diff --git a/R/mc_core_cross_variability.R b/R/mc_core_cross_variability.R
index 610bff7920a2d45c9a1ff354c5eec52bc5c291d3..2bff4076255cebb5c1fc2ee1f8ebb21f06ba7636 100644
--- a/R/mc_core_cross_variability.R
+++ b/R/mc_core_cross_variability.R
@@ -1,13 +1,16 @@
-#' Cross variability matrix
+#' @title Cross variability matrix
+#' @author Wagner Hugo Bonat
#'
-#' @description Compute the cross-covariance matrix between covariance and regression parameters.
-#' Equation (11) of Bonat and Jorgensen (2015).
+#' @description Compute the cross-covariance matrix between covariance
+#' and regression parameters. Equation (11) of Bonat and Jorgensen
+#' (2015).
#'
#' @param A A matrix.
#' @param res A vector of residuals.
#' @param W A matrix of weights.
+
covprod <- function(A, res, W) {
- res = as.numeric(res)
+ res <- as.numeric(res)
saida <- (res %*% W %*% res) %*% (t(res) %*% A)
return(as.numeric(saida))
-}
+}
diff --git a/R/mc_core_pearson.R b/R/mc_core_pearson.R
index 22719d872533dbf7a0f8b6a03d346f001a2123d6..cbe5e0d759713337abd2ea55c1657b32629d0c9f 100644
--- a/R/mc_core_pearson.R
+++ b/R/mc_core_pearson.R
@@ -1,4 +1,6 @@
-#' Core of the Pearson estimating function.
+#' @title Core of the Pearson estimating function.
+#' @author Wagner Hugo Bonat
+#'
#' @description Core of the Pearson estimating function.
#'
#' @param product A matrix.
@@ -8,6 +10,7 @@
#' @details It is an internal function.
mc_core_pearson <- function(product, inv_C, res) {
- output <- t(res) %*% product %*% (inv_C %*% res) - sum(diag(product))
+ output <- t(res) %*% product %*%
+ (inv_C %*% res) - sum(diag(product))
return(as.numeric(output))
}
diff --git a/R/mc_correction.R b/R/mc_correction.R
index aebd8ef9da4ec17e747fd83b15b9384fd4f6cc4a..4154774865190bfb9bbd773529c8e2f5e87d6337 100644
--- a/R/mc_correction.R
+++ b/R/mc_correction.R
@@ -1,17 +1,26 @@
-#' Pearson correction term
+#' @title Pearson correction term
+#' @author Wagner Hugo Bonat
#'
-#' @description Compute the correction term associated with the Pearson estimating function.
+#' @description Compute the correction term associated with the Pearson
+#' estimating function.
#'
#' @param D_C A list of matrices.
-#' @param inv_J_beta A matrix. In general it is computed based on the output of the
-#' \code{[mcglm]{mc_quasi_score}}.
-#' @param D A matrix. In general it is the output of the \link{mc_link_function}.
-#' @param inv_C A matrix. In general the output of the \link{mc_build_C}.
-#' @return A vector with the correction terms to be used on the Pearson estimating function.
-#' @details It is an internal function useful inside the fitting algorithm.
+#' @param inv_J_beta A matrix. In general it is computed based on the
+#' output of the \code{[mcglm]{mc_quasi_score}}.
+#' @param D A matrix. In general it is the output of the
+#' \link{mc_link_function}.
+#' @param inv_C A matrix. In general the output of the
+#' \link{mc_build_C}.
+#' @return A vector with the correction terms to be used on the Pearson
+#' estimating function.
