tvgeom (version 1.0.1)

tvgeom: 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

x, q

vector of quantiles representing the trial at which the first success occurred.

prob

vector of the probability of success for each trial.

log, log.p

logical; if TRUE, probabilities, p, are given as log(p). Defaults to FALSE.

lower.tail

logical; if FALSE, ptvgeom returns

p

vector of probabilities at which to evaluate the quantile function.

n

number of observations to sample.

lower

lower value (exclusive) at which to truncate the distribution for random number generation. Defaults to 0, in which case the distribution is not left-truncated.

upper

upper value (inclusive) at which to truncate the distribution for random number generation. Defaults to length(prob), in which case the distribution is not right-truncated.

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 ...

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.

Examples

# NOT RUN {
# 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")
# }