summary.CausalMBSTS {CausalMBSTS} | R Documentation |

## Summary of causal effect estimation results obtained with `CausalMBSTS`

### Description

The method extracts and computes various summaries of the causal analysis with `CausalMBSTS`

.

### Usage

```
## S3 method for class 'CausalMBSTS'
summary(object, ...)
```

### Arguments

`object` |
An object of class 'CausalMBSTS', a result of a call to |

`...` |
further arguments passed to or from other methods (currently not used). |

### Value

Returns an object of class `summary.CausalMBSTS`

, which is a list of data frames corresponding to each
date provided in `horizon`

(or its default value) with the following columns:

`mean` |
Estimated average causal effect |

`lower` |
Lower bound of the two-sided (1- |

`upper` |
Upper bound of the two-sided (1- |

`cum.sum` |
Pointwise effect |

`cum.lower` |
Lower bound of a (1- |

`cum.upper` |
Upper bound of a (1- |

`bayes.pval` |
Bayesian p-value for the average causal effect |

`pct.causal.eff` |
Probability of a causal effect (%) |

### Examples

```
set.seed(1)
t <- seq(from = 0,to = 4*pi, length.out=300)
y <- cbind(3*sin(2*t)+rnorm(300), 2*cos(2*t) + rnorm(300))
dates <- seq.Date(from = as.Date("2015-01-01"), by = "week", length.out=300)
int.date <- as.Date("2020-02-27")
y[dates >= int.date,] <- y[dates >= int.date,]+2
# Causal effect estimation
causal.2 <- CausalMBSTS(y, components = c("trend", "cycle"), cycle.period = 75,
dates = dates, int.date = int.date, s0.r = 0.01*diag(2),
s0.eps = 0.1*diag(2), niter = 100, burn = 10)
sum.causal.2 <- summary(causal.2)
print(sum.causal.2, digits = 2)
sum.causal.2$horizon_default
```

*CausalMBSTS*version 0.1.1 Index]