cosmetic changes

AnestisTouloumis · Jul 24, 2019 · 8f68085 · 8f68085
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: SimCorMultRes
 Type: Package
 Title: Simulates Correlated Multinomial Responses
 Description: Simulates correlated multinomial responses conditional on a marginal model specification.
-Version: 1.6.8
+Version: 1.6.9
 Depends: R(>= 2.15.0)
 Imports: 
     evd,

diff --git a/R/SimCorMultRes_internals.R b/R/SimCorMultRes_internals.R
diff --git a/R/rbin.R b/R/rbin.R
@@ -102,49 +102,62 @@
 #' beta_coefficients <- 0.2
 #' latent_correlation_matrix <- toeplitz(c(1, 0.9, 0.9, 0.9))
 #' x <- rep(rnorm(sample_size), each = cluster_size)
-#' simulated_binary_dataset <- rbin(clsize = cluster_size,
-#'     intercepts = beta_intercepts, betas = beta_coefficients,
-#'     xformula = ~ x, cor.matrix = latent_correlation_matrix, link = 'probit')
+#' simulated_binary_dataset <- rbin(
+#'   clsize = cluster_size,
+#'   intercepts = beta_intercepts, betas = beta_coefficients,
+#'   xformula = ~x, cor.matrix = latent_correlation_matrix, link = "probit"
+#' )
 #' library(gee)
-#' binary_gee_model <- gee(y ~ x, family = binomial('probit'), id = id,
-#' data = simulated_binary_dataset$simdata)
+#' binary_gee_model <- gee(y ~ x,
+#'   family = binomial("probit"), id = id,
+#'   data = simulated_binary_dataset$simdata
+#' )
 #' summary(binary_gee_model)$coefficients
 #'
 #' ## See Example 3.6 in the Vignette.
 #' set.seed(8)
 #' library(evd)
-#' simulated_latent_variables1 <- rmvevd(sample_size, dep = sqrt(1 - 0.9),
-#'     model = 'log', d = cluster_size)
-#' simulated_latent_variables2 <- rmvevd(sample_size, dep = sqrt(1 - 0.9),
-#'     model = 'log', d = cluster_size)
+#' simulated_latent_variables1 <- rmvevd(sample_size,
+#'   dep = sqrt(1 - 0.9),
+#'   model = "log", d = cluster_size
+#' )
+#' simulated_latent_variables2 <- rmvevd(sample_size,
+#'   dep = sqrt(1 - 0.9),
+#'   model = "log", d = cluster_size
+#' )
 #' simulated_latent_variables <- simulated_latent_variables1 - simulated_latent_variables2 # nolintr
-#' simulated_binary_dataset <- rbin(clsize = cluster_size,
-#'     intercepts = beta_intercepts, betas = beta_coefficients,
-#'     xformula = ~ x, rlatent = simulated_latent_variables)
-#' binary_gee_model <- gee(y ~ x, family = binomial('logit'), id = id,
-#'     data = simulated_binary_dataset$simdata)
+#' simulated_binary_dataset <- rbin(
+#'   clsize = cluster_size,
+#'   intercepts = beta_intercepts, betas = beta_coefficients,
+#'   xformula = ~x, rlatent = simulated_latent_variables
+#' )
+#' binary_gee_model <- gee(y ~ x,
+#'   family = binomial("logit"), id = id,
+#'   data = simulated_binary_dataset$simdata
+#' )
 #' summary(binary_gee_model)$coefficients
-#'
 #' @export
 rbin <- function(clsize = clsize, intercepts = intercepts, betas = betas,
-    xformula = formula(xdata), xdata = parent.frame(), link = "logit",
-    cor.matrix = cor.matrix, rlatent = NULL){ # nolint
-    check_cluster_size(clsize)
-    beta_intercepts <- check_intercepts(intercepts, clsize, "rbin")
-    beta_coefficients <- check_betas(betas, clsize)
-    linear_predictor_formula <- check_xformula(xformula)
-    if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
-    linear_predictor <- create_linear_predictor(beta_coefficients, clsize,
-                                                linear_predictor_formula, xdata,
-                                                "rbin")
-    sample_size <- nrow(linear_predictor)
-    simulated_latent_variables <- create_rlatent(rlatent, sample_size, link,
-                                                 clsize, cor.matrix, "rbin")
-    simulated_binary_responses <- apply_threshold(linear_predictor,
-                                                  simulated_latent_variables,
-                                                  clsize, "rbin",
-                                                  beta_intercepts)
-    create_output(simulated_binary_responses, sample_size, clsize,
-                  simulated_latent_variables, linear_predictor_formula, xdata,
