# ForwardSearch.plot: Plots forward residuals with simultaneous confidence bands In ForwardSearch: Forward Search using asymptotic theory

## Description

Plots forward residuals with simultaneous confidence bands based on Johansen and Nielsen (2013, 2014).

## Usage

 ```1 2 3 4``` ```ForwardSearch.plot(FS, ref.dist = "normal", bias.correct = FALSE, return = FALSE, plot.legend = TRUE, col = NULL, legend = NULL, lty = NULL, lwd = NULL, main = NULL, type = NULL, xlab = NULL, ylab = NULL) ```

## Arguments

 `FS` List. Value of the function `ForwardSearch.fit`. `ref.dist` Character. Reference distribution. "normal"standard normal distribution. `bias.correct` Logical. If FALSE do not bias correct variance, so plots have appearance similar to Atkinson and Riani (2000). If TRUE do bias correct variance, so plots start at origin. Default is FALSE. `return` Logical. Default is FALSE: do not return values. `plot.legend` Logical. Default is TRUE: include legend in plot. `col` `plot` parameter. Vector of 6 colours. `legend` `plot` parameter. Vector of 6 characters. `lty` `plot` parameter. Vector of 6 line types. `lwd` `plot` parameter. Vector of 6 line widths. `main` `plot` parameter. Character. `type` `plot` parameter. Charcater for plot type. `xlab` `plot` parameter. Charcater for x label. `ylab` `plot` parameter. Charcater for y label.

## Value

 `ref.dist` Character. From argument. `bias.correct` Logical. From argument. `forward.residual.scaled` Vector. Forward residuals scaled by estimated variance. The estimated variance is or is not bias corrected depending on the choice of `bias.correct`. `forward.asymp.median` Vector. Asymptotic median. `forward.asymp.sdv` Vector. Asymptotic standard deviation. Not divided by squareroot of sample size. `cut.off` Matrix. Cut-offs taken from Table 3 of Johansen and Nielsen (2014).

## Author(s)

Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 9 Sep 2014

## References

Johansen, S. and Nielsen, B. (2013) Asymptotic analysis of the Forward Search. Download: Nuffield DP.

Johansen, S. and Nielsen, B. (2014) Outlier detection algorithms for least squares time series. Download: Nuffield DP.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```##################### # EXAMPLE 1 # using Fulton Fish data, # see Johansen and Nielsen (2014). # Call package library(ForwardSearch) # Call data data(Fulton) mdata <- as.matrix(Fulton) n <- nrow(mdata) # Identify variable to reproduce Johansen and Nielsen (2014) q <- mdata[2:n ,9] q_1 <- mdata[1:(n-1) ,9] s <- mdata[2:n ,6] x.q.s <- cbind(q_1,s) colnames(x.q.s ) <- c("q_1","stormy") # Fit Forward Search FS95 <- ForwardSearch.fit(x.q.s,q,psi.0=0.95) ForwardSearch.plot(FS95) ```

ForwardSearch documentation built on May 1, 2019, 6:51 p.m.