version 1.0.0

DESCRIPTION

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+Package: lmvar
+Type: Package
+Title: Linear Regression with Non-Constant Variances
+Version: 1.0.0
+Author: Posthuma Partners <info@posthuma-partners.nl> 
+Maintainer: Marco Nijmeijer <nijmeijer@posthuma-partners.nl>
+Description: Runs a linear regression in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.
+License: GPL-3
+LazyData: TRUE
+Imports: Matrix (>= 1.2-4), matrixcalc (>= 1.0-3), nleqslv (>= 3.0.3),
+        stats (>= 3.2.5)
+RoxygenNote: 6.0.1
+Suggests: testthat, knitr, rmarkdown, R.rsp
+VignetteBuilder: knitr, R.rsp
+NeedsCompilation: no
+Packaged: 2017-02-16 16:39:12 UTC; Marc
+Repository: CRAN
+Date/Publication: 2017-02-17 14:38:14

MD5

@@ -0,0 +1,66 @@
+0da3dd6021303151430b92d13b01bc04 *DESCRIPTION
+41b7f05cd32d8769ef2208ad6368d7e9 *NAMESPACE
+799ae591e93eb076cea0d9f8150235dd *R/AIC.lmvar.R
+b0137d370c4c1a01bc5ceb87d8cc6285 *R/alias.lmvar.R
+c5a11e9f3431dd97d7e32a2df0f1fbd5 *R/beta_sigma_names.R
+08618c12995a54734f49b0735c535257 *R/coef.lmvar.R
+010573949388442cd98049ecc6b0e7b2 *R/df.residual.lmvar.R
+a9d9d476093ad924986c191b5241769b *R/dfree.R
+39b90ad72cc9880282de9bf614d68133 *R/examples/AIC_examples.R
+bd9d7d3681defffd188f22a59df22c77 *R/examples/alias_examples.R
+13fa82f04c348bab1ab18187053eea3b *R/examples/beta_sigma_names_examples.R
+c5ae01de2d2f10f7b4bfa919e3f388a6 *R/examples/coef_examples.R
+ee365be6d30d6714e4a6454e6a0bfef7 *R/examples/df.residual_examples.R
+9d526fd9bc9b4c72c55cc5cd2614b4fa *R/examples/dfree_examples.R
+dd73ddf4aff15db9a6a6407af1a59577 *R/examples/fisher_examples.R
+698ac483d76f91ef2a49fc965577ee33 *R/examples/fitted_examples.R
+8cb1e5039896f1872911159fc875f758 *R/examples/lmvar_examples.R
+e93509e8f18367eae4c7a21672482354 *R/examples/logLik_examples.R
+02000462392443088a7b9628b773cd8a *R/examples/nobs_examples.R
+f28168301e48d6a216067c9e6f46320a *R/examples/predict_examples.R
+014ab1c3a4fb3f2511a5df2dbe775723 *R/examples/residuals_examples.R
+3b54dd7601fce2246ecfe4fbebaf345f *R/examples/summary_examples.R
+2139927b488f26929811b608c885885f *R/examples/vcov_examples.R
+65c5d7d3efb959034969ce25081d0525 *R/fisher.R
+bb4e182ff8110e449ab82117c67b5f6f *R/fitted.lmvar.R
+d97d4f2dd4613ba9af5c79e8c683d4b7 *R/gaussian_var.R
+ae576df528a3f5b30cac7df2df9dce68 *R/lmvar.R
+8a5f8803d15359cd28f236e6a59d3d1d *R/logLik.lmvar.R
+11a615ab05ce7c6a45d5905a064587b6 *R/make_matrix_full_rank.R
+c6eb72596cb95e6d1f12968a110a1fcb *R/matrix_column_names.R
+4f63ac17353588bd3d9497d181ace9cb *R/nobs.lmvar.R
+f37f6f29c93effe8ebfdc3998dc28a20 *R/predict.lmvar.R
+af17a34e533280763874b7faa31208a1 *R/print.summary_lmvar.R
+68ba53a968caa6fc52a96117a4b2a537 *R/residuals.lmvar.R
+6f3aa4fe0edbf10588592d9bef786368 *R/summary.lmvar.R
+b07e75a5335b4375cedc45d4e876594f *R/vcov.lmvar.R
+058e5cb3b2ffde63a0e073c229b7ca24 *README.md
+9a51ea3e128c9edff4d3fe1f448b53a5 *build/vignette.rds
+828d6a905ed48145c432c23f00ef645c *inst/doc/Intro.R
+475cfafe2a4d03be48ab649bab4d5f81 *inst/doc/Intro.Rmd
+7fff116f96908a44f030b786be788671 *inst/doc/Intro.html
+0d2d2347fc49b246c9f6607324c60005 *inst/doc/Math.ltx
+604b46aa4979321bd4c90566bfa4cc44 *inst/doc/Math.pdf
+cdc8775371ef46701cbbfdd098441d22 *man/AIC.lmvar.Rd
+40be9e23383ce6cfb87f6158572eab2e *man/alias.lmvar.Rd
+96870ffeb6e33b5476b9014730e99d0f *man/beta_sigma_names.Rd
+dc231983bd8fc1c8234230199df56ebc *man/coef.lmvar.Rd
+ae69a6216348af4e1787e9550574a03e *man/df.residual.lmvar.Rd
+8a46914afe8717717af7868eb56fe898 *man/dfree.Rd
+a5e5ccd1c0fafcdaee3bbeb7cab47cd5 *man/fisher.Rd
+f84bf4c6fc34202b0f52fc560ac08b84 *man/fitted.lmvar.Rd
+9105d49018403123fc7fe8430ec2299a *man/lmvar.Rd
+d3041936af6e96cdc0ae4464e0f52796 *man/logLik.lmvar.Rd
+c33fe1105392aeab24e540c8f09459a4 *man/nobs.lmvar.Rd
+85f6305560b23fac8565e52f25fc606a *man/predict.lmvar.Rd
+080d695ec1395b492167dbf7e898c71a *man/print.summary_lmvar.Rd
+9b35a0b154bc37c64d22c063fe84850e *man/residuals.lmvar.Rd
+3001d4fdbb5a458419ddcac4c7e161f7 *man/summary.lmvar.Rd
+103ad2096a2c2810478be7d856ea2b32 *man/vcov.lmvar.Rd
+bde5eb3a375b2d0298b8c17d84eda405 *tests/create_test_data.R
+b5e9bbfbc1b319bc3baec90d163e1426 *tests/testthat.R
+9181c1bb3ff9b24d1d5da2dcf1a0ce1e *tests/testthat/test_extractors.R
+3dc4e0f80fc5c12cef9aadd1a98116f7 *tests/testthat/test_lmvar.R
+18b7f56763d877e27e3f5083153c384f *tests/testthat/test_predict.R
+475cfafe2a4d03be48ab649bab4d5f81 *vignettes/Intro.Rmd
+0d2d2347fc49b246c9f6607324c60005 *vignettes/Math.ltx

