Use is.null vs is.missing where relevant (#14)

NEWS.md

@@ -5,6 +5,7 @@ Bug fixes
 ---------
 
  * `confint.profile.gnm()` now works for a single parameter  ![(#10)](https://github.com/hturner/gnm/issues/10)
+  * `gnm()` now works when `eliminate` is specified as `NULL` ![(#14)](https://github.com/hturner/gnm/issues/14)
 
 Changes in gnm 1.1-0
 ====================

R/gnm.R

@@ -42,8 +42,8 @@ gnm <- function(formula, eliminate = NULL, ofInterest = NULL,
                            drop.unused.levels = TRUE))
     modelData <- eval(modelData, parent.frame())
 
-    if (!missing(eliminate)) {
-        eliminate <- modelData$`(eliminate)`
+    eliminate <- modelData$`(eliminate)`
+    if (!is.null(eliminate)) {
         if (!is.factor(eliminate))
             stop("'eliminate' must be a factor")
         xtf <- xtfrm(modelData$`(eliminate)`)
@@ -106,7 +106,7 @@ gnm <- function(formula, eliminate = NULL, ofInterest = NULL,
         }
     }
 
-    if (is.empty.model(modelTerms) && missing(eliminate)) {
+    if (is.empty.model(modelTerms) && is.null(eliminate)) {
         if (method == "coefNames") return(numeric(0))
         else if (method == "model.matrix")
             return(model.matrix(modelTerms, data = modelData))
@@ -176,7 +176,7 @@ gnm <- function(formula, eliminate = NULL, ofInterest = NULL,
         if (is.null(start))
             start <- rep.int(NA, nElim + nParam)
         else if (length(start) != nElim + nParam) {
-            if (!missing(eliminate) && length(start) == nParam)
+            if (!is.null(eliminate) && length(start) == nParam)
                 start <- c(rep.int(NA, nElim), start)
             else
                 stop("length(start) must either equal the no. of parameters\n",
@@ -270,7 +270,7 @@ gnm <- function(formula, eliminate = NULL, ofInterest = NULL,
         return()
     }
 
-    if (is.null(ofInterest) && !missing(eliminate))
+    if (is.null(ofInterest) && !is.null(eliminate))
         ofInterest <- seq_len(nParam)
     if (identical(ofInterest, "[?]"))
         call$ofInterest <- ofInterest <-
@@ -291,7 +291,7 @@ gnm <- function(formula, eliminate = NULL, ofInterest = NULL,
         names(ofInterest) <- coefNames[ofInterest]
     }
 
-    if (missing(data))
+    if (is.null(data))
         data <- environment(formula)
     fit <- c(list(call = call, formula = formula,
                   terms = modelTerms, data = data, eliminate = eliminate,
@@ -301,7 +301,7 @@ gnm <- function(formula, eliminate = NULL, ofInterest = NULL,
                   offset = offset, tolerance = tolerance, iterStart = iterStart,
                   iterMax = iterMax), fit)
 
-    if (!missing(eliminate) && ordTRUE) {
+    if (!is.null(eliminate) && ordTRUE) {
         reorder <- order(ord)
         fit <- within(fit, {
             y <- y[reorder]

R/gnmTerms.R

@@ -81,7 +81,7 @@ gnmTerms <- function(formula, eliminate = NULL, data = NULL)
     inst[patch] <- 1
 
     nonsense <- tapply(inst, term, FUN = function(x)
-                   {!is.na(x) && !identical(as.integer(x), seq(x))})
+                   {!all(is.na(x)) && !identical(as.integer(x), seq(x))})
     if (any(nonsense))
         stop("Specified instances of ",
              paste(names(nonsense)[nonsense], ")"),

R/pickCoef.R

@@ -17,7 +17,7 @@ pickCoef <- function(object, pattern = NULL, value = FALSE, ...){
     coefs <- names(coef(object))
     if (is.null(coefs))
         stop("Coefficient names cannot be extracted from 'object'")
-    if (missing(pattern)) {
+    if (is.null(pattern)) {
         default <- list(setlabels = "Selected coefficients",
                         title = "Select coefficients of interest",
                         items.label = "Model coefficients:",

