Create an spatio - temporal object with regular data, in order to employ median polish technique.

1 | ```
ConstructMPst(valuest,time,pts,Delta)
``` |

`valuest` |
data.frame in which different columns refer to different locations, and each row reflects a particular observation time. |

`time` |
indicate the time of valuest, the intervals of time must be regular. |

`pts` |
data.frame that hold three dimensions spatial coordinates |

`Delta` |
vector with number of divisions of each spatial direction. c(Delta |

This function configures an irregular distribution of spatio – temporal data in four - ways. Therefore, the new data corresponds to the average of values and coordinates of every spatio -temporal cell.

An object of class ConstructMPst with the following list of components:

`results` |
average value on the stations set into unity spatio – temporal defined for delta. |

`Value` |
array with the results organized by cells, the size of the cells is defined in Delta. |

`valuest` |
valuest. |

`pts` |
pts. |

`time` |
time. |

`Delta` |
Delta. |

Martínez, W. A., Melo, C. E., & Melo, O. O. (2017). *Median Polish Kriging for space–time analysis of precipitation* Spatial Statistics, 19, 1-20. [link]

Berke, O. (2001). *Modified median polish kriging and its application to the wolfcamp - aquifer data.* Environmetrics, 12(8):731-748.[link]

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Not run:
library(zoo)
data(Metadb)
#records of monthly precipitation from january 2007 to january 2010
Metadb<-Metadb[,c(1:4,89:125)]
x<-matrix(0,1,37)
for(i in 1:37){
x[,i] <- 2007 + (seq(0, 36)/12)[i]
}
x<-as.Date (as.yearmon(x), frac = 1)
time = as.POSIXct(x, tz = "GMT")
MPST<-ConstructMPst(Metadb[,-c(1:4)],time,pts=Metadb[,2:4],Delta=c(7,6,5))
## End(Not run)
``` |

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