+#' @details It is an internal function useful inside the fitting
+#' algorithm.
mc_correction <- function(D_C, inv_J_beta, D, inv_C) {
- term1 <- lapply(D_C, mc_sandwich, bord1 = t(D) %*% inv_C, bord2 = inv_C %*% D)
- output <- lapply(term1, function(x, inv_J_beta) sum(x * inv_J_beta), inv_J_beta = inv_J_beta)
+ term1 <- lapply(D_C, mc_sandwich, bord1 = t(D) %*% inv_C,
+ bord2 = inv_C %*% D)
+ output <- lapply(term1,
+ function(x, inv_J_beta) sum(x * inv_J_beta),
+ inv_J_beta = inv_J_beta)
return(unlist(output))
}
diff --git a/R/mc_cross_sensitivity.R b/R/mc_cross_sensitivity.R
index 5df0b1f7ab875cd3ad6e411421e0c5c182472e95..ba87b88875b5af6da5a482e1e66e869c8d51cf9e 100644
--- a/R/mc_cross_sensitivity.R
+++ b/R/mc_cross_sensitivity.R
@@ -1,23 +1,32 @@
-#' Cross-sensitivity
+#' @title Cross-sensitivity
+#' @author Wagner Hugo Bonat
+#'
+#' @description Compute the cross-sensitivity matrix between regression
+#' and covariance parameters. Equation 10 of Bonat and Jorgensen
+#' (2015).
#'
-#' @description Compute the cross-sensitivity matrix between regression and covariance parameters.
-#' Equation 10 of Bonat and Jorgensen (2015).
#' @param Product_cov A list of matrices.
#' @param Product_beta A list of matrices.
-#' @param n_beta_effective Numeric. Effective number of regression parameters.
-#' @return The cross-sensitivity matrix. Equation (10) of Bonat and Jorgensen (2015).
+#' @param n_beta_effective Numeric. Effective number of regression
+#' parameters.
+#' @return The cross-sensitivity matrix. Equation (10) of Bonat and
+#' Jorgensen (2015).
-mc_cross_sensitivity <- function(Product_cov, Product_beta, n_beta_effective = length(Product_beta)) {
+mc_cross_sensitivity <- function(Product_cov, Product_beta,
+ n_beta_effective =
+ length(Product_beta)) {
n_beta <- length(Product_beta)
n_cov <- length(Product_cov)
if (n_beta == 0) {
- cross_sensitivity <- Matrix(0, ncol = n_beta_effective, nrow = n_cov)
+ cross_sensitivity <- Matrix(0, ncol = n_beta_effective,
+ nrow = n_cov)
}
if (n_beta != 0) {
cross_sensitivity <- Matrix(NA, nrow = n_cov, ncol = n_beta)
for (i in 1:n_cov) {
for (j in 1:n_beta) {
- cross_sensitivity[i, j] <- -sum(Product_cov[[i]] * Product_beta[[j]])
+ cross_sensitivity[i, j] <-
+ -sum(Product_cov[[i]] * Product_beta[[j]])
}
}
}
diff --git a/R/mc_cross_variability.R b/R/mc_cross_variability.R
index ae9da6ac0f681f8a2c9c82be893da1f3bdb9bdba..d6dc3de235e718c3119c4256c9512fbd77837950 100644
--- a/R/mc_cross_variability.R
+++ b/R/mc_cross_variability.R
@@ -1,22 +1,26 @@
-#' Compute the cross-variability matrix
+#' @title Compute the cross-variability matrix
+#' @author Wagner Hugo Bonat
#'
-#' @description Compute the cross-variability matrix between covariance and regression parameters.
+#' @description Compute the cross-variability matrix between covariance
+#' and regression parameters.
#'
#' @param Product_cov A list of matrices.
#' @param inv_C A matrix.
#' @param res A vector.
#' @param D A matrix.
-#' @return The cross-variability matrix between regression and covariance parameters.
