DatAssim (version 1.0)

datassim: Data Assimilation

Description

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

Usage

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<U+00F6>m, M., Grafstr<U+00F6>m, A., Persson, H., Olsson, H. & St<U+00E5>hl, G. (2017). Assessing error correlations in remote sensing-based predictions of forest attributes for improved data assimilation. DOI

Examples

# NOT RUN {
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);
# }
# NOT RUN {
<!-- % -->
# }
# NOT RUN {
# Predictions based on ten observations, at four different time points
Prediction = cbind(Pred1, Pred2, Pred3, Pred4); 
# }
# NOT RUN {
<!-- % -->
# }
# NOT RUN {
Var1 = matrix(10000, 10);
Var2 = matrix(900, 10);
Var3 = matrix(1600, 10);
Var4 = matrix(10000, 10);
# }
# NOT RUN {
<!-- % -->
# }
# NOT RUN {
# Corresponding prediction variances
Variance = cbind(Var1, Var2, Var3, Var4);
# }
# NOT RUN {
<!-- % -->
# }
# NOT RUN {
# Corr = 0 by default
datassim(X = Prediction, Var = Variance);
# }
# NOT RUN {
<!-- % -->
# }
# NOT RUN {
# Corr = 0.5
datassim(Prediction, Variance, 0.5); 
# }
# NOT RUN {
<!-- % -->
# }
# NOT RUN {
Corr = cor(Prediction);
datassim(Prediction, Variance, Corr);
# }
# NOT RUN {
<!-- % -->
# }