predict.mtsdi: Imputed Dataset Extraction

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/mtsdi.r

Description

Extract imputed dataset from a mtsdi object

Usage

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

Arguments

object

imputation object

...

further options passed to the generic function predict

Details

If log tranformation was used, dataset is back transformed accordingly.

Value

A vector of of rows mean with lenght n, where n is the number of observations.

Author(s)

Washington Leite Junger [email protected] and Antonio Ponce de Leon [email protected]

References

Junger, W. L. Ponce de Leon, A. Santos, N. (2003) Missing Data Imputation in Multivariate Time Series via EM Algorithm. Cadernos do IME 15, 8–21.

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, getmean, edaprep

Examples

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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)))
predict(i)

Example output

Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.14-4

This is 'mtsdi' library 0.3.3
         c31       c32         c33        c34        c35
1   5.050000  6.790000  5.74000000  8.7700000  1.3400000
2   9.810000  4.310000  7.92000000  6.8100000 12.8100000
3  15.750000 12.570000 12.01000000 16.8100000 19.5000000
4   4.070000  3.430000  2.64000000  4.5300000  4.3600000
5   1.730000  1.020000  0.11477641  0.3000000  0.5700000
6   0.440000  0.440000  0.35000000  0.7900000  0.7900000
7   1.508246  0.590000  0.06146899  0.9100000  0.9700000
8   1.930000  1.080000  1.50000000  1.4600000  1.1200000
9   2.220000  0.970000  0.38000000  1.7300000  1.9200000
10  4.100000  1.860000  1.18000000  2.1900000  2.1800000
11  2.560000  1.460000  1.50290116  3.1000000  2.6300000
12  4.150000  2.180000  1.65000000  4.5900000  3.2000000
13  6.570000  5.730295  5.97000000  7.4500000  6.9200000
14  4.700000  6.040000  5.79000000  6.9235854  4.5709039
15  4.390000  4.100000  5.41000000  4.0500000  6.8700000
16  8.480000 11.880000 20.49000000 12.1200000 14.0000000
17  1.920000  3.170000  7.84000000  2.5600000  3.7500000
18  1.880000  0.990000  1.88000000  1.1000000  1.6000000
19  1.130000  0.470000  0.93000000  0.8700000  0.8000000
20  0.680000  0.760000  0.74000000  0.6200000  0.8000000
21  2.060000  0.990000  0.41957849  0.7340658  0.7755184
22  9.140000  4.610000  2.12142405  5.3918963  6.7598324
23  2.990000  2.140000  1.06000000  1.2800000  1.0600000
24  9.020000  4.120000 -1.18066303  3.5800000  2.2700000

mtsdi documentation built on May 30, 2017, 4:42 a.m.

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