predict_spm | R Documentation |
Realizes predictions that can be useful when researchers are interested in predict a variable observed in one political division of a city (or state) on another division of the same region.
predict_spm(x, ...) ## S3 method for class 'spm_fit' predict_spm(x, .aggregate = TRUE, ...) ## S3 method for class 'sf' predict_spm(x, spm_obj, n_pts, type, outer_poly = NULL, id_var, ...)
x |
a |
... |
additional parameters |
.aggregate |
|
spm_obj |
an object of either class |
n_pts |
a |
type |
|
outer_poly |
(object) |
id_var |
if |
a list
of size 4 belonging to the class spm_pred
. This
list contains the predicted values and the mean and covariance matrix
associated with the conditional distribution used to compute the
predictions.
data(liv_lsoa) ## loading the LSOA data data(liv_msoa) ## loading the MSOA data msoa_spm <- sf_to_spm(sf_obj = liv_msoa, n_pts = 500, type = "regular", by_polygon = FALSE, poly_ids = "msoa11cd", var_ids = "leb_est") ## fitting model theta_st_msoa <- c("phi" = 1) # initial value for the range parameter fit_msoa <- fit_spm(x = msoa_spm, theta_st = theta_st_msoa, model = "matern", nu = .5, apply_exp = TRUE, opt_method = "L-BFGS-B", control = list(maxit = 500)) pred_lsoa <- predict_spm(x = liv_lsoa, spm_obj = fit_msoa, id_var = "lsoa11cd")
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