gtsmb {HMB} | R Documentation |
Generalized Two-Staged Model-Based estmation
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
|
X_S |
Object of predictors variables that can be coersed into a matrix.
The rows of |
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_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 |
Phis_Sa |
A 3D array, where the third dimension corresponds to the
covariance structure of
|
Details
The GTSMB assumes the superpopulations
y = \boldsymbol{x} \boldsymbol{\beta} + \epsilon
x_k = \boldsymbol{z} \boldsymbol{\gamma}_k + \xi_k
\epsilon \perp \xi_k
For a sample from the superpopulation, the GTSMB assumes
E(\boldsymbol{\epsilon}) = \mathbf{0},
E(\boldsymbol{\epsilon} \boldsymbol{\epsilon}^T) = \omega^2 \boldsymbol{\Omega}
E(\boldsymbol{\xi}_k) = \mathbf{0},
E(\boldsymbol{\xi}_k \boldsymbol{\xi}_j^T) = \theta_{\Phi,k,j}^2 \boldsymbol{\Phi}_{k,j},
\theta_{\Phi,k,j}^2 \boldsymbol{\Phi}_{k,j} = \theta_{\Phi,j,k}^2 \boldsymbol{\Phi}_{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
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