diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/estpoly.R | 28 |
1 files changed, 13 insertions, 15 deletions
diff --git a/R/estpoly.R b/R/estpoly.R index d2569a3..4bb1ac9 100644 --- a/R/estpoly.R +++ b/R/estpoly.R @@ -1,9 +1,9 @@ #' @export estPoly <- function(coefficients,vcov,sigma,df,fitted.values, - residuals,call,time,input){ + residuals,call,input){ out <- list(coefficients= coefficients,vcpv= vcov,sigma = sigma, df= df,fitted.values=fitted.values, - residuals= residuals,call= call,time=time,input=input) + residuals= residuals,call= call,input=input) class(out) <- "estPoly" out } @@ -28,7 +28,7 @@ summary.estPoly <- function(object) } res <- list(call=object$call,coefficients=TAB,sigma=object$sigma, df=object$df) - class(res) <- "summary.estARX" + class(res) <- "summary.estPoly" res } @@ -58,13 +58,13 @@ plot.estPoly <- function(model,newdata=NULL){ if(is.null(newdata)){ ypred <- fitted(model) yact <- fitted(model) + resid(model) - time <- model$time + time <- time(model$input) titstr <- "Predictions of Model on Training Set" } else{ if(class(newdata)!="idframe") stop("Only idframe objects allowed") - ypred <- sim(coef(model),newdata$input[,1,drop=F]) - yact <- newdata$output[,1] - time <- seq(from=newdata$t.start,to=newdata$t.end,by=newdata$Ts) + ypred <- sim(coef(model),inputData(newdata)) + yact <- outputData(newdata) + time <- time(newdata) titstr <- "Predictions of Model on Test Set" } df <- data.frame(Predicted=ypred,Actual=yact,Time=time) @@ -92,7 +92,7 @@ residplot <- function(model,newdata=NULL){ #' #' Fit an ARX model of the specified order given the input-output data #' -#' @param data an object of class \code{idframe} +#' @param x an object of class \code{idframe} #' @param order: Specification of the orders: the three integer components #' (na,nb,nk) are the order of polynolnomial A, order of polynomial B and #' the input-output delay @@ -142,9 +142,8 @@ residplot <- function(model,newdata=NULL){ #' plot(model) # plot the predicted and actual responses #' #' @export -arx <- function(data,order=c(0,1,0)){ - y <- as.matrix(data$output) - u <- as.matrix(data$input); N <- dim(y)[1] +arx <- function(x,order=c(0,1,0)){ + y <- outputData(x); u <- inputData(x); N <- dim(y)[1] na <- order[1];nb <- order[2]; nk <- order[3] nb1 <- nb+nk ; n <- max(na,nb1); df <- N - na - nb - 1 @@ -164,12 +163,11 @@ arx <- function(data,order=c(0,1,0)){ vcov <- sigma2 * chol2inv(qx$qr) - model <- arx(A = c(1,coef[1:na]),B = coef[na+1:(nb+1)],ioDelay = nk) - time <- seq(from=data$t.start,to=data$t.end,by=data$Ts) + model <- arx(A = c(1,coef[1:na]),B = coef[na+1:(nb+1)], + ioDelay = nk,Ts=deltat(x)) est <- estPoly(coefficients = model,vcov = vcov, sigma = sqrt(sigma2), df = df,fitted.values=(X%*%coef)[1:N,], - residuals=(Y-X%*%coef)[1:N,],call=match.call(), - time=time,input=u) + residuals=(Y-X%*%coef)[1:N,],call=match.call(),input=u) est }
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