tsmb: Two-staged Model-Based estmation

In HMB: Hierarchical Model-Based Estimation Approach

Description

Two-staged Model-Based estmation

Usage

tsmb(y_S, X_S, X_Sa, Z_Sa, Z_U)

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.

Details

The TSMB 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 TSMB assumes

E(ε) = 0, E(ε ε') = ω^2 I

E(ξ_k) = 0, E(ξ_k ξ_j') = φ_k,j^2 I

Value

A fitted object of class HMB.

References

Saarela, S., Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, T.G., Nelson, R.F. & Ståhl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information. Annals of Forest Science, 73(4), 895-910.

See Also

summary, getSpec.

Examples

pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
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")]

tsmb_model = tsmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
tsmb_model

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