summary.mtsdi {mtsdi}

Summary Information

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)