summary.mtsdi: Summary Information

In mtsdi: Multivariate Time Series Data Imputation

Description

Print summary information on the imputation object

Usage

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

Arguments

object	an object of class mtsdi
...	further options passed to print.summary.mtsdi

Value

The function resturns a list containing

call	function call
muhat	estimated mean vector
sigmahat	estimated covariance matrix
iterations	number of iterations used
convergence	relative difference of covariance determinant reached
time	time used in the process
models	details on the models used for time filtering
log	a logical indicating that data are log transformed
log.offset	offset used in the log transformation in order to avoid zeros

Author(s)

Washington Junger [e-mail] and Antonio Ponce de Leon [e-mail]

References

Junger, W.L. and Ponce de Leon, A. (2015) Imputation of Missing Data in Time Series for Air Pollutants. Atmospheric Environment, 102, 96-104.

Johnson, R., Wichern, D. (1998) Applied Multivariate Statistical Analysis. Prentice Hall.

Dempster, A., Laird, N., Rubin, D. (1977) Maximum Likelihood from Incomplete Data via the Algorithm EM. Journal of the Royal Statistical Society 39(B)), 1–38.

McLachlan, G. J., Krishnan, T. (1997) The EM algorithm and extensions. John Wiley and Sons.

Box, G., Jenkins, G., Reinsel, G. (1994) Time Series Analysis: Forecasting and Control. 3 ed. Prentice Hall.

Hastie, T. J.; Tibshirani, R. J. (1990) Generalized Additive Models. Chapman and Hall.

See Also

mnimput, predict

Examples

data(miss)
f <- ~c31+c32+c33+c34+c35
i <- mnimput(f,miss,eps=1e-3,ts=TRUE, method="spline",sp.control=list(df=c(7,7,7,7,7)))
summary(i)

mtsdi documentation built on May 2, 2019, 1:09 p.m.