# plot.smooth: Plots for the fit and states In config-i1/smooth: Forecasting Using State Space Models

## Description

The function produces diagnostics plots for a `smooth` model

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## S3 method for class 'adam' plot(x, which = c(1, 2, 4, 6), level = 0.95, legend = FALSE, ask = prod(par("mfcol")) < length(which) && dev.interactive(), lowess = TRUE, ...) ## S3 method for class 'smooth' plot(x, which = c(1, 2, 4, 6), level = 0.95, legend = FALSE, ask = prod(par("mfcol")) < length(which) && dev.interactive(), lowess = TRUE, ...) ## S3 method for class 'msdecompose' plot(x, which = c(1, 2, 4, 6), level = 0.95, legend = FALSE, ask = prod(par("mfcol")) < length(which) && dev.interactive(), lowess = TRUE, ...) ```

## Arguments

 `x` Estimated smooth model. `which` Which of the plots to produce. The possible options (see details for explanations): Actuals vs Fitted values; Standardised residuals vs Fitted; Studentised residuals vs Fitted; Absolute residuals vs Fitted; Squared residuals vs Fitted; Q-Q plot with the specified distribution; Fitted over time; Standardised residuals vs Time; Studentised residuals vs Time; ACF of the residuals; PACF of the residuals; Plot of states of the model; Absolute standardised residuals vs Fitted; Squared standardised residuals vs Fitted. `level` Confidence level. Defines width of confidence interval. Used in plots (2), (3), (7), (8), (9), (10) and (11). `legend` If `TRUE`, then the legend is produced on plots (2), (3) and (7). `ask` Logical; if `TRUE`, the user is asked to press Enter before each plot. `lowess` Logical; if `TRUE`, LOWESS lines are drawn on scatterplots, see lowess. `...` The parameters passed to the plot functions. Recommended to use with separate plots.

## Details

The list of produced plots includes:

1. Actuals vs Fitted values. Allows analysing, whether there are any issues in the fit. Does the variability of actuals increase with the increase of fitted values? Is the relation well captured? They grey line on the plot corresponds to the perfect fit of the model.

2. Standardised residuals vs Fitted. Plots the points and the confidence bounds (red lines) for the specified confidence `level`. Useful for the analysis of outliers;

3. Studentised residuals vs Fitted. This is similar to the previous plot, but with the residuals divided by the scales with the leave-one-out approach. Should be more sensitive to outliers;

4. Absolute residuals vs Fitted. Useful for the analysis of heteroscedasticity;

5. Squared residuals vs Fitted - similar to (3), but with squared values;

6. Q-Q plot with the specified distribution. Can be used in order to see if the residuals follow the assumed distribution. The type of distribution depends on the one used in the estimation (see `distribution` parameter in alm);

7. ACF of the residuals. Are the residuals autocorrelated? See acf for details;

8. Fitted over time. Plots actuals (black line), fitted values (purple line), point forecast (blue line) and prediction interval (grey lines). Can be used in order to make sure that the model did not miss any important events over time;

9. Standardised residuals vs Time. Useful if you want to see, if there is autocorrelation or if there is heteroscedasticity in time. This also shows, when the outliers happen;

10. Studentised residuals vs Time. Similar to previous, but with studentised residuals;

11. PACF of the residuals. No, really, are they autocorrelated? See pacf function from stats package for details;

12. Plot of the states of the model. It is not recommended to produce this plot together with the others, because there might be several states, which would cause the creation of a different canvas. In case of "msdecompose", this will produce the decomposition of the series into states on a different canvas;

13. Absolute standardised residuals vs Fitted. Similar to the previous, but with absolute values. This is more relevant to the models where scale is calculated as an absolute value of something (e.g. Laplace);

14. Squared standardised residuals vs Fitted. This is an additional plot needed to diagnose heteroscedasticity in a model with varying scale. The variance on this plot will be constant if the adequate model for `scale` was constructed. This is more appropriate for normal and the related distributions.

Which of the plots to produce, is specified via the `which` parameter.

## Value

The function produces the number of plots, specified in the parameter `which`.

## Author(s)

Ivan Svetunkov, ivan@svetunkov.ru

plot.greybox

## Examples

 ```1 2 3 4``` ```ourModel <- es(c(rnorm(50,100,10),rnorm(50,120,10)), "ANN", h=10) par(mfcol=c(3,4)) plot(ourModel, c(1:11)) plot(ourModel, 12) ```

config-i1/smooth documentation built on June 16, 2021, 2:13 p.m.