Description Usage Arguments Value References Examples

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

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

`X` |
Matrix of predictions, with |

`Var` |
Matrix of corresponding prediction variances, same dimension as |

`Corr` |
Matrix or value of correlations between observations from different time points, by default |

`$weights` |
Estimated Kalman gain according to Eq.[7] in Ehlers |

`$PreDA` |
Predicted values through Data Assimilation according to Eq.[5] in Ehlers |

`$VarDA` |
Corresponding estimated variances according to Eq.[6] in Ehlers |

`$Correlation` |
Correlation matrix. |

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

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);
``` |

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