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+S3method(AIC,lmvar)
+S3method(alias,lmvar)
+S3method(coef,lmvar)
+S3method(df.residual,lmvar)
+S3method(fitted,lmvar)
+S3method(logLik,lmvar)
+S3method(nobs,lmvar)
+S3method(predict,lmvar)
+S3method(print,summary_lmvar)
+S3method(residuals,lmvar)
+S3method(summary,lmvar)
+S3method(vcov,lmvar)
+export(beta_sigma_names)
+export(dfree)
+export(fisher)
+export(lmvar)
+importFrom(stats,alias)
+importFrom(stats,nobs)

R/AIC.lmvar.R

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+#' @title AIC for an object of class 'lmvar'
+#'
+#' @description AIC (Aikaike's 'An Information Criterion') for an object of class 'lmvar'
+#'
+#' @param object Object of class 'lmvar'
+#' @param ... For compatibility with \code{\link[stats]{AIC}} generic
+#' @param k Numeric, the penalty per parameter to be used. The default k = 2 is the classical AIC.
+#'
+#' @return the AIC of the object
+#'
+#' @export
+#'
+#' @example R/examples/AIC_examples.R
+#'
+AIC.lmvar <- function(object, ..., k = 2){
+  df = dfree(object)
+  return(k*df - 2 * logLik.lmvar(object)[1])
+}

