R/8.bivdph.R In matrixdist: Statistics for Matrix Distributions

Documented in bivdph

```#' Bivariate discrete phase-type distributions
#'
#' Class of objects for bivariate discrete phase-type distributions.
#'
#' @slot name Name of the discrete phase-type distribution.
#' @slot pars A list comprising of the parameters.
#' @slot fit A list containing estimation information.
#'
#' @return Class object.
#' @export
#'
setClass("bivdph",
slots = list(
name = "character",
pars = "list",
fit = "list"
),
prototype = list(
name = NA_character_,
pars = list(),
fit = list()
)
)

#' Constructor function for bivariate discrete phase-type distributions
#'
#' @param alpha A probability vector.
#' @param S11 A sub-transition matrix.
#' @param S12 A matrix.
#' @param S22 A sub-transition matrix.
#' @param dimensions The dimensions of the bivariate discrete phase-type
#'  (if no parameters are provided).
#'
#' @return An object of class \linkS4class{bivdph}.
#' @export
#'
#' @examples
#' bivdph(dimensions = c(3, 3))
#' S11 <- matrix(c(0.1, .5, .5, 0.1), 2, 2)
#' S12 <- matrix(c(.2, .3, .2, .1), 2, 2)
#' S22 <- matrix(c(0.2, 0, 0.1, 0.1), 2, 2)
#' bivdph(alpha = c(.5, .5), S11, S12, S22)
bivdph <- function(alpha = NULL, S11 = NULL, S12 = NULL, S22 = NULL, dimensions = c(3, 3)) {
if (any(is.null(alpha)) & any(is.null(S11))) {
alpha <- stats::runif(dimensions[1])
alpha <- alpha / sum(alpha)
aux_m <- matrix(stats::runif(dimensions[1] * (dimensions[1] + dimensions[2])), nrow = dimensions[1])
aux_m <- aux_m / rowSums(aux_m)
S11 <- aux_m[, 1:dimensions[1]]
S12 <- aux_m[, -(1:dimensions[1])]
aux_m <- matrix(stats::runif(dimensions[2] * (dimensions[2] + 1)), nrow = dimensions[2])
aux_m <- aux_m / rowSums(aux_m)
S22 <- aux_m[, 1:dimensions[2]]
name <- "random"
} else {
if (dim(S11)[1] != dim(S11)[2]) {
stop("matrix S11 should be square")
} else if (dim(S22)[1] != dim(S22)[2]) {
stop("matrix S22 should be square")
} else if (dim(S11)[1] != dim(S12)[1]) {
stop("incompatible dimensions of S11 and S12")
} else if (dim(S22)[1] != dim(S12)[2]) {
stop("incompatible dimensions of S12 and S22")
} else if (length(alpha) != dim(S11)[1]) {
stop("incompatible dimensions of alpha and S11")
}
name <- "custom"
}
methods::new("bivdph",
name = paste(name, " bivdph(", length(alpha), ")", sep = ""),
pars = list(alpha = alpha, S11 = S11, S12 = S12, S22 = S22)
)
}

#' Show method for bivariate discrete phase-type distributions
#'
#' @param object An object of class \linkS4class{bivdph}.
#' @importFrom methods show
#' @export
#'
setMethod("show", "bivdph", function(object) {
cat("object class: ", methods::is(object)[[1]], "\n", sep = "")
cat("name: ", object@name, "\n", sep = "")
cat("parameters: ", "\n", sep = "")
methods::show(object@pars)
})

#' Density method for bivariate discrete phase-type distributions
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param y A matrix of locations.
#'
#' @return A vector containing the joint density evaluations at the given locations.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' dens(obj, matrix(c(1, 2), ncol = 2))
setMethod("dens", c(x = "bivdph"), function(x, y) {
if (is.vector(y)) {
y <- t(y)
}
bivdph_density(y, x@pars\$alpha, x@pars\$S11, x@pars\$S12, x@pars\$S22)
})

#' Coef method for bivdph class
#'
#' @param object An object of class \linkS4class{bivdph}.
#'
#' @return Parameters of bivariate discrete phase-type model.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' coef(obj)
setMethod("coef", c(object = "bivdph"), function(object) {
object@pars
})

#' Marginal method for bivdph class
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param mar Indicator of which marginal.
#' @return An object of the of class \linkS4class{dph}.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' marginal(obj, 1)
setMethod("marginal", c(x = "bivdph"), function(x, mar = 1) {
if (!(mar %in% 1:2)) {
stop("maringal provided not available")
}
if (mar == 1) {
x0 <- dph(alpha = x@pars\$alpha, S = x@pars\$S11)
} else {
alpha0 <- x@pars\$alpha %*% base::solve(diag(ncol(x@pars\$S11)) - x@pars\$S11) %*% x@pars\$S12
x0 <- dph(alpha = alpha0, S = x@pars\$S22)
}
x0
})

#' Moment method for bivdph class
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param k A vector with the location.