-                  "rbin")
+                 xformula = formula(xdata), xdata = parent.frame(), link = "logit", # nolintr
+                 cor.matrix = cor.matrix, rlatent = NULL) { # nolint
+  check_cluster_size(clsize)
+  beta_intercepts <- check_intercepts(intercepts, clsize, "rbin")
+  beta_coefficients <- check_betas(betas, clsize)
+  linear_predictor_formula <- check_xformula(xformula)
+  if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
+  linear_predictor <- create_linear_predictor(
+    beta_coefficients, clsize, linear_predictor_formula, xdata, "rbin"
+  )
+  sample_size <- nrow(linear_predictor)
+  simulated_latent_variables <- create_rlatent(
+    rlatent, sample_size, link, clsize, cor.matrix, "rbin"
+  )
+  simulated_binary_responses <- apply_threshold(
+    linear_predictor, simulated_latent_variables, clsize, "rbin",
+    beta_intercepts
+  )
+  create_output(
+    simulated_binary_responses, sample_size, clsize, simulated_latent_variables,
+    linear_predictor_formula, xdata, "rbin"
+  )
 }
diff --git a/R/rmult.acl.R b/R/rmult.acl.R
@@ -105,28 +105,34 @@
 #' xdata <- data.frame(x1, x2)
 #' identity_matrix <- diag(4)
 #' equicorrelation_matrix <- toeplitz(c(1, rep(0.95, cluster_size - 1)))
-#' latent_correlation_matrix <- kronecker(equicorrelation_matrix,
-#'     identity_matrix)
-#' simulated_ordinal_dataset <- rmult.acl(clsize = cluster_size,
-#'     intercepts = beta_intercepts, betas = beta_coefficients,
-#'     xformula = ~ x1 + x2, xdata = xdata,
-#'     cor.matrix = latent_correlation_matrix)
-#' suppressPackageStartupMessages(library('multgee'))
+#' latent_correlation_matrix <- kronecker(
+#'   equicorrelation_matrix,
+#'   identity_matrix
+#' )
+#' simulated_ordinal_dataset <- rmult.acl(
+#'   clsize = cluster_size,
+#'   intercepts = beta_intercepts, betas = beta_coefficients,
+#'   xformula = ~ x1 + x2, xdata = xdata,
+#'   cor.matrix = latent_correlation_matrix
+#' )
+#' suppressPackageStartupMessages(library("multgee"))
 #' ordinal_gee_model <- ordLORgee(y ~ x1 + x2,
-#'     data = simulated_ordinal_dataset$simdata, id = id, repeated = time,
-#' LORstr = 'time.exch', link='acl')
+#'   data = simulated_ordinal_dataset$simdata, id = id, repeated = time,
+#'   LORstr = "time.exch", link = "acl"
+#' )
 #' round(coef(ordinal_gee_model), 2)
-#'
 #' @export
 rmult.acl <- function(clsize = clsize, intercepts = intercepts, betas = betas, # nolint
-    xformula = formula(xdata), xdata = parent.frame(), cor.matrix = cor.matrix, # nolint
-    rlatent = NULL) {
-    check_cluster_size(clsize)
-    beta_intercepts <- check_intercepts(intercepts, clsize, "rmult.acl")
-    categories_no <- ncol(beta_intercepts) + 1
-    beta_coefficients <- check_betas(betas, clsize)
-    betas_bcl <- create_betas_acl2bcl(beta_intercepts, categories_no,
-                                      beta_coefficients)
-    rmult.bcl(clsize, categories_no, betas_bcl, xformula, xdata, cor.matrix,
-              rlatent)
+                      xformula = formula(xdata), xdata = parent.frame(), cor.matrix = cor.matrix, # nolint
+                      rlatent = NULL) {
+  check_cluster_size(clsize)
+  beta_intercepts <- check_intercepts(intercepts, clsize, "rmult.acl")
+  categories_no <- ncol(beta_intercepts) + 1
+  beta_coefficients <- check_betas(betas, clsize)
+  betas_bcl <- create_betas_acl2bcl(
+    beta_intercepts, categories_no, beta_coefficients
+  )
+  rmult.bcl(
+    clsize, categories_no, betas_bcl, xformula, xdata, cor.matrix, rlatent
+  )
 }
diff --git a/R/rmult.bcl.R b/R/rmult.bcl.R
@@ -104,38 +104,42 @@
 #' equicorrelation_matrix <- toeplitz(c(1, rep(0.95, cluster_size - 1)))
 #' identity_matrix <- diag(categories_no)
 #' latent_correlation_matrix <- kronecker(equicorrelation_matrix, identity_matrix) # nolint
-#' simulated_nominal_dataset <- rmult.bcl(clsize = cluster_size,
-#'     ncategories = categories_no, betas = betas, xformula = ~ x1 + x2,
-#'     xdata = xdata, cor.matrix = latent_correlation_matrix)
-#' suppressPackageStartupMessages(library('multgee'))
+#' simulated_nominal_dataset <- rmult.bcl(
