bv_ggplot {BVARverse}  R Documentation 
Function to quickly plot outputs from bvar
and derived objects.
Supported plots include traces and densities, forecasts, and impulse
response functions. For more flexible plots one may use the outputs of
tidy.bvar
and augment.bvar
.
bv_ggplot(x, ...) ## Default S3 method: bv_ggplot(x, ...) ## S3 method for class 'bvar_chains' bv_ggplot(x, ...) ## S3 method for class 'bvar' bv_ggplot( x, type = c("trace", "density"), vars = NULL, vars_response = NULL, vars_impulse = NULL, orientation = c("horizontal", "vertical"), chains = list(), ... ) ## S3 method for class 'bvar_irf' bv_ggplot(x, vars_response = NULL, vars_impulse = NULL, col = "#737373", ...) ## S3 method for class 'bvar_fcast' bv_ggplot(x, vars = NULL, col = "#737373", t_back = 1L, ...)
x 
A 
... 
Not used. 
type 
A string with the type (trace or density) of plot desired. 
vars 
Character vector used to select variables. Elements are matched
to hyperparameters or coefficients. Coefficients may be matched based on
the dependent variable (by providing the name or position) or the
explanatory variables (by providing the name and the desired lag). See the
example section for a demonstration. Defaults to 
vars_response 
Optional character or integer vectors used
to select coefficents. Dependent variables are specified with
vars_response, explanatory ones with vars_impulse. Defaults to

vars_impulse 
Optional character or integer vectors used
to select coefficents. Dependent variables are specified with
vars_response, explanatory ones with vars_impulse. Defaults to

orientation 
A string indicating the desired orientation of trace or density plots 
chains 
List of 
col 
Character vector. Colour(s) of the lines delineating credible
intervals. Single values will be recycled if necessary. Recycled HEX color
codes are varied in transparency if not provided (e.g. "#737373FF"). Lines
can be bypassed by setting this to 
t_back 
Integer scalar. Whether to include actual datapoints in the tidied forecast. 
Returns a ggplot
object with a basic structure.
# Access a subset of the fred_qd dataset data < fred_qd[, c("CPIAUCSL", "UNRATE", "FEDFUNDS")] # Transform it to be stationary data < fred_transform(data, codes = c(5, 5, 1), lag = 4) # Estimate a BVAR using one lag, default settings and very few draws x < bvar(data, lags = 1, n_draw = 1000L, n_burn = 200L, verbose = FALSE) # Plot the outputs  alternatively use ggplot() with fortify() bv_ggplot(x) bv_ggplot(irf(x)) bv_ggplot(predict(x))