Add tags and tidy code an text.

R/mc_core_cross_variability.R

-#' 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))
 }

R/mc_core_pearson.R

-#' 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))
 }

R/mc_correction.R

-#' 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))
 }

R/mc_cross_sensitivity.R

-#' 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]])
             }
         }
     }

R/mc_cross_variability.R

-#' 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)

R/mc_derivative_C_rho.R

-#' 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)
 }

R/mc_derivative_cholesky.R

-#' 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)
 }

R/mc_derivative_expm.R

-#' 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
+#' @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)
 }

R/mc_derivative_sigma_beta.R

-#' 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 \eqn{V^{1/2}} matrix with
+#'     respect to the regression parameters beta.
 #' 
-#' @description Compute the derivatives of 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)

R/mc_dexp_gold.R

-#' 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 dM A matrix.
@@ -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))
 }