ctsfeatures (version 1.2.2)

plot_cramer: Constructs a serial dependence plot based on Cramer's vi

Description

plot_cramer constructs a serial dependence plot of a categorical time series based on Cramer's vi

Usage

plot_cramer(
  series,
  max_lag = 10,
  alpha = 0.05,
  plot = TRUE,
  title = "Serial dependence plot",
  bar_width = 0.12,
  ...
)

Value

If plot = TRUE (default), returns the serial dependence plot based on Cramer's vi. Otherwise, the function returns a list with the values of Cramer's vi, the critical value and the corresponding p-values.

Arguments

series

An object of type tsibble (see R package tsibble), whose column named Value contains the values of the corresponding CTS. This column must be of class factor and its levels must be determined by the range of the CTS.

max_lag

The maximum lag represented in the plot (default is 10).

alpha

The significance level for the corresponding hypothesis test (default is 0.05).

plot

Logical. If plot = TRUE (default), returns the serial dependence plot. Otherwise, returns a list with the values of Cramer's vi, the critical value and the corresponding p-values.

title

The title of the graph.

bar_width

The width of the corresponding bars.

...

Additional parameters for the function.

Author

Ángel López-Oriona, José A. Vilar

Details

Constructs a serial dependence plot based on Cramer's vi, \(\widehat{v}(l)\), for several lags. A dashed lined is incorporated indicating the critical value of the test based on the following asymptotic approximation (under the i.i.d. assumption): $$T(r-1)\widehat{v}(l)^2 \sim\chi^2_{(r-1)^2},$$ where \(T\) is the series length and \(r\) is the number of categories in the time series.

References

weiss2013serialctsfeatures

Examples

sequence_1 <- SyntheticData1[which(SyntheticData1$Series==1),]
plot_cv <- plot_cramer(series = sequence_1, max_lag = 3) # Representing
# the serial dependence plot
list_cv <- plot_cramer(series = sequence_1, max_lag = 3, plot = FALSE) # Obtaining
# the values of Cramer's vi, the critical value and the p-values