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Function for geographically optimal zones-based heterogeneity detector.

Usage

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

Arguments

formula

A formula of GOZH detector.

data

A data.frame or tibble of observation data.

cores

(optional) A positive integer(default is 1). If cores > 1, a 'parallel' package cluster with that many cores is created and used. You can also supply a cluster object.

type

(optional) The type of geographical detector,which must be one of factor(default), interaction, risk, ecological.

alpha

(optional) Confidence level of the interval,default is 0.95.

...

(optional) Other arguments passed to rpart_disc().

Value

A list of tibble with the corresponding result under different detector types.

factor

the result of factor detector

interaction

the result of interaction detector

risk

the result of risk detector

ecological

the result of ecological detector

Note

Only one type of detector is supported in a gozh_detector() run at a time.

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_detector(NDVIchange ~ ., data = ndvi)
g
#>                 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 |