| Type: | Package |
| Title: | Misreported Time Series Analysis |
| Version: | 0.0.2 |
| Date: | 2021-07-14 |
| Encoding: | UTF-8 |
| Maintainer: | David Moriña Soler <dmorina@ub.edu> |
| Description: | Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <doi:10.48550/arXiv.2003.09202>. |
| Depends: | R (≥ 3.5.0), mixtools, boot, tseries |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2021-07-14 06:07:21 UTC; dmorina |
| Author: | David Moriña Soler
 [aut, cre],
Amanda Fernández-Fontelo [aut],
Alejandra Cabaña [aut],
Pedro Puig [aut] |
| Repository: | CRAN |
| Date/Publication: | 2021-07-14 07:00:02 UTC |
Misreported time series analysis
Description
Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <https://arxiv.org/abs/2003.09202v2>.
Details
| Package: | MisRepARMA |
| Type: | Package |
| Version: | 0.0.2 |
| Date: | 2021-07-14 |
| License: | GPL version 2 or newer |
| LazyLoad: | yes |
The package implements function fitMisRepARMA,
which is able to fit an ARMA time series model to misreported data, and the function
reconstruct which is able to reconstruct the most likely real series.
Author(s)
David Moriña, Amanda Fernández-Fontelo, Alejandra Cabaña, Pedro Puig
Mantainer: David Moriña Soler <dmorina@ub.edu>
References
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
Kunsch, H.R. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics, 17, 1217–1241.
Moriña, D., Fernández-Fontelo, A., Cabaña, A., Puig, P. (2021): New statistical model for misreported data with application to current public health challenges. arXiv preprint (https://arxiv.org/pdf/2003.09202.pdf)
Politis, D.N. and Romano, J.P. (1994) The stationary bootstrap. Journal of the American Statistical Association, 89, 1303–1313.
See Also
MisRepARMA-package, fitMisRepARMA, reconstruct
Internal MisRepARMA functions
Description
Internal MisRepARMA functions
Usage
estimate(data, tol, p_AR, q_MA, covars=NULL, misReport="U")
ran.genf(data, n, ran.args)
## S3 method for class 'fitMisRepARMA'
summary(object, ...)
## S3 method for class 'fitMisRepARMA'
print.summary(x, ...)
Details
These functions are not to be called by the user
See Also
MisRepARMA-package, fitMisRepARMA, reconstruct
Fit ARMA model to misreported time series data
Description
Fits an ARMA model to misreported time series data.
Usage
fitMisRepARMA(y, tol, B, p_AR, q_MA, covars=NULL, misReport="U", ...)
Arguments
y |
a numeric vector or time series giving the original data. |
tol |
tolerance limit to stop the iterative algorithm. |
B |
the number of bootstrap series to compute. |
p_AR |
order of the AR part. |
q_MA |
order of the MA part. |
covars |
matrix of explanatory variables. Its default value is |
misReport |
direction of misreporting issue. Its default value is |
... |
additional arguments to pass to |
Details
The model based resampling scheme with B bootstrap resamples is computed. This
Value
An object of class fitMisRepARMA with the following elements is returned:
-
data: The original time series. -
t0: The results of applying statistic to the original series. -
t: Estimates on each replicated time series. -
call: The original call to tsboot.
Author(s)
David Moriña, Amanda Fernández-Fontelo, Alejandra Cabaña, Pedro Puig
References
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
Kunsch, H.R. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics, 17, 1217–1241.
Moriña, D., Fernández-Fontelo, A., Cabaña, A., Puig, P. (2021): New statistical model for misreported data with application to current public health challenges. arXiv preprint (https://arxiv.org/pdf/2003.09202.pdf)
Politis, D.N. and Romano, J.P. (1994) The stationary bootstrap. Journal of the American Statistical Association, 89, 1303–1313.
See Also
MisRepARMA-package, reconstruct
Examples
### Simulate underreported time series data
set.seed(12345)
x <- arima.sim(model=list(ar=0.4), n=50)
ind <- rbinom(50, 1, 0.6)
y <- ifelse(ind==0, x, x*0.3)
mod <- fitMisRepARMA(y, 1e-6, 3, 0.05, 1, 0, covars=NULL, misReport="U")
Reconstruct the most likely series
Description
Reconstructs the most likely series.
Usage
reconstruct(object)
Arguments
object |
object of class |
Value
the function returns a vector of the same length of data containing the reconstruction of the most likely series.
Author(s)
David Moriña, Amanda Fernández-Fontelo, Alejandra Cabaña, Pedro Puig
References
D. Moriña, A. Fernández-Fontelo, A. Cabaña, P. Puig (2021): New statistical model for misreported data with application to current public health challenges. arXiv preprint (https://arxiv.org/pdf/2003.09202.pdf)
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge. ISBN 0-521-57391-2
See Also
MisRepARMA-package, fitMisRepARMA
Examples
### Simulate underreported time series data
x <- arima.sim(model=list(ar=0.4), n=50)
ind <- rbinom(50, 1, 0.6)
y <- ifelse(ind==0, x, x*0.3)
pr <- fitMisRepARMA(y, 1e-8, 5, 0.05, 1, 0, covars=NULL, misReport="U")
x <- reconstruct(pr)