+#'   clsize = cluster_size,
+#'   ncategories = categories_no, betas = betas, xformula = ~ x1 + x2,
+#'   xdata = xdata, cor.matrix = latent_correlation_matrix
+#' )
+#' suppressPackageStartupMessages(library("multgee"))
 #' nominal_gee_model <- nomLORgee(y ~ x1 + x2,
-#'     data = simulated_nominal_dataset$simdata, id = id, repeated = time,
-#' LORstr = 'time.exch')
+#'   data = simulated_nominal_dataset$simdata, id = id, repeated = time,
+#'   LORstr = "time.exch"
+#' )
 #' round(coef(nominal_gee_model), 2)
-#'
 #' @export
 rmult.bcl <- function(clsize = clsize, ncategories = ncategories, betas = betas, # nolint
-    xformula = formula(xdata), xdata = parent.frame(), cor.matrix = cor.matrix, # nolint
-    rlatent = NULL) {
-    check_cluster_size(clsize)
-    categories_no <- check_ncategories(ncategories)
-    betas <- check_betas(betas, clsize)
-    linear_predictor_formula <- check_xformula(xformula)
-    if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
-    linear_predictor <- create_linear_predictor(betas, clsize,
-                                                linear_predictor_formula, xdata,
-                                                "rmult.bcl", categories_no)
-    sample_size <- nrow(linear_predictor)
-    simulated_latent_responses <- create_rlatent(rlatent, sample_size,
-                                                 "cloglog", clsize, cor.matrix,
-                                                 "rmult.bcl", categories_no)
-    simulated_nominal_responses <- apply_threshold(linear_predictor,
-                                                   simulated_latent_responses,
-                                                   clsize, "rmult.bcl",
-                                                   categories_no =
-                                                       categories_no)
-    linear_predictor_formula <- update(linear_predictor_formula, ~. - 1)
-    create_output(simulated_nominal_responses, sample_size, clsize,
-                  simulated_latent_responses, linear_predictor_formula, xdata,
-                  "rmult.bcl", categories_no = categories_no)
+                      xformula = formula(xdata), xdata = parent.frame(), cor.matrix = cor.matrix, # nolint
+                      rlatent = NULL) {
+  check_cluster_size(clsize)
+  categories_no <- check_ncategories(ncategories)
+  betas <- check_betas(betas, clsize)
+  linear_predictor_formula <- check_xformula(xformula)
+  if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
+  linear_predictor <- create_linear_predictor(
+    betas, clsize, linear_predictor_formula, xdata, "rmult.bcl", categories_no
+  )
+  sample_size <- nrow(linear_predictor)
+  simulated_latent_responses <- create_rlatent(
+    rlatent, sample_size, "cloglog", clsize, cor.matrix, "rmult.bcl",
+    categories_no
+  )
+  simulated_nominal_responses <- apply_threshold(
+    linear_predictor, simulated_latent_responses, clsize, "rmult.bcl",
+    categories_no = categories_no
+  )
+  linear_predictor_formula <- update(linear_predictor_formula, ~ . - 1)
+  create_output(
+    simulated_nominal_responses, sample_size, clsize,
+    simulated_latent_responses, linear_predictor_formula, xdata, "rmult.bcl",
+    categories_no = categories_no
+  )
 }
diff --git a/R/rmult.clm.R b/R/rmult.clm.R
@@ -104,43 +104,56 @@
 #' beta_coefficients <- matrix(c(1, 2, 3, 4), 4, 1)
 #' x <- rep(rnorm(sample_size), each = cluster_size)
 #' latent_correlation_matrix <- toeplitz(c(1, 0.85, 0.5, 0.15))
-#' simulated_ordinal_dataset <- rmult.clm(clsize = cluster_size,
-#'     intercepts = beta_intercepts, betas = beta_coefficients, xformula = ~ x,
-#'     cor.matrix = latent_correlation_matrix, link = 'probit')
+#' simulated_ordinal_dataset <- rmult.clm(
+#'   clsize = cluster_size,
+#'   intercepts = beta_intercepts, betas = beta_coefficients, xformula = ~x,
+#'   cor.matrix = latent_correlation_matrix, link = "probit"
+#' )
 #' head(simulated_ordinal_dataset$simdata, n = 8)
 #'
 #' ## Same sampling scheme except that the parameter vector is time-stationary.