R/predict.gnm.R

@@ -28,7 +28,7 @@ predict.gnm <- function (object, newdata = NULL,
             terms <- setdiff(terms, "(eliminate)")
         }
     }
-    if (missing(newdata)) {
+    if (is.null(newdata)) {
         pred <- switch(type, link = object$predictors,
                        response = object$fitted.values,
                        terms = {pred <- termPredictors(object)
@@ -97,7 +97,7 @@ predict.gnm <- function (object, newdata = NULL,
     if (se.fit) {
         V <- vcov(object, dispersion = dispersion, with.eliminate = TRUE)
         residual.scale <- as.vector(sqrt(attr(V, "dispersion")))
-        if (missing(newdata)) {
+        if (is.null(newdata)) {
             X <- model.matrix(object)
             elim <- object$eliminate
         } else {
@@ -123,14 +123,14 @@ predict.gnm <- function (object, newdata = NULL,
                                        2*rowSums(X * covElim) + varElim)
                    se.fit <- d * se.fit},
                terms = {
-                   if (missing(newdata)) {
+                   if (is.null(newdata)) {
                        assign <- split(seq(ncol(X)), attr(X, "assign"))
                    } else {
                        M <- model.matrix(object)
                        assign <- split(seq(ncol(X)), attr(M, "assign"))
                    }
                    if (hasintercept) {
-                       if (missing(newdata)) {
+                       if (is.null(newdata)) {
                            X <- sweep(X, 2, colMeans(X))
                        } else X <- sweep(X, 2, colMeans(M))
                    }
@@ -151,7 +151,7 @@ predict.gnm <- function (object, newdata = NULL,
                    }
                })
         ## check estimability of predictions
-        if (!missing(newdata) && type != "terms"){
+        if (!is.null(newdata) && type != "terms"){
             estimable <- checkEstimable(object, t(X))
             is.na(se.fit)[estimable %in% c(FALSE, NA)] <- TRUE
         }

man/confint.gnm.Rd

@@ -16,7 +16,7 @@
   \item{object}{ an object of class \code{"gnm"} or \code{"profile.gnm"}}
   \item{parm}{ (optional) either a numeric vector of indices or a
     character vector of names, specifying the parameters for which
-    confidence intervals are to be estimated. If \code{parm} is missing,
+    confidence intervals are to be estimated. If \code{parm} is \code{NULL},
     confidence intervals are found for all parameters.} 
   \item{level}{ the confidence level required. }
   \item{trace}{ a logical value indicating whether profiling should be
@@ -29,7 +29,7 @@
 
   For \code{"gnm"} objects, \code{profile.gnm} is first called to
   profile the deviance over each parameter specified by \code{parm}, or
-  over all parameters in the model if \code{parm} is missing.
+  over all parameters in the model if \code{parm} is \code{NULL}.
 
   The method for \code{"profile.gnm"} objects is then called, which
   interpolates the deviance profiles to estimate the limits of the

man/gnm.Rd

@@ -22,7 +22,7 @@ gnm(formula, eliminate = NULL, ofInterest = NULL, constrain = numeric(0),
     improve computational efficiency. See details. }
   \item{ofInterest}{ optional coefficients of interest, specified by a
     regular expression, a numeric vector of indices, a character vector of
-    names, or "[?]" to select from a Tk dialog. If missing, it is
+    names, or "[?]" to select from a Tk dialog. If \code{NULL}, it is
     assumed that all non-\code{eliminate}d coefficients are of interest. }
   \item{constrain}{ (non-eliminated) coefficients to constrain, specified
     by a regular expression, a numeric vector of indices, a logical vector, a

man/pickCoef.Rd

@@ -12,7 +12,7 @@ pickCoef(object, pattern = NULL, value = FALSE, ...)
   \item{object}{ a model object. }
   \item{pattern}{ character string containing a regular expression or
     (with \code{fixed = TRUE}) a pattern to be matched exactly. If
-    missing, a Tk dialog will open for coefficient selection.  }
+    \code{NULL}, a Tk dialog will open for coefficient selection.  }
   \item{value}{ if \code{FALSE}, a named vector of indices, otherwise
     the value of the selected coefficients. }
   \item{\dots}{ arguments to pass on to \link[relimp]{pickFrom} if

man/profile.gnm.Rd

@@ -14,7 +14,7 @@
   \item{fitted}{ an object of class \code{"gnm"}. }
   \item{which}{ (optional) either a numeric vector of indices or a
     character vector of names, specifying the parameters over which the
-    deviance is to be profiled. If missing, the deviance is profiled
+    deviance is to be profiled. If \code{NULL}, the deviance is profiled
     over all parameters. }  
   \item{alpha}{ the significance level of the z statistic, indicating
     the range that the profile must cover (see details). }
@@ -23,7 +23,7 @@
   \item{stepsize}{ (optional) a numeric vector of length two, specifying
     the size of steps to take when profiling down and up respectively,
     or a single number specifying the step size in both directions. If
-    missing, the step sizes will be determined automatically. }
+    \code{NULL}, the step sizes will be determined automatically. }
   \item{trace}{ logical, indicating whether profiling should be
     traced. } 
   \item{\dots}{ further arguments. }

man/se.gnm.Rd

@@ -19,7 +19,7 @@
     numeric vector of indices or \code{"[?]"} to select from a Tk
     dialog. In the second case coefficients given as a vector or the
     rows of a matrix, such that \code{NROW(estimate)} is equal to 
-    \code{length(coef(object))}. If missing, standard errors are returned
+    \code{length(coef(object))}. If \code{NULL}, standard errors are returned
     for all (non-eliminated) parameters in the model.}
   \item{checkEstimability}{ logical: should the estimability of
     all specified combinations be checked?}