1.0.1

### New features
- New `vcov` method

### Bug fixes
- Adapts `smooth_scam` function to new version of package `gratia`
- Fixes names in confidence intervals from `confint.cgaim`

.Rbuildignore

@@ -4,3 +4,4 @@ MASTER.R
 TODO.*
 cran-comments.md
 ^CRAN-SUBMISSION$
+revdep

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.0.0
-Date: 2022-07-07 17:31:28 UTC
-SHA: f25c091cb14623d70c2ce89f349d8e338ae4e71d
+Version: 1.0.1
+Date: 2024-03-29 15:33:13 UTC
+SHA: a6166b74bbd812f1e28652612228db1a837c6e8a

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: cgaim
 Title: Constrained Groupwise Additive Index Models
-Version: 1.0.0
+Version: 1.0.1
 Authors@R: 
     person(given = "Pierre",
            family = "Masselot",
@@ -13,7 +13,7 @@ Encoding: UTF-8
 LazyData: true
 Suggests: 
     testthat (>= 2.1.0)
-RoxygenNote: 7.2.0
+RoxygenNote: 7.3.1
 Imports: 
     graphics,
     stats,

NAMESPACE

@@ -5,6 +5,8 @@ S3method(confint,cgaim)
 S3method(plot,cgaim)
 S3method(predict,cgaim)
 S3method(print,cgaim)
+S3method(vcov,boot.cgaim)
+S3method(vcov,cgaim)
 export(boot.cgaim)
 export(build_constraints)
 export(cgaim)

NEWS.md

@@ -1,3 +1,12 @@
+## 1.0.1
+
+### New features
+- New `vcov` method
+
+### Bug fixes
+- Adapts `smooth_scam` function to new version of package `gratia`
+- Fixes names in confidence intervals from `confint.cgaim`
+
 ## 1.0.0
 
 ### Changes

R/confint.boot.cgaim.R

@@ -36,7 +36,7 @@ confint.boot.cgaim <- function(object, parm, level = 0.95, ...)
       alims, na.rm = TRUE))
 
     # Names
-    rownames(res$alpha) <- names(unlist(object$alpha))
+    rownames(res$alpha) <- names(unlist(object$obs$alpha))
     colnames(res$alpha) <- level.labels
   }
   if (2 %in% parm){

R/smooth_scam.R

@@ -17,7 +17,7 @@ smooth_scam <- function(x, y, formula, Xcov, ...)
     # print("scam"); flush.console()
   } else {
     gfit <- mgcv::gam(formula, data = scam_data, ...)
-    derivs <- as.data.frame(gratia::derivatives(gfit, newdata = scam_data))
+    derivs <- as.data.frame(gratia::derivatives(gfit, data = scam_data))
     # print("gam"); flush.console()
   }
   # Extract estimated terms
@@ -43,8 +43,8 @@ smooth_scam <- function(x, y, formula, Xcov, ...)
       if (iscons){
         dgx[,j] <- scam::derivative.scam(gfit, jind)$d
       } else {
-        dgx[,j] <- derivs[derivs$smooth == gfit$smooth[[jind]]$label, 
-          "derivative"]
+        dgx[,j] <- derivs[derivs$.smooth == gfit$smooth[[jind]]$label, 
+          ".derivative"]
       }
     } else {
       if (is.numeric(scam_data[j])){

R/vcov.boot.cgaim.R

@@ -0,0 +1,39 @@
+#' @rdname vcov.cgaim
+#' @order 2
+#' 
+#' @export
+vcov.boot.cgaim <- function(object, parm = c("alpha", "beta"), complete = TRUE, 
+  ...)
+{
+  #----- Header
+
+  # Check parm
+  parm <- match.arg(parm)
+
+  #----- Create variance covariane matrix
+
+  # Compute vcov
+  vres <- stats::var(t(object$boot[[parm]]))
+
+  # Set names
+  rownames(vres) <- colnames(vres) <- names(unlist(object$obs[[parm]]))
+
+  #----- Complete for potentially aliased coefficients
+  if (isTRUE(complete)){
+
+    # Check if any aliased coefficients
+    aliased <- is.na(unlist(object$obs[[parm]]))
+
+    # Complete
+    vresa <- matrix(NA, length(aliased), length(aliased),
+      dimnames = list(names(unlist(object$obs[[parm]])), 
+        names(unlist(object$obs[[parm]]))))
+    vresa[!aliased, !aliased] <- vres
+
+    #----- Return 
+    return(vresa)
+  } else {
+    return(vres)  
+  }
+
+}