R/alias.lmvar.R

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+#'
+#' @title Aliased coefficients in an 'lmvar' object
+#'
+#' @description Returns the columns present in the user-specified model-matrices \eqn{X_\mu} and \eqn{X_\sigma} that were removed by
+#' \code{lmvar} to make the matrices full-rank.
+#'
+#' @param object Object of class 'lmvar'
+#' @param mu Boolean, specifies whether the aliased columns from the model matrix \eqn{X_\mu} must be returned
+#' @param sigma Boolean, specifies whether the aliased columns from the model matrix \eqn{X_\sigma} must be returned
+#' @param ... Additional arguments, not used in the current implementation
+#'
+#' @return A character vector containing the names of the aliased columns
+#'
+#' @details If \code{mu = TRUE} and \code{sigma = TRUE}, the function returns the aliased columns of both \eqn{X_\mu}
+#' and \eqn{X_\sigma}. The string "_s" is appended to the aliased column names from \eqn{X_\sigma} if at least one of those
+#' names also appears in \eqn{X_\mu}
+#'
+#'If \code{mu = TRUE} and \code{sigma = FALSE}, the function returns the aliased columns of \eqn{X_\mu}.
+#'
+#'If \code{mu = FALSE} and \code{sigma = TRUE}, the function returns the aliased columns of \eqn{X_\sigma}.
+#'
+#' @export
+#'
+#' @importFrom stats alias
+#'
+#' @example R/examples/alias_examples.R
+#'
+alias.lmvar <- function( object, mu = TRUE, sigma = TRUE, ...){
+
+  aliased_mu = character()
+  if (mu){
+    aliased_mu = names(object$aliased_mu)[object$aliased_mu]
+  }
+
+  aliased_sigma = character()
+  if (sigma){
+    aliased_sigma = names(object$aliased_sigma)[object$aliased_sigma]
+  }
+
+  if (mu & !sigma){
+    return(aliased_mu)
+  }
+  else if (!mu & sigma){
+    return(aliased_sigma)
+  }
+  else if (mu & sigma){
+    a_names = c( aliased_mu, beta_sigma_names( names(object$aliased_mu), aliased_sigma))
+    return(as.character(a_names))
+  }
+  else {
+    return(character())
+  }
+}

R/beta_sigma_names.R

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+#'
+#' @title Unique names for beta_sigma
+#'
+#' @description Returns adapted names for the coefficients \eqn{\beta_\sigma} to distinguish them from the names
+#' of the coefficients
+#' \eqn{\beta_\mu}. This is a helper function which is used in situations where it is necessary or convenient
+#' for the coefficient names of \eqn{\beta_\sigma}
+#' to be different from \eqn{\beta_\mu}.
+#'
+#' @param beta_mu_names Character vector with the names of the coefficients \eqn{\beta_\mu}
+#' @param beta_sigma_names Character vector with the names of the coefficients \eqn{\beta_\sigma}
+#' @param ... Additional arguments, not used in the current implementation
+#'
+#' @return Named character vector with the names of the coefficients \eqn{\beta_\sigma}. The name of a vector element
+#' is the original
+#' name of the coefficient. The value is the adapted name. The name and the value are equal if no adaptation was needed.
+#'
+#' @details When the name of at least one coefficient in \eqn{\beta_\sigma} is equal to one of the names of the
+#' coefficients in \eqn{\beta_\mu}, the string '_s' is
+#' appended to the names of all coefficients in \eqn{\beta_\sigma}.
+#' Otherwise, the names of the coefficients in \eqn{\beta_\sigma} are left unchanged.
+#'
+#' @export
+#'
+#' @example  R/examples/beta_sigma_names_examples.R
+#'
+
+beta_sigma_names <- function( beta_mu_names, beta_sigma_names, ...){
+
+  if (is.null(beta_sigma_names)){
+    return(character())
+  }
+  else
+  {
+    # Check if at least one name of beta_sigma is identical to beta_mu
+    bool = any(is.element( beta_sigma_names, beta_mu_names))
+
+    # Adapt the names of beta_sigma if at least one is identical to beta_mu
+    if (bool){
+      beta_sigma_names_new = sapply( beta_sigma_names, function(x){
+        if (x != "(Intercept_s)"){
+          return(paste( x, "_s", sep=""))
+        }
+        else {
+          return(x)
+        }
+      })
+    }
+    else {
+      beta_sigma_names_new = beta_sigma_names
+    }
+
+    names(beta_sigma_names_new) = beta_sigma_names
+
+    return(beta_sigma_names_new)
+  }
+}

