Description Usage Arguments Details Value References See Also Examples
Two-staged Model-Based estmation
1 | tsmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
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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. |
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
A fitted object of class HMB.
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.
1 2 3 4 5 6 7 8 9 10 11 12 | 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
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