plot.cosimmr_pred_out {cosimmr} | R Documentation |
Plot different features of an object created from cosimmr_ffvb
.
Description
This function allows for 4 different types of plots of the simmr output
created from cosimmr_ffvb
. The
types are: plot of beta values
Usage
## S3 method for class 'cosimmr_pred_out'
plot(
x,
type = c("beta_histogram", "beta_boxplot", "prop_obs", "prop_density"),
obs = 1,
cov_name = NULL,
binwidth = 0.05,
alpha = 0.5,
title = NULL,
n_output = 3600,
...
)
Arguments
x |
An object of class |
type |
The type of plot required. Can be one or more of 'isospace', 'beta_histogram', 'beta_boxplot', 'prob_histogram', 'prob_density', 'covariates_plot' |
obs |
The observation you wish to plot |
cov_name |
The name of the covariate you wish to plot (for beta and covariate plots) |
binwidth |
The width of the bins for the histogram. Defaults to 0.05 |
alpha |
The degree of transparency of the plots. Not relevant for matrix plots |
title |
The title of the plot. |
n_output |
The number of theta samples you wish to plot with. Defaults to 3600 |
... |
Currently not used |
Details
The matrix plot should form a necessary part of any SIMM analysis since it allows the user to judge which sources are identifiable by the model. Further detail about these plots is provided in the vignette.
Value
one or more of 'isospace', 'beta_histogram', 'beta_boxplot', 'prop_histogram', 'prop_density', or 'covariates_plot'
Author(s)
Emma Govan <emmagovan@gmail.com>>, Andrew Parnell
See Also
See cosimmr_ffvb
for
creating objects suitable for this function, and many more examples. See
also cosimmr_load
for creating simmr objects,
plot.cosimmr_input
for creating isospace plots.
Examples
# A simple example with 10 observations, 2 tracers and 4 sources
# The data
data(geese_data_day1)
# Load into simmr
cosimmr_1 <- with(
geese_data_day1,
cosimmr_load(
formula = mixtures ~ 1,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means
)
)
# Plot
plot(cosimmr_1)
# FFVB run
cosimmr_1_out <- cosimmr_ffvb(cosimmr_1)
plot(cosimmr_1_out, type = c("isospace", "beta_hist"))