plot.plm0  R Documentation 
Visualize discharge rating curve model objects
## S3 method for class 'plm0'
plot(
x,
...,
type = "rating_curve",
param = NULL,
transformed = FALSE,
title = NULL,
xlim = NULL,
ylim = NULL
)
## S3 method for class 'plm'
plot(
x,
...,
type = "rating_curve",
param = NULL,
transformed = FALSE,
title = NULL,
xlim = NULL,
ylim = NULL
)
## S3 method for class 'gplm0'
plot(
x,
...,
type = "rating_curve",
param = NULL,
transformed = FALSE,
title = NULL,
xlim = NULL,
ylim = NULL
)
## S3 method for class 'gplm'
plot(
x,
...,
type = "rating_curve",
param = NULL,
transformed = FALSE,
title = NULL,
xlim = NULL,
ylim = NULL
)
x 
object of class "plm0", "plm", "gplm0" or "gplm". 
... 
other plotting parameters (not used in this function) 
type 
a character denoting what type of plot should be drawn. Defaults to "rating_curve". Possible types are

param 
a character vector with the parameters to plot. Defaults to NULL and is only used if type is "trace" or "histogram". Allowed values are the parameters given in the model summary of x as well as "hyperparameters" or "latent_parameters" for specific groups of parameters. 
transformed 
a logical value indicating whether the quantity should be plotted on a transformed scale used during the Bayesian inference. Defaults to FALSE. 
title 
a character denoting the title of the plot 
xlim 
numeric vector of length 2, denoting the limits on the x axis of the plot. Applicable for types "rating_curve","rating_curve_mean","f","beta","sigma_eps","residuals". 
ylim 
numeric vector of length 2, denoting the limits on the y axis of the plot. Applicable for types "rating_curve","rating_curve_mean","f","beta","sigma_eps","residuals". 
No return value, called for side effects.
plot(plm0)
: Plot method for plm0
plot(plm)
: Plot method for plm
plot(gplm0)
: Plot method for gplm0
plot(gplm)
: Plot method for gplm
plm0
, plm
, gplm0
and gplm
for fitting a discharge rating curve and summary.plm0
, summary.plm
, summary.gplm0
and summary.gplm
for summaries. It is also useful to look at spread_draws
and gather_draws
to work directly with the MCMC samples.
data(krokfors)
set.seed(1)
plm0.fit < plm0(formula=Q~W,data=krokfors,num_cores=2)
plot(plm0.fit)
plot(plm0.fit,transformed=TRUE)
plot(plm0.fit,type='histogram',param='c')
plot(plm0.fit,type='histogram',param='c',transformed=TRUE)
plot(plm0.fit,type='histogram',param='hyperparameters')
plot(plm0.fit,type='histogram',param='latent_parameters')
plot(plm0.fit,type='residuals')
plot(plm0.fit,type='f')
plot(plm0.fit,type='sigma_eps')
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