README

This R package contains PMF, CDF, quantile, and random number generation functions for the time-varying right-truncated geometric (tvgeom) distribution. The tvgeom distribution is derived from the geometric distribution and has a vector of success probabilities as its parameter. Whereas the geometric distribution has a constant probability of success over time and has no upper bound of support, the tvgeom distribution has a probability of success that changes over time. Additionally, to accommodate situations in which the event can only occur in \(n\) days, after which success can not occur, the tvgeom distribution is right-truncated (it has a maximum possible value of the length, n, of the probability vector plus 1). When a tvgeom distributed variable has value n+1, this means the event did not occur in the first n time steps. For more detailed information on this package and the tvgeom distribution, please see the package vignette.

Example

The following example demonstrates the relationship between the geometric distribution and the time-varying geometric distribution.

library(tvgeom)
# What's the probability that a given number of trials, n, are needed to get
# one success if `prob` = `p0`, as defined below...?
p0 <- .15 # the probability of success

# Axis labels (for plotting purposes, below).
x_lab <- "Number of trials, n"
y_lab <- sprintf("P(success at trial n | prob = %s)", p0)

# Scenario 1: the probability of success is constant and we invoke functions
# from base R's implementation of the geometric distribution.
y1 <- rgeom(1e3, p0) + 1 # '+1' b/c dgeom parameterizes in terms of failures
x1 <- seq_len(max(y1))
z1 <- dgeom(x1 - 1, p0)
plot(table(y1) / 1e3,
  xlab = x_lab, ylab = y_lab, col = "#00000020",
  bty = "n", ylim = c(0, p0)
)
lines(x1, z1, type = "l")

# Scenario 2: the probability of success is constant, but we use tvgeom's
# implementation of the time-varying geometric distribution. For the purposes
# of this demonstration, the length of vector `prob` (`n_p0`) is chosen to be
# arbitrarily large *relative* to the distribution of n above (`y1`) to
# ensure we don't accidentally create any censored observations!
n_p0 <- max(y1) * 5
p0_vec <- rep(p0, n_p0)
y2 <- rtvgeom(1e3, p0_vec)
x2 <- seq_len(max(max(y1), max(y2)))
z2 <- dtvgeom(x2, p0_vec) # dtvgeom is parameterized in terms of successes
points(x2[x2 <= max(y1)], z2[x2 <= max(y1)],
  col = "red", xlim = c(1, max(y1))
)

# Scenario 3: the probability of success for each process varies over time
# (e.g., chances increase linearly by `rate` for each subsequent trial until
# chances saturate at `prob` = 1).
rate <- 1.5
prob_tv <- numeric(n_p0)
for (i in 1:length(p0_vec)) {
  prob_tv[i] <- ifelse(i == 1, p0_vec[i], rate * prob_tv[i - 1])
}
prob_tv[prob_tv > 1] <- 1
y3 <- rtvgeom(1e3, prob_tv)
x3 <- seq_len(max(y3))
z3 <- dtvgeom(x3, prob_tv)
plot(table(y3) / 1e3,
  xlab = x_lab, col = "#00000020", bty = "n",
  ylim = c(0, max(z3)),
  ylab = sprintf("P(success at trial n | prob = %s)", "`prob_tv`")
)
lines(x3, z3, type = "l")

tvgeom

Description

Example

To install