 #' set.seed(12345)
-#' simulated_ordinal_dataset <- rmult.clm(clsize = cluster_size, betas = 1,
-#'     xformula = ~ x, cor.matrix = latent_correlation_matrix,
-#'     intercepts = beta_intercepts, link = 'probit')
+#' simulated_ordinal_dataset <- rmult.clm(
+#'   clsize = cluster_size, betas = 1,
+#'   xformula = ~x, cor.matrix = latent_correlation_matrix,
+#'   intercepts = beta_intercepts, link = "probit"
+#' )
 #' ## Fit a GEE model (Touloumis et al., 2013) to estimate the regression
 #' ## coefficients.
 #' library(multgee)
-#' ordinal_gee_model <- ordLORgee(y ~ x, id = id, repeated = time,
-#'     link = 'probit', data = simulated_ordinal_dataset$simdata)
+#' ordinal_gee_model <- ordLORgee(y ~ x,
+#'   id = id, repeated = time,
+#'   link = "probit", data = simulated_ordinal_dataset$simdata
+#' )
 #' coef(ordinal_gee_model)
-#'
 #' @export
 rmult.clm <- function(clsize = clsize, intercepts = intercepts, betas = betas, # nolint
-    xformula = formula(xdata), xdata = parent.frame(), link = "logit",
-    cor.matrix = cor.matrix, rlatent = NULL) { # nolint
-    check_cluster_size(clsize)
-    beta_intercepts <- check_intercepts(intercepts, clsize, "rmult.clm")
-    beta_coefficients <- check_betas(betas, clsize)
-    linear_predictor_formula <- check_xformula(xformula)
-    if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
-    linear_predictor <- create_linear_predictor(beta_coefficients, clsize,
-                                                linear_predictor_formula, xdata,
-                                                "rmult.clm")
-    sample_size <- nrow(linear_predictor)
-    simulated_latent_responses <- create_rlatent(rlatent, sample_size, link,
-                                                 clsize, cor.matrix,
-                                                 "rmult.clm")
-    simulated_ordinal_responses <-
-        apply_threshold(linear_predictor, simulated_latent_responses, clsize,
-                        "rmult.clm", beta_intercepts)
-    create_output(simulated_ordinal_responses, sample_size, clsize,
-                  simulated_latent_responses, linear_predictor_formula, xdata,
-                  "rmult.clm")
+                      xformula = formula(xdata), xdata = parent.frame(), link = "logit", # nolintr
+                      cor.matrix = cor.matrix, rlatent = NULL) { # nolint
+  check_cluster_size(clsize)
+  beta_intercepts <- check_intercepts(intercepts, clsize, "rmult.clm")
+  beta_coefficients <- check_betas(betas, clsize)
+  linear_predictor_formula <- check_xformula(xformula)
+  if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
+  linear_predictor <- create_linear_predictor(
+    beta_coefficients, clsize,
+    linear_predictor_formula, xdata,
+    "rmult.clm"
+  )
+  sample_size <- nrow(linear_predictor)
+  simulated_latent_responses <- create_rlatent(
+    rlatent, sample_size, link,
+    clsize, cor.matrix,
+    "rmult.clm"
+  )
+  simulated_ordinal_responses <-
+    apply_threshold(
+      linear_predictor, simulated_latent_responses, clsize,
+      "rmult.clm", beta_intercepts
+    )
+  create_output(
+    simulated_ordinal_responses, sample_size, clsize,
+    simulated_latent_responses, linear_predictor_formula, xdata,
+    "rmult.clm"
+  )
 }
diff --git a/R/rmult.crm.R b/R/rmult.crm.R
@@ -107,40 +107,43 @@
 #' categories_no <- 5
 #' identity_matrix <- diag(1, (categories_no - 1) * cluster_size)
 #' equicorrelation_matrix <- toeplitz(c(0, rep(0.24, categories_no - 2)))
-#' ones_matrix <-  matrix(1, cluster_size, cluster_size)
+#' ones_matrix <- matrix(1, cluster_size, cluster_size)
 #' latent_correlation_matrix <- identity_matrix +
