[R-sig-Geo] Question re: Intamap package / Interpolate method: Inputs NOT both Spatial Points Data Frame?

[Rick Reeves]

Hello Edzer:

(by the way, I am using R 2.12)

Yes, I had - BUT - I rechecked my script, and the 'interpolate' method 
in intamap was apparently MASKED
by the 'interpolate' method in the subsequently loaded raster package. 
(in-progress code is below...)

I canceled the loading of the raster package, and re-ran the script with 
better results. This time,
the line:

   interpImage <- interpolate(gddThisYear,monarchStaLocs,list(mean=TRUE, 
variance=TRUE))

results in:
-------------------------------------------------
R 2010-10-26 13:31:48 interpolating 133 observations, 94 prediction 
locations
[1] "estimated time for  copula 60.5284609039258"
Error in min(dataObs) : invalid 'type' (S4) of argument
In addition: Warning message:
In predictTime(nObs = dim(observations)[1], nPred = 
dim(coordinates(predictionLocations))[1],  :

  using standard model for estimating time. For better
  platform spesific predictions, please run
  timeModels <- generateTimeModels()
   and save the workspace
------------------------------------------------
So intamap::interpolate documentation matches the argument list. But, my 
script is still not correct.

I suspect that this error happens because I do not fully understand how 
to use 'interpolate' for this task.

My task could also be solved by an approach like that demonstrated in 
Dylan Beaudette's excellent example at:

http://casoilresource.lawr.ucdavis.edu/drupal/node/442

.. but using intamap::interpolate would be much simpler if I have made 
the correct assumptions.

What are your thoughts?

Thanks,
Rick R
























##############################################################################################################

interpGddData <- function()
{
#   require(gstat)
    require(intamap)
#   require(rgdal)
    require(maptools)
#   require(raster)

    setwd("v:/MigrationDynamics/NCEAS PROCESS")

    firstGddCol <- 5

#   setwd("/data/scientist_share/scientist/MigrationDynamics/NCEAS 
PROCESS")

    gddTable <- readShapePoints("OhioGDDStationsGP.shp")
    monarchStaLocs <- readShapePoints("OH_Locations.shp")

    numGddCols <- 1 + (ncol(gddTable) - firstGddCol)

# The Idea: For each sampling year, generate one map for each GDD parameter.
# Each map is an interpolated 'surface' from all of the GDD-calculated 
'station'
# locations in the input file.

    theYears <- unique(gddTable at data$YR)
    numYears <- length(theYears)

    for (curYear in theYears)
    {
       gddThisYear <- gddTable[which(gddTable at data$YR==curYear),] # 
table entries: all stations, current year

    # make input table copy to append with interpolated GDD values for 
current year

       monarchStasWithGDD <- gddThisYear

# Write one output ESRI Shape File for each year, containing the Monarch 
station
# location points with all of the interpolated GDD parameters for the 
current year appended.

       outFileName <- sprintf("OH_MonarchSta_GDD_%d.tif",curYear)
#      theBase <- [,1:(firstGddColumn-1)]

# Create one interpolated image for each parameter, resulting in a named 
matrix file.

       for (jCtr in firstGddCol : numGddCols)
       {
#         fieldName <- names(gddThisYear)[jCtr] # we need this for the 
fileName
#         fileName <- sprintf("%s_%d.tif",fieldName,curYear)

          gddThisYear$value <- gddThisYear[,jCtr]

# Note: gddThisYear needs to be upgraded to a spatialPixelsDataFrame  so 
that it is a grid
# Despite documentation, A SpatialPointsDataFrame object is not a valid 
input.
# first argument(spatialPoints): measurement locations (in our case, the 
GDD values)
# second argument(spatialPixels): where to place the interpolated grid 
of measurement values.
# I need to make an empty grid to hold the data.

          interpImage <- 
interpolate(gddThisYear,monarchStaLocs,list(mean=TRUE, variance=TRUE))
          message("Latest interp done!")
          browser()

# Assign the result of interpolation as an attribute to the Monarch 
Station Locations File: 'newcol'

          cbind(monarchStasWithGDD at data,newcol)
       }
# Write the output file

       writePointsShape(monarchStasWithGDD,outFileName)

    }
}
############################################################################################################



On 10/26/2010 1:09 PM, Edzer Pebesma wrote:
> Are you sure you loaded package intamap before trying this?
>
> On 10/26/2010 09:05 PM, Rick Reeves wrote:
>> Hello:
>>
>> I note a conflict in the documentation for Intamap/interpolate(),
>> to know how to resolve:
>>
>> I have two SpatalPointsDataFrame objects:
>>
>>   growingDegreeObservations<- readShapePoints("OhioGDDStationsGP.shp")
>> # computed surface phenomena, for individual lat/lon points
>>   OrgStaLocs<- readShapePoints("Org_Locations.shp")                    #
>> species observation stations, for DIFFERENT lat/lon points
>>
>> # Objective: Interpolate the growingDegree points into a SpatialGrid (?)
>> raster field, and then
>> # overlay the observation stations on to the field, and assign field
>> values to each observation station points
>>
>> # according to intamap documentation, the interpolate() method accepts
>> two SptatialPointsDataFrame objects, interpolates
>> # the first ($value field) into a grid, and assigns interpolated
>> observations to the predicted locations (monarchStaLocs).
>>
>> interpImage<- interpolate(growingDegreeObservations
>> ,OrgStaLocs,list(mean=TRUE, variance=TRUE))
>>
>> However, when this line is executed, the error occurs:
>>
>> Error in function (classes, fdef, mtable)  :
>>    unable to find an inherited method for function "interpolate", for
>> signature "SpatialPointsDataFrame"
>>
>> I guess that this refers to the first argument, and that the argument
>> must be a SpatialPixels or SpatialGrid data frame.
>> Questions:
>>
>> 1) Do I understand the inputs and outputs of interpolate() correctly?
>>
>> 2) How best to transform the incorrect (SpatialPointsDataFrame) argument
>>       to match the required inputs? E.G., create the interpolated
>> SpatialPixelsDataFrame
>>       of observations before calling interpolate())
>>
>> 3) Is there a better R technique (e.g., direct call to krige()) for
>> computing my solution?
>>
>> Thanks, Rick R
>>
>>
>>
>>
>>
>>
>>