# datassim: Data Assimilation In DatAssim: Data Assimilation

## Description

This function estimates a variable of interest through Data Assimilation technique by incorporating results from previous assessments.

## Usage

 `1` ```datassim(X, Var, Corr) ```

## Arguments

 `X` Matrix of predictions, with `n` number of rows as the number of observations, and `t` number of columns as the number of time points from which data were collected.
 `Var` Matrix of corresponding prediction variances, same dimension as `X`.
 `Corr` Matrix or value of correlations between observations from different time points, by default `Corr` = 0.

## Value

 `\$weights` Estimated Kalman gain according to Eq.[7] in Ehlers et al. (2017).
 `\$PreDA` Predicted values through Data Assimilation according to Eq.[5] in Ehlers et al. (2017).
 `\$VarDA` Corresponding estimated variances according to Eq.[6] in Ehlers et al. (2017).
 `\$Correlation` Correlation matrix.

## References

Ehlers, S., Saarela, S., Lindgren, N., Lindberg, E., Nystr<c3><b6>m, M., Grafstr<c3><b6>m, A., Persson, H., Olsson, H. & St<c3><a5>hl, G. (2017). Assessing error correlations in remote sensing-based predictions of forest attributes for improved data assimilation. DOI

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```Pred1 = rnorm(10, mean = 50, sd = 100); Pred2 = rnorm(10, mean = 50, sd = 30); Pred3 = rnorm(10, mean = 50, sd = 80); Pred4 = rnorm(10, mean = 50, sd = 100); # Predictions based on ten observations, at four different time points Prediction = cbind(Pred1, Pred2, Pred3, Pred4); Var1 = matrix(10000, 10); Var2 = matrix(900, 10); Var3 = matrix(1600, 10); Var4 = matrix(10000, 10); # Corresponding prediction variances Variance = cbind(Var1, Var2, Var3, Var4); # Corr = 0 by default datassim(X = Prediction, Var = Variance); # Corr = 0.5 datassim(Prediction, Variance, 0.5); Corr = cor(Prediction); datassim(Prediction, Variance, Corr); ```

DatAssim documentation built on May 2, 2019, 3:46 p.m.