-#'     kronecker(equicorrelation_matrix, ones_matrix)
-#' simulated_ordinal_dataset <- rmult.crm(clsize = cluster_size,
-#'     intercepts = beta_intercepts, betas = beta_coefficients, xformula = ~ x,
-#'     cor.matrix = latent_correlation_matrix, link = 'probit')
+#'   kronecker(equicorrelation_matrix, ones_matrix)
+#' simulated_ordinal_dataset <- rmult.crm(
+#'   clsize = cluster_size,
+#'   intercepts = beta_intercepts, betas = beta_coefficients, xformula = ~x,
+#'   cor.matrix = latent_correlation_matrix, link = "probit"
+#' )
 #' head(simulated_ordinal_dataset$Ysim)
-#'
-#'@export
+#' @export
 rmult.crm <- function(clsize = clsize, intercepts = intercepts, betas = betas, # nolint
-    xformula = formula(xdata), xdata = parent.frame(), link = "logit",
-    cor.matrix = cor.matrix, rlatent = NULL) { # nolint
-    check_cluster_size(clsize)
-    beta_coefficients <- check_betas(betas, clsize)
-    linear_predictor_formula <- check_xformula(xformula)
-    if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
-    linear_predictor <- create_linear_predictor(beta_coefficients, clsize,
-                                                linear_predictor_formula, xdata,
-                                                "rmult.clm")
-    sample_size <- nrow(linear_predictor)
-    beta_intercepts <- check_intercepts(intercepts, clsize, "rmult.crm",
-                                        sample_size)
-    categories_no <- ncol(beta_intercepts) / clsize + 1
-    linear_predictor_extended <- t(apply(linear_predictor, 1, function(x)
-        rep(x, each = categories_no - 1)))
-    simulated_latent_responses <- create_rlatent(rlatent, sample_size, link,
-                                                 clsize, cor.matrix,
-                                                 "rmult.crm", categories_no)
-    simulated_ordinal_responses <- apply_threshold(linear_predictor_extended,
-                                                   simulated_latent_responses,
-                                                   clsize, "rmult.crm",
-                                                   beta_intercepts,
-                                                   categories_no)
-    create_output(simulated_ordinal_responses, sample_size, clsize,
-                  simulated_latent_responses, linear_predictor_formula, xdata,
-                  "rmult.crm", categories_no)
+                      xformula = formula(xdata), xdata = parent.frame(), link = "logit", # nolintr
+                      cor.matrix = cor.matrix, rlatent = NULL) { # nolint
+  check_cluster_size(clsize)
+  beta_coefficients <- check_betas(betas, clsize)
+  linear_predictor_formula <- check_xformula(xformula)
+  if (!is.environment(xdata)) xdata <- data.frame(na.omit(xdata))
+  linear_predictor <- create_linear_predictor(
+    beta_coefficients, clsize, linear_predictor_formula, xdata, "rmult.clm"
+  )
+  sample_size <- nrow(linear_predictor)
+  beta_intercepts <- check_intercepts(
+    intercepts, clsize, "rmult.crm", sample_size
+  )
+  categories_no <- ncol(beta_intercepts) / clsize + 1
+  linear_predictor_extended <- t(apply(linear_predictor, 1, function(x)
+    rep(x, each = categories_no - 1)))
+  simulated_latent_responses <- create_rlatent(
+    rlatent, sample_size, link, clsize, cor.matrix, "rmult.crm", categories_no
+  )
+  simulated_ordinal_responses <- apply_threshold(
+    linear_predictor_extended, simulated_latent_responses, clsize, "rmult.crm",
+    beta_intercepts, categories_no
+  )
+  create_output(
+    simulated_ordinal_responses, sample_size, clsize,
+    simulated_latent_responses, linear_predictor_formula, xdata, "rmult.crm",
+    categories_no
+  )
 }