RDocumentation: email

Beta

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if(!require('openintro')) {
    install.packages('openintro')
    library('openintro')
}


e <- email

# ______ Variables For Logistic Regression ______#
# Variables are modified to match
#   OpenIntro Statistics, Second Edition
# As Is (7): spam, to_multiple, winner, format,
#            re_subj, exclaim_subj
# Omitted (6): from, sent_email, time, image,
#              viagra, urgent_subj, number
# Become Indicators (5): cc, attach, dollar,
#                        inherit, password
e$cc <- ifelse(email$cc > 0, 1, 0)
e$attach <- ifelse(email$attach > 0, 1, 0)
e$dollar <- ifelse(email$dollar > 0, 1, 0)
e$inherit <- ifelse(email$inherit > 0, 1, 0)
e$password <- ifelse(email$password > 0, 1, 0)
# Transform (3): num_char, line_breaks, exclaim_mess
# e$num_char     <- cut(email$num_char, c(0,1,5,10,20,1000))
# e$line_breaks  <- cut(email$line_breaks, c(0,10,100,500,10000))
# e$exclaim_mess <- cut(email$exclaim_mess, c(-1,0,1,5,10000))
g <- glm(spam ~ to_multiple + winner + format +
  re_subj + exclaim_subj +
  cc + attach + dollar +
  inherit + password, # +
# num_char + line_breaks + exclaim_mess,
data = e, family = binomial
)
summary(g)


# ______ Variable Selection Via AIC ______#
g. <- step(g)
plot(predict(g., type = "response"), e$spam)


# ______ Splitting num_char by html ______#
x <- log(email$num_char)
bw <- 0.004
R <- range(x) + c(-1, 1)
wt <- sum(email$format == 1) / nrow(email)
htmlAll <- density(x, bw = 0.4, from = R[1], to = R[2])
htmlNo <- density(x[email$format != 1],
  bw = 0.4,
  from = R[1], to = R[2]
)
htmlYes <- density(x[email$format == 1],
  bw = 0.4,
  from = R[1], to = R[2]
)
htmlNo$y <- htmlNo$y #* (1-wt)
htmlYes$y <- htmlYes$y #* wt + htmlNo$y
plot(htmlAll, xlim = c(-4, 6), ylim = c(0, 0.4))
lines(htmlNo, col = 4)
lines(htmlYes, lwd = 2, col = 2)