+#' @return The cross-variability matrix between regression and
+#' covariance parameters.
mc_cross_variability <- function(Product_cov, inv_C, res, D) {
Wlist <- lapply(Product_cov, mc_multiply2, bord2 = inv_C)
- A = t(D) %*% inv_C
+ A <- t(D) %*% inv_C
n_beta <- dim(A)[1]
n_cov <- length(Product_cov)
cross_variability <- Matrix(NA, ncol = n_cov, nrow = n_beta)
for (j in 1:n_beta) {
for (i in 1:n_cov) {
- cross_variability[j, i] <- covprod(A[j, ], Wlist[[i]], res = res)
+ cross_variability[j, i] <-
+ covprod(A[j, ], Wlist[[i]], res = res)
}
}
return(cross_variability)
diff --git a/R/mc_derivative_C_rho.R b/R/mc_derivative_C_rho.R
index bcbbcaaafcb376611912236f97a143a65dec73f1..913920649a163c9667f04a73e3fa1674b0512d5d 100644
--- a/R/mc_derivative_C_rho.R
+++ b/R/mc_derivative_C_rho.R
@@ -1,17 +1,24 @@
-#' Derivative of C with respect to rho.
+#' @title Derivative of C with respect to rho.
+#' @author Wagner Hugo Bonat
#'
-#'@description Compute the derivative of the C matrix with respect to the correlation parameters rho.
+#'@description Compute the derivative of the C matrix with respect to
+#' the correlation parameters rho.
#'
#'@param D_Sigmab A matrix.
#'@param Bdiag_chol_Sigma_within A block-diagonal matrix.
#'@param t_Bdiag_chol_Sigma_within A block-diagonal matrix.
#'@param II A diagonal matrix.
#'@return A matrix.
-#'@details It is an internal function used to build the derivatives of the C matrix.
+#'@details It is an internal function used to build the derivatives of
+#' the C matrix.
-mc_derivative_C_rho <- function(D_Sigmab, Bdiag_chol_Sigma_within, t_Bdiag_chol_Sigma_within, II) {
- output <- lapply(D_Sigmab, function(x) {
- t_Bdiag_chol_Sigma_within %*% kronecker(x, II) %*% Bdiag_chol_Sigma_within
- })
+mc_derivative_C_rho <- function(D_Sigmab, Bdiag_chol_Sigma_within,
+ t_Bdiag_chol_Sigma_within, II) {
+ output <- lapply(D_Sigmab,
+ function(x) {
+ t_Bdiag_chol_Sigma_within %*%
+ kronecker(x, II) %*%
+ Bdiag_chol_Sigma_within
+ })
return(output)
}
diff --git a/R/mc_derivative_cholesky.R b/R/mc_derivative_cholesky.R
index 5defd4e5ef8cdc78db5ab04cdd82f7e5c494ad94..d48b1c3e8a7f79678fe1a9c09d1f540c9dc6899b 100644
--- a/R/mc_derivative_cholesky.R
+++ b/R/mc_derivative_cholesky.R
@@ -1,5 +1,8 @@
-#' Derivatives of the Cholesky decomposition
-#' @description This function compute the derivative of the Cholesky decomposition.
+#' @title Derivatives of the Cholesky decomposition
+#' @author Wagner Hugo Bonat
+#'
+#' @description This function compute the derivative of the Cholesky
+#' decomposition.
#'
#' @param derivada A matrix.
#' @param inv_chol_Sigma A matrix.
@@ -7,7 +10,8 @@
#' @return A list of matrix.
#' @details It is an internal function.
