tvgeom: A package for the time-varying geometric probability distribution

Description

The tvgeom package provides two categories of important functions: probability distribution functions (d, p, q, r) and moments (tvgeom_mean and tvgeom_var).

Density (dtvgeom), distribution function (qtvgeom), quantile function (ptvgeom), and random number generation (rtvgeom and rttvgeom for sampling from the full and truncated distribution, respectively) for the time-varying, right-truncated geometric distribution with parameter prob.

Usage

dtvgeom(x, prob, log = FALSE)

ptvgeom(q, prob, lower.tail = TRUE, log.p = FALSE)

qtvgeom(p, prob, lower.tail = TRUE, log.p = FALSE)

rtvgeom(n, prob)

rttvgeom(n, prob, lower = 0, upper = length(prob) + 1)

Arguments

Details

The time-varying geometric distribution describes the number of independent Bernoulli trials needed to obtain one success. The probability of success, prob, may vary for each trial. It has mass

p(x) = prob[x] * prod(1 - prob[1:(x-1)])

with support x = 1, 2, ..., n + 1, where n equals then length of prob. For i in prob, 0 \le i \le 1. The n+1 case represents the case that the event did not happen in the first n trials.

Value

dtvgeom gives the probability mass, qtvgeom gives the quantile functions, ptvgeom gives the distribution function, rtvgeom generates random numbers, and rttvgeom gives random numbers from the distribution truncated at bounds provided by the user. P(X > x) instead of P(X \le x). Defaults to TRUE.

Package functions

The tvgeom functions ...

Examples

# 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")

Moments for the The Time-Varying (Right-Truncated) Geometric Distribution

Description

Functions to calculate first moment tvgeom_mean() and second central moment tvgeom_var() for the time-varying geometric distribution.

Usage

tvgeom_mean(prob)

tvgeom_var(prob)

Arguments

Value

tvgeom_mean returns the moment (the mean), and tvgeom_var returns the second central moment (the variance).

Examples

tvgeom_mean(prob = rep(0.1, 5))
tvgeom_var(prob = rep(0.1, 5))