#' @return An real value.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' moment(obj, c(1, 1))
setMethod("moment", c(x = "bivdph"), function(x, k = c(1, 1)) {
if (any(k <= 0)) {
stop("k should have positive entries")
}
if (any((k %% 1) != 0)) {
stop("k should be an integer")
}
m11 <- matrix_power(k[1] - 1, x@pars\$S11)
m12 <- matrix_power(k[1] + 1, solve(diag(nrow(x@pars\$S11)) - x@pars\$S11))
m21 <- matrix_power(k[2] - 1, x@pars\$S22)
m22 <- matrix_power(k[2], solve(diag(nrow(x@pars\$S22)) - x@pars\$S22))
ee <- rep(1, nrow(x@pars\$S22))
factorial(k[1]) * factorial(k[2]) * x@pars\$alpha %*% m11 %*% m12 %*% x@pars\$S12 %*% m21 %*% m22 %*% ee
})

#' Mean method for bivdph class
#'
#' @param x An object of class \linkS4class{bivdph}.
#'
#' @return The mean of the bivariate discrete phase-type distribution.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' mean(obj)
setMethod("mean", c(x = "bivdph"), function(x) {
c(mean(marginal(x, 1)), mean(marginal(x, 2)))
})

#' Var method for bivdph class
#'
#' @param x An object of class \linkS4class{bivdph}.
#'
#' @return The covariance matrix of the bivariate discrete phase-type distribution.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' var(obj)
setMethod("var", c(x = "bivdph"), function(x) {
mar1 <- marginal(x, 1)
mar2 <- marginal(x, 2)
re <- matrix(0, 2, 2)
re[1, 1] <- var(mar1)
re[1, 2] <- moment(x, c(1, 1)) - mean(mar1) * mean(mar2)
re[2, 1] <- re[1, 2]
re[2, 2] <- var(mar2)
re
})

#' Cor method for bivdph class
#'
#' @param x An object of class \linkS4class{bivdph}.
#'
#' @return The correlation matrix of the bivariate discrete phase-type distribution.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' cor(obj)
setMethod("cor", c(x = "bivdph"), function(x) {
stats::cov2cor(var(x))
})

#' Pgf method for bivariate discrete phase-type distributions
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param z A vector of real values.
#'
#' @return The joint pdf of the \linkS4class{dph} object at the
#'  given location.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' pgf(obj, c(0.5, 0.2))
setMethod(
"pgf", signature(x = "bivdph"),
function(x, z) {
if (any(abs(z) > 1)) {
stop("z should between -1 and 1")
}
m1 <- solve(diag(nrow(x@pars\$S11)) - z[1] * x@pars\$S11)
m2 <- solve(diag(nrow(x@pars\$S22)) - z[2] * x@pars\$S22)
ee <- rep(1, nrow(x@pars\$S22))
t2 <- ee - x@pars\$S22 %*% ee
z[1] * z[2] * x@pars\$alpha %*% m1 %*% x@pars\$S12 %*% m2 %*% t2
}
)

#' Simulation method for bivariate discrete phase-type distributions
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param n An integer of length of realization.
#'
#' @return A realization of independent and identically distributed bivariate
#'  discrete phase-type vector.
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' sim(obj, n = 100)
setMethod("sim", c(x = "bivdph"), function(x, n = 1000) {
p1_aux <- dim(x@pars\$S11)[1]
p2_aux <- dim(x@pars\$S22)[1]
alpha_aux <- c(x@pars\$alpha, rep(0, p2_aux))
S_aux <- merge_matrices(x@pars\$S11, x@pars\$S12, x@pars\$S22)
R_aux <- matrix(c(c(rep(1, p1_aux), rep(0, p2_aux)), c(rep(0, p1_aux), rep(1, p2_aux))), ncol = 2)
exit_vec <- 1 - rowSums(S_aux)
trans_mat <- cbind(S_aux, exit_vec)
aux_vec <- rep(0, p1_aux + p2_aux + 1)
aux_vec[p1_aux + p2_aux + 1] <- 1
trans_mat <- rbind(trans_mat, aux_vec)
rMDPHstar(n, alpha_aux, trans_mat, R_aux)
})

#' Fit method for bivdph Class
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param y A matrix with the data.
#' @param weight Vector of weights.
#' @param stepsEM Number of EM steps to be performed.
#' @param every Number of iterations between likelihood display updates.
#'
#' @return An object of class \linkS4class{bivdph}.
