plot_ts_lines: Plot timeseries values as lines.

Description

Plot timeseries values as lines.

Usage

plot_ts_lines(data, y, horiz = NULL, horiz2 = NULL, vert = NULL,
  vert2 = NULL, relative.error = FALSE, color = NULL, axes.free = TRUE,
  print = TRUE)

Arguments

data

A valid data frame containing scalar or timeseries values from a ss3sim simulation. That data are generated from get_results_all.

A character string denoting which column to use as the y variable. Must be a numeric column.

horiz, horiz2

A character string denoting which column to use as the first (horiz) and second (horiz2) level of faceting in the horizontal direction. E.g. "M" or "species". A value of NULL (default) indicates no faceting.

vert, vert2

A character string denoting which column to use as the first (vert) and second (vert2) level of faceting in the vertical direction. E.g. "M" or "species". A value of NULL (default) indicates no faceting.

relative.error

Boolean for whether the y-axis should be interpreted as relative error. If TRUE, ylim is set to c(-1,1), the y axis label is changed automatically, and a red line at y=0 is added.

color

A character string denoting which column to use to map color. Not valid for boxplot functions. Useful for looking at EM performance criteria against other dimensions of the EM or OM. See example below for how to merge in a metric from a scalar dataset to

axes.free

Boolean for whether the y-axis scales should be free in facet_grid.

A logical for whether the plot is printed or not.

Output

These functions print the ggplot object, but also return it invisibly for saving or printing again later.

Details

The ss3sim plotting functions are simply wrappers for ggplot2 code, specific to the output from ss3sim simulation scalar and timeseries (ts) objects. They are designed to quickly explore simulation output, rather than publication-level figures. The functions use the aes_string function within ggplot2 such that arguments are passed as characters that refer to columns of data.

Note that there are some subtle differences between the functions. Scalar plots require a value for x, while for ts plots x is invalid because it is fixed internally as 'year', since it makes no sense to use another column. Boxplots cannot have a color mapped to them like points or lines, and thus color is not a valid argument. The ts point and line plots are grouped internally by 'ID', which is a combination of scenario and replicate.

Examples

Run this code

ts_dat$SpawnBio <- with(ts_dat, (SpawnBio_om-SpawnBio_em)/SpawnBio_om)
# Merge in max_grad, a performance metric, to use for color
ts_dat <- merge(scalar_dat[, c("ID", "max_grad")], ts_dat)
plot_ts_lines(ts_dat, y = "SpawnBio", horiz = "D", vert = "E",
  relative.error = TRUE, color = "max_grad")