geographically optimal zones-based heterogeneity(GOZH) model

Usage

gozh(formula, data, cores = 1, type = "factor", alpha = 0.95, ...)

Arguments

formula: A formula of GOZH model.
data: A data.frame, tibble or sf object of observation data.
cores: (optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
type: (optional) The type of geographical detector, which must be factor(default), interaction, risk, ecological. You can run one or more types at one time.
alpha: (optional) Specifies the size of confidence level. Default is 0.95.
...: (optional) Other arguments passed to rpart_disc().

Value

A list.

factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

References

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., & Meng, L. (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

data('ndvi')
g = gozh(NDVIchange ~ ., data = ndvi)
g
#> ***   Geographically Optimal Zones-based Heterogeneity Model       
#>                 Factor Detector            
#> 
#> |   variable    | Q-statistic | P-value  |
#> |:-------------:|:-----------:|:--------:|
#> | Precipitation | 0.87255056  | 4.52e-10 |
#> |  Climatezone  | 0.82129550  | 2.50e-10 |
#> |  Tempchange   | 0.33324945  | 1.12e-10 |
#> |  Popdensity   | 0.22321863  | 3.00e-10 |
#> |    Mining     | 0.13982859  | 6.00e-11 |
#> |      GDP      | 0.09170153  | 3.96e-10 |
#>