-mc_derivative_cholesky <- function(derivada, inv_chol_Sigma, chol_Sigma) {
+mc_derivative_cholesky <- function(derivada, inv_chol_Sigma,
+ chol_Sigma) {
faux <- function(derivada, inv_chol_Sigma, chol_Sigma) {
t1 <- inv_chol_Sigma %*% derivada %*% t(inv_chol_Sigma)
t1 <- tril(t1)
@@ -15,6 +19,8 @@ mc_derivative_cholesky <- function(derivada, inv_chol_Sigma, chol_Sigma) {
output <- chol_Sigma %*% t1
return(output)
}
- list_D_chol <- lapply(derivada, faux, inv_chol_Sigma = inv_chol_Sigma, chol_Sigma = chol_Sigma)
+ list_D_chol <- lapply(derivada, faux,
+ inv_chol_Sigma = inv_chol_Sigma,
+ chol_Sigma = chol_Sigma)
return(list_D_chol)
}
diff --git a/R/mc_derivative_expm.R b/R/mc_derivative_expm.R
index 46bde0a5f2b83a524c157b4b9cf345b93eac78a6..a40876285d0db27ac8bb9c06f0889f520d40506a 100644
--- a/R/mc_derivative_expm.R
+++ b/R/mc_derivative_expm.R
@@ -1,6 +1,8 @@
-#' Derivative of exponential-matrix function
+#' @title Derivative of exponential-matrix function
+#' @author Wagner Hugo Bonat
#'
-#' @description Compute the derivative of the exponential-matrix covariance link function.
+#' @description Compute the derivative of the exponential-matrix
+#' covariance link function.
#'
#' @param UU A matrix.
#' @param inv_UU A matrix.
@@ -9,19 +11,21 @@
#' @param n A numeric.
#' @param sparse Logical.
#' @return A matrix.
-#' @seealso \code{\link[Matrix]{expm}}, \code{link[mcglm]{mc_dexp_gold}} and
-#' \code{link[mcglm]{mc_dexpm}}.
-#' @details Many arguments required by this function are provide by the \code{link[mcglm]{mc_dexpm}}.
-#' The argument dU is the derivative of the U matrix with respect to the models parameters. It should
-#' be computed by the user.
+#' @seealso \code{\link[Matrix]{expm}}, \code{link[mcglm]{mc_dexp_gold}}
+#' and \code{link[mcglm]{mc_dexpm}}.
+#' @details Many arguments required by this function are provide by the
+#' \code{link[mcglm]{mc_dexpm}}. The argument dU is the derivative
+#' of the U matrix with respect to the models parameters. It should
+#' be computed by the user.
-mc_derivative_expm <- function(dU, UU, inv_UU, Q, n = dim(UU)[1], sparse = FALSE) {
- H = inv_UU %*% dU %*% UU
+mc_derivative_expm <- function(dU, UU, inv_UU, Q, n = dim(UU)[1],
+ sparse = FALSE) {
+ H <- inv_UU %*% dU %*% UU
P <- Matrix(0, ncol = n, nrow = n)
diag(P) <- diag(H) * exp(Q)
P[upper.tri(P)] <- H[upper.tri(H)] * c(dist(exp(Q))/dist(Q))
P[is.na(P)] <- 0
P <- forceSymmetric(P)
- D_Omega = Matrix(UU %*% P %*% inv_UU, sparse = FALSE)
+ D_Omega <- Matrix(UU %*% P %*% inv_UU, sparse = FALSE)
return(D_Omega)
}
diff --git a/R/mc_derivative_sigma_beta.R b/R/mc_derivative_sigma_beta.R
index fee51bd54f0a834afde05b67d1c45807e757f4a0..70f3e0f1a4e0f779dd85c4ae6d0b13e54548aad7 100644
--- a/R/mc_derivative_sigma_beta.R
+++ b/R/mc_derivative_sigma_beta.R
@@ -1,29 +1,37 @@
-#' Derivatives of V^{1/2} with respect to beta.
+#' @title Derivatives of V^{1/2} with respect to beta.
+#' @author Wagner Hugo Bonat
#'
-#' @description Compute the derivatives of V^{1/2} matrix with respect to the regression
-#' parameters beta.
+#' @description Compute the derivatives of \eqn{V^{1/2}} matrix with
+#' respect to the regression parameters beta.
+#'
#' @param D A matrix.
#' @param D_V_sqrt_mu A matrix.
#' @param Omega A matrix.
#' @param V_sqrt A matrix.
#' @param variance A string specifying the variance function name.
-#' @return A list of matrices, containg the derivatives of V^{1/2} with respect to the regression
-#' parameters.
