datassim {DatAssim} | R Documentation |
This function estimates a variable of interest through Data Assimilation technique by incorporating results from previous assessments.
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 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. |
Ehlers, S., Saarela, S., Lindgren, N., Lindberg, E., Nyström, M., Grafström, A., Persson, H., Olsson, H. & Ståhl, G. (2017). Assessing error correlations in remote sensing-based predictions of forest attributes for improved data assimilation. DOI
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);