ComparePCurve {DSWE}

Power curve comparison

Description

Power curve comparison

Usage

ComparePCurve(
  data,
  xCol,
  xCol.circ = NULL,
  yCol,
  testCol,
  testSet = NULL,
  thrs = 0.2,
  conflevel = 0.95,
  gridSize = c(50, 50),
  powerbins = 15,
  baseline = 1,
  limitMemory = TRUE,
  opt_method = "nlminb",
  sampleSize = list(optimSize = 500, bandSize = 5000),
  rngSeed = 1
)

Arguments

data

A list of data sets to be compared, the difference in the mean function is always computed as (f(data2) - f(data1))

xCol

A numeric or vector stating column number of covariates

xCol.circ

A numeric or vector stating column number of circular covariates

yCol

A numeric value stating the column number of the response

testCol

A numeric/vector stating column number of covariates to used in generating test set. Maximum of two columns to be used.

testSet

A matrix or dataframe consisting of test points, default value NULL, if NULL computes test points internally using testCol variables. If not NULL, total number of test points must be less than or equal to 2500.

thrs

A numeric or vector representing threshold for each covariates

conflevel

A numeric between (0,1) representing the statistical significance level for constructing the band

gridSize

A numeric / vector to be used in constructing test set, should be provided when testSet is NuLL, else it is ignored. Default is c(50,50) for 2-dim input which is converted internally to a default of c(1000) for 1-dim input. Total number of test points (product of gridSize vector components) must be less than or equal to 2500.

powerbins

A numeric stating the number of power bins for computing the scaled difference, default is 15.

baseline

An integer between 0 to 2, where 1 indicates to use power curve of first dataset as the base for metric calculation, 2 indicates to use the power curve of second dataset as the base, and 0 indicates to use the average of both power curves as the base. Default is set to 1.

limitMemory

A boolean (True/False) indicating whether to limit the memory use or not. Default is true. If set to true, 5000 datapoints are randomly sampled from each dataset under comparison for inference

opt_method

A string specifying the optimization method to be used for hyperparameter estimation. Current options are: 'L-BFGS-B', 'BFGS', and 'nlminb'. Default is set to 'nlminb'.

sampleSize

A named list of two integer items: optimSize and bandSize, denoting the sample size for each dataset for hyperparameter optimization and confidence band computation, respectively, when limitMemory = TRUE. Default value is list(optimSize = 500, bandSize = 5000).

rngSeed

Random seed for sampling data when limitMemory = TRUE. Default is 1.

Value

a list containing :

• weightedDiff - a numeric, % difference between the functions weighted using the density of the covariates

• weightedStatDiff - a numeric, % statistically significant difference between the functions weighted using the density of the covariates

• scaledDiff - a numeric, % difference between the functions scaled to the orginal data

• scaledStatDiff - a numeric, % statistically significant difference between the functions scaled to the orginal data

• unweightedDiff - a numeric, % difference between the functions unweighted

• unweightedStatDiff - a numeric, % statistically significant difference between the functions unweighted

• reductionRatio - a list consisting of shrinkage ratio of features used in testSet

• mu1 - a vector of prediction on testset using the first data set

• mu2 - a vector of prediction on testset using the second data set

• muDiff - a vector of the difference in prediction (mu2 - mu1) for each test point

• band - a vector for the confidence band at all the testpoints for the two functions to be the same at a given cofidence level.

• confLevel - a numeric representing the statistical significance level for constructing the band

• testSet - a vector/matrix of the test points either provided by user, or generated internally

• estimatedParams - a list of estimated hyperaparameters for the Gaussian process model

• matchedData - a list of two matched datasets as generated by covariate matching

References

For details, see Ding et al. (2021) available doi:10.1016/j.renene.2021.02.136.

Examples

data1 = data1[1:100, ]
data2 = data2[1:100, ]
data = list(data1, data2)
xCol = 2
xCol.circ = NULL
yCol = 7
testCol = 2
testSet = NULL
thrs = 0.2
confLevel = 0.95
gridSize = 20
function_comparison = ComparePCurve(data, xCol, xCol.circ, yCol,
testCol, testSet, thrs, confLevel, gridSize)