R/vcov.cgaim.R

@@ -0,0 +1,124 @@
+#' Calculate Variance-Covariance Matrix for a Fitted CGAIM Object
+#'
+#' Returns the variance covariance matrix of the main parameters of a fitted \code{cgaim} object. These parameters correspond to the index weights \code{alpha} and the scaling coefficients \code{beta}.
+#'    
+#' @param object A \code{cgaim} or \code{boot.cgaim} object.
+#' @param parm The model components for which to get confidence intervals. 
+#'    Either \code{"alpha"} (the default) for index weights or \code{"beta"} for scaling coefficients.
+#' @param type The type of confidence intervals. Either \code{"normal"} (the default)
+#'    or \code{"bootstrap"}. See details.
+#' @param B The number of samples to be simulated.
+#' @param complete Indicates whether the full variance-covariance matrix should be returned when some of the parameters could not be estimated. If so, the matrix is padded with \code{NA}s.
+#' @param ... Additional parameters to be passed to \code{\link{boot.cgaim}} for bootstrap replications.
+#'
+#' @details 
+#' Two types of computation are currently implemented in the function.
+#' When \code{type = "normal"}, variance-covariance matrices are computed assuming 
+#' components are normally distributed. Beta coefficients are treated as
+#' regular linear regression coefficients and alpha
+#' coefficients are assumed to follow a Truncated Multivariate Normal distribution. 
+#' The latter is obtained by simulating from TMVN (see \code{\link[TruncatedNormal]{tmvnorm}}) 
+#' and computing the empirical variance covariance matrix from these simulations. The parameter \code{B} controls the number of simulations from the TMVN (and is not used when \code{parm = "beta"}).
+#' 
+#' When \code{type = "bootstrap"}, the variance-covariance matrix is computed on Bootstrap replications. In this case \code{\link{boot.cgaim}} is called internally and \code{B} corresponds to the number of replications. Alternatively, the user can directly call \code{\link{boot.cgaim}} and feed the result into \code{vcov.boot.cgaim} (see examples).
+#' 
+#' @returns A variance-covariance matrix object. 
+#' 
+#' @seealso \code{\link{boot.cgaim}} for bootstrapping and \code{\link{confint.cgaim}} for confidence intervals. 
+#'
+#' @references
+#'   Masselot, P. and others, 2022. Constrained groupwise additive index models.
+#'     Biostatistics.
+#'     
+#'   Pya, N., Wood, S.N., 2015. Shape constrained additive models. 
+#'    Stat. Comput. 25, 543–559. 
+#'    
+#'   Wood, S.N., 2017. Generalized Additive Models: An Introduction with R, 
+#'     2nd ed, Texts in Statistical Science. Chapman and Hall/CRC.
+#'
+#' @examples 
+#' # A simple CGAIM
+#' n <- 200
+#' x1 <- rnorm(n)
+#' x2 <- x1 + rnorm(n)
+#' z <- x1 + x2
+#' y <- z + rnorm(n)
+#' df1 <- data.frame(y, x1, x2) 
+#' ans <- cgaim(y ~ g(x1, x2, acons = list(monotone = 1)), data = df1)
+#' 
+#' # (Truncated) Normal variance-covariance matrix
+#' set.seed(1)
+#' vcov(ans, B = 1000)
+#' set.seed(1)
+#' vcov(ans, parm = "alpha", B = 1000) # Same result
+#' vcov(ans, parm = "beta", B = 1000)
+#' 
+#' # Confidence intervals by bootstrap (more computationally intensive, B should be increased)
+#' set.seed(2)
+#' vcov(ans, type = "boot", B = 10)
+#' 
+#' # Alternatively, bootstrap samples can be performed beforehand
+#' set.seed(2) 
+#' boot1 <- boot.cgaim(ans, B = 10)
+#' vcov(boot1)
+#'
+#' @order 1
+#' @export
+vcov.cgaim <- function(object, parm = c("alpha", "beta"), 
+  type = c("normal", "bootstrap"), B = 100, complete = TRUE, ...)
+{
+  #----- Header
+
+  # Check parm
+  parm <- match.arg(parm)
+
+  # Check type
+  type <- match.arg(type)
+
+  #----- Compute vcov if "normal"
+  if (type == "normal"){
+    # For alpha: simulate from truncated normal
+    if(parm == "alpha"){
+
+      # Simulate alpha from truncated multivariate normal
+      simures <- simul_tmvnorm(object, B = B)
+
+      # Compute vcov
+      vres <- stats::var(simures)
+    }
+
+    # For beta
+    if (parm == "beta"){
+
+      # use the usual vcov matrix
+      vres <- vcov_beta(object)
+    }
+
+    # Set names
+    rownames(vres) <- colnames(vres) <- names(unlist(object[[parm]]))
+
+    # Fill potentially for aliased coefficients
+    if (isTRUE(complete)){
+
+      # Check if any aliased coefficients
+      aliased <- is.na(unlist(object[[parm]]))
+
+      # Complete
+      vresa <- matrix(NA, length(aliased), length(aliased),
+        dimnames = list(names(unlist(object[[parm]])), 
+          names(unlist(object[[parm]]))))
+      vresa[!aliased, !aliased] <- vres
+      vres <- vresa
+    }
+  } else {
+
+    # Simulate
+    simures <- boot.cgaim(object, B = B, ...)
+
+    # Compute CI
+    vres <- vcov.boot.cgaim(simures, parm = parm, complete = complete)
+  }
+
+  #----- Return
+  return(vres)
+}

