predNew_mr: Prediction at new locations based on the fitting results on...

In XiaosuTong/drsstl: Spatial Seasonal Trend Loess analysis routine using divide and recombined

Description

The prediction at new locations are calculated based on the fitting results saved on HDFS based on the original dataset.

Usage

predNew_mr(newdata, input, output, info, mlcontrol = spacetime.control(),
  clcontrol = mapreduce.control())

Arguments

newdata	A data.frame includes all locations' longitude, latitude, and elevation, where the prediction is to be calculated.
input	The path of input file on HDFS. It should be by-month division with all fitting results of original dataset
output	The path of output on HDFS where all the intermediate outputs will be saved.
info	The RData path on HDFS which contains all station metadata of original dataset
mlcontrol	Should be a list object generated from spacetime.control function. The list including all necessary smoothing parameters of nonparametric fitting.
clcontrol	Should be a list object generated from mapreduce.control function. The list including all necessary Rhipe parameters and also user tunable MapReduce parameters.

Author(s)

Xiaosu Tong

See Also

spacetime.control, mapreduce.control

Examples

## Not run: 
    clcontrol <- mapreduce.control(libLoc=NULL, reduceTask=95, io_sort=100, slow_starts = 0.5)
    mlcontrol <- spacetime.control(
      vari="resp", time="date", n=576, n.p=12, stat_n=7738,
      s.window="periodic", t.window = 241, degree=2, span=0.015, Edeg=2
    )

    new.grid <- expand.grid(
      lon = seq(-126, -67, by = 0.1),
      lat = seq(25, 49, by = 0.1)
    )
    instate <- !is.na(map.where("state", new.grid$lon, new.grid$lat))
    new.grid <- new.grid[instate, ]

    elev.fit <- spaloess( elev ~ lon + lat,
      data = station_info,
      degree = 2,
      span = 0.015,
      distance = "Latlong",
      normalize = FALSE,
      napred = FALSE,
      alltree = FALSE,
      family="symmetric",
      control=loess.control(surface = "direct")
    )
    grid.fit <- predloess(
      object = elev.fit,
      newdata = data.frame(
        lon = new.grid$lon,
        lat = new.grid$lat
      )
    )
    new.grid$elev2 <- log2(grid.fit + 128)

    predNew_mr(
      newdata=new.grid, input="/tmp/output_bymth", output = "/tmp",
      info="/tmp/station_info.RData", mlcontrol=mlcontrol, clcontrol=clcontrol
    )

## End(Not run)

XiaosuTong/drsstl documentation built on May 9, 2019, 11:06 p.m.