plot_n_grams: Plot n-grams
In pmcharrison/ppm: Prediction by Partial Matching

Description Usage Arguments Details Note

View source: R/plot-n-grams.R

This function takes a trained PPM model and plots transition probabilities computed by tabulating n-grams of length 1 and 2.

plot_n_grams(
  mod,
  pos = 1L,
  time = 0,
  max_alphabet_size = 30L,
  zero_indexed = FALSE,
  heights = c(0.25, 0.75),
  bigram_fill_scale = ggplot2::scale_fill_viridis_c("Probability (relative)")
)

`mod`	A PPM model object as produced by (for example) `new_ppm_simple` or `new_ppm_decay`, and subsequently trained on input sequences using `model_seq`.
`pos`	(Integerish scalar) The nominal 'position' at which the n-gram counts are retrieved (only relevant for decay-based models).
`time`	(Numeric scalar) The nominal 'time' at which the n-grams are retrieved (only relevant for decay-based models).
`max_alphabet_size`	If the model's alphabet size is larger than this value, then the function will throw an error, to protect the user from trying to plot prohibitively large transition matrices.
`zero_indexed`	(Logical scalar) If `zero_indexed = FALSE` (default), then the alphabet is mapped to ascending integers beginning at 1; otherwise, the alphabet is mapped to ascending integers beginning at 0 (i.e. all symbols are decremented by 1).
`heights`	A numeric vector of length 2 specifying the relative heights of the top and bottom plot panel respectively.
`bigram_fill_scale`	A `ggplot2` scale for the fill aesthetic of the bigram plot.

The output comprises two panels. The top panel plots the empirical probability distribution of 1-grams; this captures the relative frequencies of different symbols in the alphabet. The bottom panel plots conditional probability distributions computed from 2-grams. Each row corresponds to a maximum-likelihood probability distribution for the next symbol conditioned on the preceding symbol indexed by that row. Each column corresponds to a different continuation. These 2-gram conditional probabilities are not plotted directly, but are instead plotted relative to the corresponding 1-gram probabilities (i.e. the 2-gram probability minus the 1-gram probability). This helps the reader to separate 2-gram structure from 1-gram structure.

This function requires the following additional packages: dplyr, ggplot2, and egg, each of which can be installed using install.packages from CRAN.

pmcharrison/ppm documentation built on June 4, 2021, 9:45 a.m.