result.main {phenmod}

Result evaluation

Description

Main function for result evaluation.

Usage

result.main(mask.grid, result.grid, plant="beech", model="pim", 
			year=1954, picPath=getwd(), picName="beech-budburst", 
			createFiles=TRUE, rsquarePath=getwd(), 
			rsquareFile="rsquare.RData", rsquare.type="cod", 
			silent=FALSE, withOutliers=FALSE)

Arguments

mask.grid

A grid with spatial information related to the resulting grid of modelling.

result.grid

The resulting grid of modelling. Can be obtained by using pim.solve or tsm.solve.

plant

The plant name for which the values of 'result.grid' are modelled.

model

A character value determining which model was used to create the results (either ‘pim’ or ‘tsm’).

year

The processed year.

picPath

The path where the created png-files should be stored.

picName

The filename of the created png-files.

createFiles

A boolean flag determining wether the results should be stored in files or not.

rsquarePath

The path where the RData-file with the rsquare-dataset should be stored.

rsquareFile

The filename of the RData-file with the rsquare-dataset.

rsquare.type

The value of type (either ‘cod’ or ‘pearson’) determines whether the coefficient of determination or the squared pearson correlation coefficient is used as rsquare.

silent

A boolean value determining wether the function should generate output messages or not.

withOutliers

A boolean value determining wether outliers should be included in extraction and transformation or not.

Details

Main function for result evaluation. Extracts essential values from resulting grid, interpolates and masks values, creates histogramms, scatterplot and maps and calculates r-square.

Author(s)

Daniel Doktor, Maximilian Lange

See Also

result.extract.main,result.extract.interpolate,result.pic.histogramm,result.pic.maps,result.pic.scatterplot,result.rsquare,pim.solve,tsm.solve

Examples

  ## load preprocessed data
  data(dataFinal)
  ## load spatial information
  data(relatedGrid)

  ## set or load params
  params <- c(0, 0.058326, 0.109494, 0.039178, 
			-10.34, -0.89, 18.11,-10.03, 
			28.61, 44.49)

  ## apply model
  result <- pim.solve(params, dataFinal, model.no=11, 
			silent=FALSE, out2File=FALSE)
  ## evaluate resuts
  result.main(mask.grid=relatedGrid, 
		result.grid=result, plant="beech", model="pim", 
		year="1952-2009", picPath=getwd(), picName="beech-budburst",
		createFiles=FALSE, rsquarePath=getwd(), 
		rsquareFile="rsquare.RData", rsquare.type="cod", 
		silent=FALSE, withOutliers=FALSE)