README.md

@@ -28,5 +28,5 @@ The package is available for installation from the usual CRAN repository. Altern
 
 ## Functions
 
-The main function of the package is the eponymous `cgaim` that fits the model. Then, the `print.cgaim` method allows displaying the results. The `confint.cgaim` method allows computing confidence intervals, either using normal approximation or through bootstrap. Ridge functions can be displayed using the `plot.cgaim` method, with the possibility to add confidence intervals. Finally, `predict.cgaim` allows computing the indices and perform prediction on new observations.
+The main function of the package is the eponymous `cgaim` that fits the model. Then, the `print.cgaim` method allows displaying the results. The `confint.cgaim` and `vcov.cgaim` methods allow computing confidence intervals and variance-covariance matrix using various approaches. Ridge functions can be displayed using the `plot.cgaim` method, with the possibility to add confidence intervals. Finally, `predict.cgaim` allows computing the indices and perform prediction on new observations.
 See the help of each function.

cran-comments.md

@@ -1,35 +1,9 @@
-# 1.0.0
+# 1.0.1
 
-## Resubmission
-This is a resubmission. In this version I have:
-
-* Added a \donttest statement for R CMD check to ignore the most computationally expensive example.
-* Added the reference to the methodological paper in the DESCRIPTION file.
-* Replaced the T and F that previously escaped me by TRUE and FALSE.
-* Added \value fields to the documentation of exported functions missing one.
+Package update after check errors from 27/04/2024. Fixes error induced by the update of the `gratia` dependency.
 
 ## R CMD check results
-There were no ERRORs or WARNINGs.
-
-There was one NOTE on winbuilder only:
-
-* checking CRAN incoming feasibility ... NOTE
-Maintainer: 'Pierre Masselot <pierre.masselot@lshtm.ac.uk>'
-
-New submission
-
-Possibly misspelled words in DESCRIPTION:
-  Campagna (10:182)
-  Chebana (10:173)
-  Gosselin (10:209)
-  Groupwise (2:20)
-  Lavigne (10:192)
-  Masselot (10:163)
-  Ouarda (10:201)
-  groupwise (10:31, 10:238)
-
-I believe these notes are just flagging the fact it is a new submission.
-Regarding misspelled words, this is how these words are spelled and used in associated publications and are therefore correct (groupwise) and the names of authors in the citation.
+0 errors | 0 warnings | 0 notes
 
 ## Downstream dependencies
-There are currently no downstream dependencies for this package.
+Only bug fix and addition of new convenience functions, no change in the specification nor output of the existing function.

inst/CITATION

@@ -1,23 +1,23 @@
 citHeader("To cite cgaim in publications use:")
 
-citEntry(entry = "Article",
+bibentry(bibtype = "Article",
   title        = "Constrained groupwise additive index models",
-  author       = personList(as.person("Pierre Masselot"),
+  author       = c(as.person("Pierre Masselot"),
                    as.person("Fateh Chebana"),
                    as.person("Céline Campagna"),
                    as.person("Éric Lavigne"),
                    as.person("Taha B.M.J. Ouarda"),
                    as.person("Pierre Gosselin")),
   journal      = "Biostatistics",
   year         = "2022",
-  volume       = "00",
-  number       = "00",
-  pages        = "1--19",
+  volume       = "24",
+  number       = "4",
+  pages        = "1066–1084",
   url          = "https://doi.org/10.1093/biostatistics/kxac023",
 
   textVersion  =
-  paste("Pierre Masselot, Fateh Chebana, Céline Campagna, Éric Lavigne, Taha B M J Ouarda, Pierre Gosselin (2022).",
+  paste("Pierre Masselot, Fateh Chebana, Céline Campagna, Éric Lavigne, Taha B.M.J. Ouarda, Pierre Gosselin (2022).",
         "Constrained groupwise additive index models.",
-        "Biostatistics, 00(00), 1-19.",
+        "Biostatistics, 24(4), 1066–1084.",
         "URL https://doi.org/10.1093/biostatistics/kxac023.")
 )