Similar to 2-dimensional wavelet decomposition, for a given irrelular-lattice field represented by spatial polygons dataframe, the method decompose the field into different scales and a global trend component by EEMD method. The scale components are also called also called intrinsic mode functions (IMFs), which represent different scale information in the spatial field.

1 2 |

`spPolysDf` |
a SpatialPolygonsDataFrame object. |

`valueField` |
a field name that stores value. |

`nMaxIMF` |
maximum number of components to be decomposed. |

`tolSift` |
sift tolerence, a small number. |

`neemd` |
number of EEMD iterations, a large number can make a stable result. |

`wnsd` |
standard deviation of added noise; it is a ratio to the standard deviation of above data. |

`fmodel` |
surface fitting function ("thinplate", "gaussian", "cubic", "multiquadric"). |

`fig` |
whether plot decomposed results. |

A SpatialPolygonsDataFrame with original value, decomposed imfs and global trend.

Hu, M.-G. and J.-F. Wang, et al. A hierarchical-scale decomposition method for irregular lattice field. Computers & Geosciences, submitted.

Huang, N. E. and Z. Shen, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of The Royal Society A - Mathematical, Physical & Engineering Sciences, 1998, 454(1971): 903-995.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Not run:
library(rgdal)
# polygon data
mydata <- system.file("extdata/simu.shp", package = "ibeemd")
layer <- basename(mydata)
layer <- substr(layer, 1, nchar(layer)-4)
mydataDf <- readOGR(dsn=mydata, layer=layer)
#spplot(mydataDf)
rslt <- iBEEMD(
spPolysDf = mydataDf,
valueField = "value",
nMaxIMF = 10,
tolSift = 0.05,
neemd = 500,
wnsd = 0.05,
fmodel = "thinplate",
fig = TRUE)
## End(Not run)
#spplot(rslt)
``` |

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