S3 methods consistency part 1

4 files changed, 31 insertions, 28 deletions

diff --git a/R/estpoly.R b/R/estpoly.R
index 5d5de76..e37ad1d 100644
--- a/R/estpoly.R
+++ b/R/estpoly.R
@@ -35,15 +35,15 @@ print.estpoly <- function(x,...){
 }
 
 #' @export
-summary.estpoly <- function(x)
+summary.estpoly <- function(object,...)
 {
-  model <- x$sys
+  model <- object$sys
   coefs <- params(model)
   
-  se <- sqrt(diag(getcov(x)))
+  se <- sqrt(diag(getcov(object)))
   params <- data.frame(Estimated=coefs,se=se)
   
-  report <- list(fit=fitch(x),params=params)
+  report <- list(fit=fitch(object),params=params)
   res <- list(model=model,report=report)
   class(res) <- "summary.estpoly"
   res
@@ -87,24 +87,24 @@ fitch <- function(x){
 }
 
 #' @export
-print.summary.estpoly <- function(x,digits=4){
+print.summary.estpoly <- function(x,digits=4,...){
   print(x$model,se=x$report$params[,2],dig=digits)
   cat("\n Fit Characteristics \n")
   print(data.frame(x$report$fit),digits=digits)
 }
 
+#' @import ggplot2
 #' @export
-plot.estpoly <- function(model,newdata=NULL){
-  loadNamespace("ggplot2")
+plot.estpoly <- function(x,newdata=NULL,...){
   
   if(is.null(newdata)){
-    ypred <- ts(fitted(model),names="Predicted")
-    yact <- ts(fitted(model) + resid(model),names="Actual")
-    time <- time(model$input)
+    ypred <- ts(fitted(x),names="Predicted")
+    yact <- ts(fitted(x) + resid(x),names="Actual")
+    time <- time(x$input)
     titstr <- "Predictions of Model on Training Set"
   } else{  
     if(class(newdata)!="idframe") stop("Only idframe objects allowed")
-    ypred <- predict(model,newdata)
+    ypred <- predict(x,newdata)
     yact <- outputData(newdata)[,1]
     time <- time(newdata)
     titstr <- "Predictions of Model on Test Set"
diff --git a/R/poly.R b/R/poly.R
index ea38487..528a599 100644
--- a/R/poly.R
+++ b/R/poly.R
@@ -71,7 +71,7 @@ checkUnity <- function(x){
 }
 
 #' @export
-print.idpoly <- function(x,se=NULL,dig=3){
+print.idpoly <- function(x,se=NULL,dig=3,...){
   main <- paste("Discrete-time",toupper(x$type),"model:")
   if(x$type=="oe" || x$type=="bj"){
     main <- paste(main,"y[k] = B(z)/F(z) u[k] +")
diff --git a/R/predict.R b/R/predict.R
index 5c8213a..b56fa8f 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -1,17 +1,17 @@
-predict.idpoly <- function(x,data,nahead=1){
+predict.idpoly <- function(object,data,nahead=1){
   y <- outputData(data); u<- inputData(data)
-  G <- signal::Arma(b=c(rep(0,x$ioDelay),x$B),
-                    a= as.numeric(polynom::polynomial(x$A)*
-                                    polynom::polynomial(x$F1)))
+  G <- signal::Arma(b=c(rep(0,object$ioDelay),object$B),
+                    a= as.numeric(polynom::polynomial(object$A)*
+                                    polynom::polynomial(object$F1)))
   det_sys <- as.numeric(signal::filter(G,u))
-  if(x$type=="oe" || nahead==Inf){
+  if(object$type=="oe" || nahead==Inf){
     ypred <- det_sys
   } else{
-    Hden <- as.numeric(polynom::polynomial(x$A)*polynom::polynomial(x$D))
-    Hinv <- signal::Arma(b=Hden,a=x$C)
+    Hden <- as.numeric(polynom::polynomial(object$A)*polynom::polynomial(object$D))
+    Hinv <- signal::Arma(b=Hden,a=object$C)
     filtered <- as.numeric(signal::filter(Hinv,as.numeric(y)-det_sys))
     if(nahead!=1){
-      H <- as.numeric(polynom::polynomial(x$C)*polyinv(Hden,nahead))
+      H <- as.numeric(polynom::polynomial(object$C)*polyinv(Hden,nahead))
       Hl <- signal::Ma(H[1:nahead])
       filtered <- as.numeric(signal::filter(Hl,filtered))
     }
@@ -41,11 +41,12 @@ polyinv <- function(x,k){
 #' 
 #' Predicts the output of an identified model (\code{estpoly}) object K steps ahead. 
 #' 
-#' @param x \code{estpoly} object containing the identified model
+#' @param object \code{estpoly} object containing the identified model
 #' @param newdata optional dataset to be used for predictions. If not supplied, 
 #' predictions are made on the training set.
 #' @param nahead number of steps ahead at which to predict (Default:1). For infinite-
 #' step ahead predictions or pure simulation, supply \code{Inf}.
+#' @param \ldots other arguments
 #' 
 #' @return 
 #' Time-series containing the predictions
@@ -62,14 +63,14 @@ polyinv <- function(x,k){
 #' and Practice}, CRC Press, Boca Raton. Chapter 18
 #' 
 #' @export
-predict.estpoly <- function(x,newdata=NULL,nahead=1){
+predict.estpoly <- function(object,newdata=NULL,nahead=1,...){
   if(is.null(newdata)&& nahead==1){
-    return(matrix(fitted(x)))
+    return(matrix(fitted(object)))
   } else{
-    model <- x$sys
+    model <- object$sys
     if(is.null(newdata)){
-      y <- fitted(x)+resid(x)
-      u <- x$input
+      y <- fitted(object)+resid(object)
+      u <- object$input
       z <- idframe(y,u,Ts = deltat(y),start=start(y))
     } else{
       z <- newdata
diff --git a/man/predict.estpoly.Rd b/man/predict.estpoly.Rd
index 4c19ed5..53371e6 100644
--- a/man/predict.estpoly.Rd
+++ b/man/predict.estpoly.Rd
@@ -4,16 +4,18 @@
 \alias{predict.estpoly}
 \title{Predictions of identified model}
 \usage{
-\method{predict}{estpoly}(x, newdata = NULL, nahead = 1)
+\method{predict}{estpoly}(object, newdata = NULL, nahead = 1, ...)
 }
 \arguments{
-\item{x}{\code{estpoly} object containing the identified model}
+\item{object}{\code{estpoly} object containing the identified model}
 
 \item{newdata}{optional dataset to be used for predictions. If not supplied, 
 predictions are made on the training set.}
 
 \item{nahead}{number of steps ahead at which to predict (Default:1). For infinite-
 step ahead predictions or pure simulation, supply \code{Inf}.}
+
+\item{\ldots}{other arguments}
 }
 \value{
 Time-series containing the predictions