SNSeg_estimate | R Documentation |

The function `SNSeg_estimate`

computes parameter estimates of each segment
that are separated by the SN-based change-point estimates.

```
SNSeg_estimate(SN_result)
```

`SN_result` |
An S3 object served as the output of the functions |

`SNSeg_estimate`

returns an S3 object of class "SNSeg_estimate" including
the parameter estimates of each segment separated by the SN-based change-point
estimates.

If the time series is univariate, for a single parameter change, the output contains parameter estimates for one of the followings:

`mean`

,`variance`

,`acf`

,`quantile`

, or`general`

, which can be referred to the change in a single mean, variance, autocorrelation, a given quantile level, or a general functional. For multi-parameter changes, the output can be a combination of`mean`

,`variance`

,`acf`

, and a dataframe with each quantile level depending on the type of parameters (argument`paras_to_test`

of`SNSeg_Uni`

,`SNSeg_Multi`

, or`SNSeg_HD`

) that users select.If the time series is multivariate with a dimension no greater than 10, the output contains parameter estimates for one of the followings:

`bivcor`

,`multi_mean`

, or`covariance`

, which can be referred to the change in correlation between bivariate time series and the change in multivariate means or covariance between multivariate time series.If the time series is high-dimensional with a dimension greater than 10, the output contains the parameter estimate

`HD_mean`

to represent the change in high-dimensional means.

For more examples of `SNSeg_estimate`

see the help vignette:
`vignette("SNSeg", package = "SNSeg")`

```
# code to simulate a univariate time series
set.seed(7)
ts <- MAR_Variance(2, "V1")
ts <- ts[,2]
# test the change in a single parameter (variance)
# grid_size defined
result <- SNSeg_Uni(ts, paras_to_test = "variance", confidence = 0.9,
grid_size_scale = 0.05, grid_size = 67,
plot_SN = TRUE, est_cp_loc = TRUE)
# estimated change-point locations
result$est_cp
# variance estimates of the separated segments
SNSeg_estimate(SN_result = result)
# For more examples of SNSeg_estimate, please run
# the command: vignette("SNSeg", package = "SNSeg")
```

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