predNewLocs: Prediction at new locations based on the fitting results of...

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

Description

Prediction at new locations based on the fitting results of original dataset

Usage

predNewLocs(fitted, newdata, output = NULL, stat_info = NULL,
  model_control = spacetime.control(), cluster_control = NULL)

Arguments

fitted	Can be either a data.frame in memory or HDFS path which contains all fitting results of original dataset.
newdata	A data.frame includes all locations' longitude, latitude, and elevation, where the prediction is to be calculated.
output	The output path of fitting results on HDFS. If data is a data.frame object, the output should be set as default NULL. Since the function will return the fitting results in memory.
stat_info	The RData on HDFS which contains all station metadata. Make sure copy the RData of station_info to HDFS first using rhput.
model_control	Should be a list object generated from spacetime.control function. The list including all necessary smoothing parameters of nonparametric fitting.
cluster_control	Should be a list object generated from mapreduce.control function. The list including all necessary Rhipe parameters and also user tunable MapReduce parameters. It is only necessary for data on HDFS situation. If data is data.frame in memory, this parameter should be kept as default NULL.

Author(s)

Xiaosu Tong

See Also

spacetime.control, mapreduce.control

Examples

## Not run: 
    mcontrol <- spacetime.control(
      vari="resp", time="date", n=576, n.p=12, stat_n=7738, surf = "interpolate",
      s.window="periodic", t.window = 241, degree=2, span=0.015, Edeg=2
    )
    ccontrol <- mapreduce.control(
      libLoc= NULL, reduceTask=169, io_sort=128, slow_starts = 0.5,
      map_jvm = "-Xmx200m", reduce_jvm = "-Xmx200m",
      map_memory = 1024, reduce_memory = 1024,
      reduce_input_buffer_percent=0.4, reduce_parallelcopies=10,
      reduce_merge_inmem=0, task_io_sort_factor=100,
      spill_percent=0.9, reduce_shuffle_input_buffer_percent = 0.8,
      reduce_shuffle_merge_percent = 0.4
    )
    new.grid <- expand.grid(
      lon = seq(-126, -67, by = 0.5),
      lat = seq(25, 49, by = 0.5)
    )
    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)

    #if the original fitting results are in memory
    fitted <- drsstl(
      data=tmax_all,
      output=NULL,
      stat_info="station_info",
      model_control=mcontrol
    )
    predNewLocs(
      original = fitted, newdata = new.grid, model_control = mcontrol
    )

    #if the fitting results are on HDFS
    predNewLocs(
      fitted="/tmp/output/output_bymth", newdata=new.grid, output = "/tmp",
      station_info="/tmp/station_info.RData", model_control = mcontrol,
      cluster_control = ccontrol
    )

## End(Not run)

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