R/coef.lmvar.R

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+#'
+#' @title Extracts coefficients from an 'lmvar' object.
+#'
+#' @description Extracts maximum-likelihood estimators for \eqn{\beta_\mu} and \eqn{\beta_\sigma} from an 'lmvar' object.
+#'
+#' @param object Object of class 'lmvar'
+#' @param mu Boolean, specifies whether or not to return the maximum-likelihood estimator for \eqn{\beta_\mu}
+#' @param sigma Boolean, specifies whether or not to return the maximum-likelihood estimator for \eqn{\beta_\sigma}
+#' @param ... For compatibility with \code{\link[stats]{coef}} generic
+#'
+#' @return When \code{mu = TRUE} and \code{sigma = TRUE}, a named numeric vector with the elements of \eqn{\beta_\mu},
+#' followed by the elements of \eqn{\beta_\sigma}.
+#'
+#' When \code{mu = TRUE} and \code{sigma = FALSE}, a named numeric vector with the elements of \eqn{\beta_\mu}.
+#'
+#' When \code{mu = FALSE} and \code{sigma = TRUE}, a named numeric vector with the elements of \eqn{\beta_\sigma}.
+#'
+#' @details When both \code{mu = TRUE} and \code{sigma = TRUE}, the names of the
+#' coefficients in \eqn{\beta_\sigma} are adapted to distinguish them from the names in \eqn{\beta_\mu}, if needed.
+#'
+#' @seealso \code{\link{beta_sigma_names}} for the adaptation of the names of the coefficients in \eqn{\beta_\sigma}.
+#'
+#' @export
+#'
+#' @example R/examples/coef_examples.R
+#'
+
+coef.lmvar <- function( object, mu = TRUE, sigma = TRUE, ...){
+
+  beta_mu = numeric()
+  beta_sigma = numeric()
+  beta_mu_names = character()
+  beta_sigma_names = character()
+
+  if (mu){
+    beta_mu = object$coefficients_mu
+    beta_mu_names = names(object$coefficients_mu)
+  }
+
+  if (sigma){
+    beta_sigma = object$coefficients_sigma
+    if (mu){
+      beta_sigma_names = lmvar::beta_sigma_names( names(object$coefficients_mu), names(object$coefficients_sigma))
+    }
+    else {
+      beta_sigma_names = names(object$coefficients_sigma)
+    }
+  }
+
+  betas = c( beta_mu, beta_sigma)
+  names(betas) = c( beta_mu_names, beta_sigma_names)
+
+  return(betas)
+}

R/df.residual.lmvar.R

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+#' @title Residual degrees of freedom for an object of class 'lmvar'
+#'
+#' @description Residual degrees of freedom for an object of class 'lmvar'. The residual degrees of freedom are defined
+#' as the number of observations minus the degrees of freedom of the model.
+#'
+#' @param object Object of class 'lmvar'
+#' @param ... For compatibility with \code{\link[stats]{df.residual}} generic
+#'
+#' @return Residual degees of freedom for \code{object}.
+#'
+#' @export
+#'
+#' @seealso \code{\link{dfree}} for the degrees of freedom of an object of class 'lmvar'
+#'
+#' \code{\link{nobs.lmvar}} for the number of observations in an object of class 'lmvar'
+#'
+#' @example R/examples/df.residual_examples.R
+#'
+
+df.residual.lmvar <- function(object, ...){
+  df = nobs(object) - dfree(object)
+  return(df)
+}

R/dfree.R

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+#' @title Degrees of freedom for an object of class 'lmvar'
+#'
+#' @description Degrees of freedom for the model in an object of class 'lmvar'. The degrees of freedom are defined as the rank of the
+#' model matrix \eqn{X_\mu} for the expectation values, plus the rank of the model matrix \eqn{X_\sigma} for the standard deviations.
+#'
+#' @param object Object of class 'lmvar'
+#' @param mu Boolean, specifies whether the degrees of freedom for the model for the expectation values must be included.
+#' @param sigma Boolean, specifies whether the degrees of freedom for the model for the standard deviations must be included.
+#' @param ... Additional arguments, not used in the current implementation
+#'
+#' @return An integer containing the degrees of freedom for the model in \code{object}.
+#'
+#' @details If \code{mu = TRUE} and \code{sigma = TRUE}, the function returns the rank of the model-matrix \eqn{X_\mu} plus the
+#' rank of the model matrix \eqn{X_\sigma}.
+#'
+#' If \code{mu = TRUE} and \code{sigma = FALSE}, the function returns the rank of the model-matrix \eqn{X_\mu}.
+#'
+#' If \code{mu = FALSE} and \code{sigma = TRUE}, the function returns the rank of the model-matrix \eqn{X_\sigma}.
+#'
+#' Both model matrices contain a column corresponding to an intercept term. This column is added by \code{\link{lmvar}}.
+#' See also the vignette 'Intro'.
+#'
+#' @export
+#'
+#' @seealso \code{\link{df.residual.lmvar}} for the residual degrees of freedom for an object of class 'lmvar'
+#'
+#' @example R/examples/dfree_examples.R
+#'
+
+dfree <- function( object, mu = TRUE, sigma = TRUE, ...){
+
+  if (class(object) != 'lmvar'){
+    stop("Object must be an 'lmvar' object")
+  }
+
+  if (mu & sigma){
+    return( ncol(object$X_mu) + ncol(object$X_sigma))
+  }
+  else if (mu){
+    return( ncol(object$X_mu))
+  }
+  else {
+    return( ncol(object$X_sigma))
+  }
+
+}

R/examples/AIC_examples.R

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+\dontrun{
+
+# Let 'fit' be an object of class 'lmvar'. The classical AIC is
+AIC(fit)
+
+# To calculate the AIC with penalty-parameter k = 3 run
+AIC(fit, k = 3)
+}