+#' @return A list of matrices, containg the derivatives of \eqn{V^{1/2}}
+#' with respect to the regression parameters.
-mc_derivative_sigma_beta <- function(D, D_V_sqrt_mu, Omega, V_sqrt, variance) {
+mc_derivative_sigma_beta <- function(D, D_V_sqrt_mu, Omega, V_sqrt,
+ variance) {
n_beta <- dim(D)[2]
n_obs <- dim(D)[1]
output <- list()
- if (variance == "power" | variance == "binomialP" | variance == "binomialPQ") {
+ if (variance == "power" | variance == "binomialP" |
+ variance == "binomialPQ") {
for (i in 1:n_beta) {
D_V_sqrt_beta <- Diagonal(n_obs, D_V_sqrt_mu * D[, i])
- output[[i]] <- mc_sandwich_power(middle = Omega, bord1 = V_sqrt, bord2 = D_V_sqrt_beta)
+ output[[i]] <-
+ mc_sandwich_power(middle = Omega,
+ bord1 = V_sqrt, bord2 = D_V_sqrt_beta)
}
}
if (variance == "poisson_tweedie") {
for (i in 1:n_beta) {
D_V_sqrt_beta <- Diagonal(n_obs, D_V_sqrt_mu * D[, i])
- output[[i]] <- Diagonal(n_obs, D[, i]) + mc_sandwich_power(middle = Omega, bord1 = V_sqrt, bord2 = D_V_sqrt_beta)
+ output[[i]] <- Diagonal(n_obs, D[, i]) +
+ mc_sandwich_power(middle = Omega, bord1 = V_sqrt,
+ bord2 = D_V_sqrt_beta)
}
}
return(output)
diff --git a/R/mc_dexp_gold.R b/R/mc_dexp_gold.R
index 77ff1a0b10abc5f7984e0615a882fbc8211b5a38..9c0d8a49ac7c6be5c1caadf38f4ddf8296943dde 100644
--- a/R/mc_dexp_gold.R
+++ b/R/mc_dexp_gold.R
@@ -1,11 +1,14 @@
-#' Exponential-matrix and its derivatives
+#' @title Exponential-matrix and its derivatives
+#' @author Wagner Hugo Bonat
#'
-#' @description Given a matrix \eqn{M} and its derivative \eqn{dM} the function \code{dexp_gold}
-#' returns the exponential-matrix \eqn{expm(M)} and its derivative. This function is based on
-#' the \code{\link[Matrix]{expm}} function. It is not really used in the package, but I keep this
-#' function to test my own implementation based on eigen values decomposition.
+#' @description Given a matrix \eqn{M} and its derivative \eqn{dM} the
+#' function \code{dexp_gold} returns the exponential-matrix
+#' \eqn{expm(M)} and its derivative. This function is based on the
+#' \code{\link[Matrix]{expm}} function. It is not really used in the
+#' package, but I keep this function to test my own implementation
+#' based on eigen values decomposition.
#'
-#' @param M A matrix.
+#' @param M A matrix.
#' @param dM A matrix.
#' @return A list with two elements: \eqn{expm(M)} and its derivatives.
#' @seealso \code{\link[Matrix]{expm}}, \code{\link[base]{eigen}}.
@@ -16,10 +19,10 @@
#' @export
mc_dexp_gold <- function(M, dM) {
- N = dim(M)
- AM = rbind(cbind(M, matrix(0, N[1], N[2])), cbind(dM, M))
- PSI = expm(AM)
- F = PSI[1:N[1], 1:N[1]]
- dF = PSI[c(N[1] + 1):c(2 * N[1]), 1:N[1]]
+ N <- dim(M)
+ AM <- rbind(cbind(M, matrix(0, N[1], N[2])), cbind(dM, M))
+ PSI <- expm(AM)
+ F <- PSI[1:N[1], 1:N[1]]
+ dF <- PSI[c(N[1] + 1):c(2 * N[1]), 1:N[1]]
return(list(F, dF))
}