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Add covariate levels detection function plots

Source: R/add_df_covar_line.R
add_df_covar_line.Rd

Add a line or lines to a plot of the detection function which correspond to a a given covariate combination. These can be particularly useful when there is a small number of factor levels or if quantiles of a continuous covariate are specified.

Arguments

ddf

a fitted detection function object.

data

a data.frame with the covariate combination you want to plot.

...

extra arguments to give to lines (e.g., lty, lwd, col).

ndist

number of distances at which to evaluate the detection function.

pdf

should the line be drawn on the probability density scale; ignored for line transects

breaks

required to ensure that PDF lines are the right size, should match what is supplied to original plot command. Defaults to "Sturges" breaks, as in hist. Only used if pdf=TRUE

Value

invisibly, the values of detectability over the truncation range.

Details

All covariates must be specified in data. Plots can become quite busy when this approach is used. It may be useful to fix some covariates at their median level and plot set values of a covariate of interest. For example setting weather (e.g., Beaufort) to its median and plotting levels of observer, then creating a second plot for a fixed observer with levels of weather.

Arguments to lines are supplied in ... and aesthetics like line type (lty), line width (lwd) and colour (col) are recycled. By default lty is used to distinguish between the lines. It may be useful to add a legend to the plot (lines are plotted in the order of data).

Note

This function is located in the mrds package but the documentation is provided here for easy access.

Author

David L Miller

Examples


# example using a model for the minke data
data(minke)
# fit a model
result <- ds(minke, formula=~Region.Label)
#> Model contains covariate term(s): no adjustment terms will be included.
#> Fitting half-normal key function
#> AIC= 57.005

# make a base plot, showpoints=FALSE makes the plot less busy
plot(result, showpoints=FALSE)

# add lines for sex one at a time
add_df_covar_line(result, data.frame(Region.Label="South"), lty=2)
add_df_covar_line(result, data.frame(Region.Label="North"), lty=3)

# add a legend
legend(1.5, 1, c("Average", "South", "North"), lty=1:3)


# point transect example
data(amakihi)
result <- ds(amakihi, truncation=150, transect="point", formula=~OBs)
#> Model contains covariate term(s): no adjustment terms will be included.
#> Fitting half-normal key function
#> AIC= 13870.198
plot(result, showpoints=FALSE, pdf=TRUE)
add_df_covar_line(result,
                  data.frame(OBs=na.omit(unique(amakihi$OBs))), pdf=TRUE)