Description Usage Arguments Details Value References See Also Examples
Generalized Hierarchical Model-Based estimation method
1 | ghmb(y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Sigma_Sa)
|
y_S |
Response object that can be coerced into a column vector. The
|
X_S |
Object of predictors variables that can be coerced into a matrix.
The rows of |
X_Sa |
Object of predictor variables that can be coerced into a matrix. The set Sa is the intermediate sample. |
Z_Sa |
Object of predictor variables that can be coerced into a matrix.
The set Sa is the intermediate sample, and the Z-variables often some
sort of auxilairy, inexpensive data. The rows of |
Z_U |
Object of predictor variables that can be coerced into a matrix. The set U is the universal population sample. |
Omega_S |
The covariance structure of ε_S, up to a constant. |
Sigma_Sa |
The covariance structure of u_Sa, up to a constant. |
The GHMB assumes two models
y = x β + ε
x β = z α + u
ε indep. u
For a sample from the superpopulation, the GHMB assumes
E(ε) = 0, E(ε ε') = ω^2 Ω
E(u) = 0, E(u u') = σ^2 Σ
A fitted object of class HMB.
Saarela, S., Holm, S., Healey, S.P., Andersen, H.-E., Petersson, H., Prentius, W., Patterson, P.L., Næsset, E., Gregoire, T.G. & Ståhl, G. (2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data, Remote Sensing, 10(11), 1832.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | pop_U = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 2500)
pop_S = sample(pop_U, 300)
y_S = HMB_data[pop_S, "GSV"]
X_S = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U = HMB_data[pop_U, c("B20", "B30", "B50")]
Omega_S = diag(1, nrow(X_S))
Sigma_Sa = diag(1, nrow(Z_Sa))
ghmb_model = ghmb(
y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Sigma_Sa)
ghmb_model
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