estimate_pt: Estimate probabilities to be in each state

In cfda: Categorical Functional Data Analysis

Estimate probabilities to be in each state

Description

Estimate probabilities to be in each state

Usage

estimate_pt(data, NAafterTmax = FALSE, timeValues = NULL)

Arguments

data	data.frame containing id, id of the trajectory, time, time at which a change occurs and state, associated state.
NAafterTmax	if TRUE, return NA if t > Tmax otherwise return the state associated with Tmax (useful when individuals has different lengths)
timeValues	time values at which probabilities are computed, if NULL, unique(data$time) are used

Value

A list of two elements:

• t: vector of time

• pt: a matrix with K (= number of states) rows and with length(t) columns containing the probabilities to be in each state at each time.

Author(s)

Cristian Preda, Quentin Grimonprez

See Also

plot.pt

Other Descriptive statistics: boxplot.timeSpent(), compute_duration(), compute_number_jumps(), compute_time_spent(), hist.duration(), hist.njump(), plot.pt(), plotData(), statetable(), summary_cfd()

Examples

# Simulate the Jukes-Cantor model of nucleotide replacement
K <- 4
PJK <- matrix(1 / 3, nrow = K, ncol = K) - diag(rep(1 / 3, K))
lambda_PJK <- c(1, 1, 1, 1)
d_JK <- generate_Markov(n = 10, K = K, P = PJK, lambda = lambda_PJK, Tmax = 10)

d_JK2 <- cut_data(d_JK, 10)

# estimate probabilities
estimate_pt(d_JK2)

cfda documentation built on April 3, 2025, 9:21 p.m.