plot.bartcs {bartcs} | R Documentation |

## Draw plot for `bartcs`

object

### Description

Two options are available: posterior inclusion probability (PIP) plot and trace plot.

### Usage

```
## S3 method for class 'bartcs'
plot(x, method = NULL, parameter = NULL, ...)
```

### Arguments

`x` |
A |

`method` |
" |

`parameter` |
Parameter for traceplot. |

`...` |
Additional arguments for PIP plot.
Check |

### Details

#### PIP plot

When a posterior sample is sampled during training,
`separate_bart()`

or `single_bart()`

also counts
which variables are included in the model and
compute PIP for each variable.
For `bartcs`

object `x`

,
this is stored in `x$var_count`

and `x$var_prob`

respectively.
`plot(method = "pip")`

uses this information and
draws plot using `ggcharts::bar_chart()`

.

#### Traceplot

Parameters are recorded for each MCMC iterations.
Parameters include "`ATE`

", "`Y1`

", "`Y0`

", "`dir_alpha`

",
and either "`sigma2_out`

" from `single_bart()`

or "`sigma2_out1`

" and "`sigma2_out0`

" from

`separate_bart()`

.
Vertical line indicates burn-in.

### Value

A `ggplot`

object of either PIP plot or trace plot.

### Examples

```
data(ihdp, package = "bartcs")
x <- single_bart(
Y = ihdp$y_factual,
trt = ihdp$treatment,
X = ihdp[, 6:30],
num_tree = 10,
num_chain = 2,
num_post_sample = 20,
num_burn_in = 10,
verbose = FALSE
)
# PIP plot
plot(x, method = "pip")
plot(x, method = "pip", top_n = 10)
plot(x, method = "pip", threshold = 0.5)
# Check `?ggcharts::bar_chart` for other possible arguments.
# trace plot
plot(x, method = "trace")
plot(x, method = "trace", "Y1")
plot(x, method = "trace", "dir_alpha")
```

*bartcs*version 1.2.2 Index]