart.fft, tree = "best.train")
## ----confusion-table, fig.align = "center", out.width="50%", echo = FALSE, fig.cap = "**Figure 2**: A 2x2 matrix illustrating the frequency counts of 4 possible outcomes."----
knitr::include_graphics("../inst/confusiontable.jpg")
## ----fft-heart----------------------------------------------------------------
heart.fft
## ----fft-levelout-------------------------------------------------------------
# A vector of levels/nodes at which each case was classified:
heart.fft$trees$decisions$train$tree_1$levelout
## ----fft-mcu------------------------------------------------------------------
# Calculate the mean number or cues used (mcu):
mean(heart.fft$trees$decisions$train$tree_1$levelout)
## ----fft-pci------------------------------------------------------------------
# Calculate pci (percentage of cues ignored) as
# (n.cues - mcu) / n.cues):
n.cues <- ncol(heartdisease)
(n.cues - heart.fft$trees$stats$train$mcu[1]) / n.cues
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