graph antibody decay curves by antigen isotype

Usage

# S3 method for class 'curve_params'
autoplot(
  object,
  antigen_isos = unique(object$antigen_iso),
  ncol = min(3, length(antigen_isos)),
  ...
)

Arguments

object

a data.frame() of curve parameters (one or more MCMC samples)

antigen_isos

antigen isotypes to analyze (can subset curve_params)

ncol

how many columns of subfigures to use in panel plot

...

Arguments passed on to plot_curve_params_one_ab

verbose

verbose output

xlim

range of x values to graph

n_curves

how many curves to plot (see details).

n_points

Number of points to interpolate along the x axis (passed to ggplot2::geom_function())

rows_to_graph

which rows of curve_params to plot (overrides n_curves).

alpha

(passed to ggplot2::geom_function()) how transparent the curves should be:

0 = fully transparent (invisible)
1 = fully opaque

log_x

should the x-axis be on a logarithmic scale (TRUE) or linear scale (FALSE, default)?

log_y

should the Y-axis be on a logarithmic scale (default, TRUE) or linear scale (FALSE)?

Value

a ggplot2::ggplot() object

Details

`rows_to_graph`

Note that if you directly specify rows_to_graph when calling this function, the row numbers are enumerated separately for each antigen isotype; in other words, for the purposes of this argument, row numbers start over at 1 for each antigen isotype. There is currently no way to specify different row numbers for different antigen isotypes; if you want to do that, you could call plot_curve_params_one_ab() directly for each antigen isotype and combine the resulting panels yourself. Or you could subset curve_params manually, before passing it to this function, and set the n_curves argument to Inf.

Examples

# \donttest{
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
library(magrittr)

curve = load_curve_params("https://osf.io/download/rtw5k/") %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
  slice(1:100, .by = antigen_iso)  %>% # Reduce dataset for this example
  autoplot()

curve

# }