#'
#' @export
#'
#' @examples
#' obj <- bivdph(dimensions = c(3, 3))
#' data <- sim(obj, n = 100)
#' fit(obj, data, stepsEM = 100, every = 50)
setMethod(
"fit", c(x = "bivdph"),
function(x,
y,
weight = numeric(0),
stepsEM = 1000,
every = 10) {
if (!all(y > 0)) {
stop("data should be positive")
}
if (!all(weight >= 0)) {
stop("weights should be non-negative")
}
if (length(weight) == 0) {
weight <- rep(1, length(y[, 1]))
}

bivdph_par <- x@pars
alpha_fit <- clone_vector(bivdph_par\$alpha)
S11_fit <- clone_matrix(bivdph_par\$S11)
S12_fit <- clone_matrix(bivdph_par\$S12)
S22_fit <- clone_matrix(bivdph_par\$S22)

options(digits.secs = 4)
cat(format(Sys.time(), format = "%H:%M:%OS"), ": EM started", sep = "")
cat("\n", sep = "")

for (k in 1:stepsEM) {
EMstep_bivdph(alpha_fit, S11_fit, S12_fit, S22_fit, y, weight)
if (k %% every == 0) {
cat("\r", "iteration:", k,
", logLik:", logLikelihoodbivDPH(alpha_fit, S11_fit, S12_fit, S22_fit, y, weight),
sep = " "
)
}
}

x@pars\$alpha <- alpha_fit
x@pars\$S11 <- S11_fit
x@pars\$S12 <- S12_fit
x@pars\$S22 <- S22_fit

cat("\n", format(Sys.time(), format = "%H:%M:%OS"), ": EM finalized", sep = "")
cat("\n", sep = "")

x
}
)

#' MoE method for bivdph Class
#'
#' @param x An object of class \linkS4class{bivdph}.
#' @param formula A regression formula.
#' @param y A matrix of observations.
#' @param data A data frame of covariates.
#' @param alpha_vecs Matrix of initial probabilities.
#' @param weight Vector of weights.
#' @param stepsEM Number of EM steps to be performed.
#' @param every Number of iterations between likelihood display updates.
#' @param rand_init Random initiation in the R-step.
#'
#' @return An object of class \linkS4class{sph}.
#'
#' @export
#'
#' @examples
#' x <- bivdph(dimensions = c(3, 3))
#' n <- 100
#' responses <- cbind(rpois(n, 3) + 1, rbinom(n, 5, 0.5))
#' covariates <- data.frame(age = sample(18:65, n, replace = TRUE) / 100, income = runif(n, 0, 0.99))
#' f <- responses ~ age + income
#' MoE(x = x, formula = f, y = responses, data = covariates, stepsEM = 20)
setMethod(
"MoE", c(x = "bivdph"),
function(x,
formula,
y,
data,
alpha_vecs = NULL,
weight = numeric(0),
stepsEM = 1000,
every = 10,
rand_init = TRUE) {
p <- length(x@pars\$alpha)
frame <- stats::model.frame(formula, data = data)
n <- nrow(frame)
d <- ncol(frame) - 1
if (is.null(alpha_vecs)) alpha_vecs <- matrix(x@pars\$alpha, ncol = p, nrow = n, byrow = TRUE)
if (length(weight) == 0) weight <- rep(1, n)
S11_fit <- clone_matrix(x@pars\$S11)
S12_fit <- clone_matrix(x@pars\$S12)
S22_fit <- clone_matrix(x@pars\$S22)
c <- c()
for (i in 1:p) c <- c(c, rep(i, n))
extended_x <- matrix(t(as.matrix(frame[, -1])), nrow = n * p, ncol = d, byrow = TRUE)
dm <- data.frame(Class = c, extended_x)
names(dm)[-1] <- names(frame)[-1]
ndm <- data.frame(dm[dm\$Class == 1, -1])
names(ndm) <- names(dm)[-1]
for (k in 1:stepsEM) {
B_matrix <- EMstep_bivdph_MoE(alpha_vecs, S11_fit, S12_fit, S22_fit, y, weight)
wt <- reshape2::melt(B_matrix)[, 3]
wt[wt < 1e-22] <- wt[wt < 1e-22] + 1e-22
if (k == 1 | rand_init == TRUE) {
multinom_model <- nnet::multinom(Class ~ ., data = dm, weights = wt, trace = F)
} else {
multinom_model <- nnet::multinom(Class ~ ., data = dm, weights = wt, trace = F, Wts = multinom_model\$wts)
}
alpha_vecs <- stats::predict(multinom_model, type = "probs", newdata = ndm)
if (k %% every == 0) {
ll <- logLikelihoodbivDPH_MoE(alpha_vecs, S11_fit, S12_fit, S22_fit, y, weight)
cat("\r", "iteration:", k, ", logLik:", ll, sep = " ")
}
}
cat("\n", sep = "")
list(alpha = alpha_vecs, S11 = S11_fit, S12 = S12_fit, S22 = S22_fit, mm = multinom_model)
}
)
```

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matrixdist documentation built on Aug. 8, 2023, 5:06 p.m.