diff options
| -rw-r--r-- | R/estpoly.R | 8 | ||||
| -rw-r--r-- | man/armax.Rd | 2 | ||||
| -rw-r--r-- | man/arx.Rd | 2 | ||||
| -rw-r--r-- | man/bj.Rd | 2 | ||||
| -rw-r--r-- | man/oe.Rd | 2 |
5 files changed, 8 insertions, 8 deletions
diff --git a/R/estpoly.R b/R/estpoly.R index 9e99dae..0407514 100644 --- a/R/estpoly.R +++ b/R/estpoly.R @@ -163,7 +163,7 @@ residplot <- function(model,newdata=NULL){ #' #' @examples #' data(arxsim) -#' model <- arx(data,c(2,1,1)) +#' model <- arx(arxsim,c(2,1,1)) #' model #' plot(model) # plot the predicted and actual responses #' @@ -324,7 +324,7 @@ arx <- function(x,order=c(1,1,1),lambda=0.1,intNoise=FALSE, #' #' @examples #' data(armaxsim) -#' z <- dataSlice(data,end=1533) # training set +#' z <- dataSlice(armaxsim,end=1533) # training set #' mod_armax <- armax(z,c(1,2,1,2)) #' mod_armax #' @@ -432,7 +432,7 @@ armax <- function(x,order=c(0,1,1,0),init_sys=NULL,intNoise=FALSE, #' #' @examples #' data(oesim) -#' z <- dataSlice(data,end=1533) # training set +#' z <- dataSlice(oesim,end=1533) # training set #' mod_oe <- oe(z,c(2,1,2)) #' mod_oe #' plot(mod_oe) # plot the predicted and actual responses @@ -549,7 +549,7 @@ oe <- function(x,order=c(1,1,0),init_sys=NULL,options=optimOptions()){ #' #' @examples #' data(bjsim) -#' z <- dataSlice(data,end=1500) # training set +#' z <- dataSlice(bjsim,end=1500) # training set #' mod_bj <- bj(z,c(2,1,1,1,2)) #' mod_bj #' residplot(mod_bj) # residual plots diff --git a/man/armax.Rd b/man/armax.Rd index 8e86575..18310d7 100644 --- a/man/armax.Rd +++ b/man/armax.Rd @@ -59,7 +59,7 @@ using the \code{\link{detrend}} function. } \examples{ data(armaxsim) -z <- dataSlice(data,end=1533) # training set +z <- dataSlice(armaxsim,end=1533) # training set mod_armax <- armax(z,c(1,2,1,2)) mod_armax @@ -55,7 +55,7 @@ order to be zero. } \examples{ data(arxsim) -model <- arx(data,c(2,1,1)) +model <- arx(arxsim,c(2,1,1)) model plot(model) # plot the predicted and actual responses @@ -72,7 +72,7 @@ using the \code{\link{detrend}} function. } \examples{ data(bjsim) -z <- dataSlice(data,end=1500) # training set +z <- dataSlice(bjsim,end=1500) # training set mod_bj <- bj(z,c(2,1,1,1,2)) mod_bj residplot(mod_bj) # residual plots @@ -56,7 +56,7 @@ using the \code{\link{detrend}} function. } \examples{ data(oesim) -z <- dataSlice(data,end=1533) # training set +z <- dataSlice(oesim,end=1533) # training set mod_oe <- oe(z,c(2,1,2)) mod_oe plot(mod_oe) # plot the predicted and actual responses |