gtsmb: Generalized Two-Staged Model-Based estmation

In HMB: Hierarchical Model-Based Estimation Approach

Description

Generalized Two-Staged Model-Based estmation

Usage

gtsmb(y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Phis_Sa)

Arguments

y_S	Response object that can be coersed into a column vector. The _S denotes that y is part of the sample S, with N_S ≤ N_Sa ≤ N_U.
X_S	Object of predictors variables that can be coersed into a matrix. The rows of X_S correspond to the rows of y_S.
X_Sa	Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample.
Z_Sa	Object of predictor variables that can be coresed 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_Sa correspond to the rows of X_Sa
Z_U	Object of predictor variables that can be coresed into a matrix. The set U is the universal population sample.
Omega_S	The covariance structure of ε_S, up to a constant.
Phis_Sa	A 3D array, where the third dimension corresponds to the covariance structure of E(ξ_k,Sa ξ_j,Sa'), in the order k=1,...,p, j=1,...,k. For p = 3, the order (k,j) will thus be (1,1), (2,1), (2,2), (3,1), (3,2), (3,3).

Details

The GTSMB assumes the superpopulations

y = x β + ε

x_k = z γ_k + ξ_k

ε indep. ξ_k

For a sample from the superpopulation, the GTSMB assumes

E(ε) = 0, E(ε ε') = ω^2 Ω

E(ξ_k) = 0, E(ξ_k ξ_j') = θ_Phi,k,j Φ_k,j, θ_Phi,k,j Φ_k,j = θ_Phi,j,k Φ_j,k

Value

A fitted object of class HMB.

References

Holm, S., Nelson, R. & Ståhl, G. (2017) Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar. Remote Sensing of Environment, 197, 85–97.

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.

See Also

summary, getSpec.

Examples

pop_U   = sample(nrow(HMB_data), 20000)
pop_Sa  = sample(pop_U, 500)
pop_S   = sample(pop_U, 100)

y_S     = HMB_data[pop_S, "GSV"]
X_S     = HMB_data[pop_S, c("hMAX", "h80", "CRR")]
X_Sa    = HMB_data[pop_Sa, c("hMAX", "h80", "CRR")]
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))
Phis_Sa = array(0, c(nrow(X_Sa), nrow(X_Sa), ncol(X_Sa) * (ncol(X_Sa) + 1) / 2))
Phis_Sa[, , 1] = diag(1, nrow(X_Sa)) # Phi(1,1)
Phis_Sa[, , 2] = diag(1, nrow(X_Sa)) # Phi(2,1)
Phis_Sa[, , 3] = diag(1, nrow(X_Sa)) # Phi(2,2)
Phis_Sa[, , 4] = diag(1, nrow(X_Sa)) # Phi(3,1)
Phis_Sa[, , 5] = diag(1, nrow(X_Sa)) # Phi(3,2)
Phis_Sa[, , 6] = diag(1, nrow(X_Sa)) # Phi(3,3)

gtsmb_model = gtsmb(y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Phis_Sa)
gtsmb_model

HMB documentation built on July 8, 2020, 7:34 p.m.