Description Usage Arguments Details Value Author(s) See Also Examples

This function uses spatial interpolation methods for obtaining the utility surface. It depends on a set of points provided by the user and on a method selected for interpolation. The available interpolation methods are: bilinear, splines, idw and krige. Check the details section for more on these methods.

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`trues` |
A vector of true target variable values. Can be NULL. See details section. |

`preds` |
A vector with corresponding predicted values for the trues provided. Can be NULL. See details section. |

`type` |
A character specifying the type of surface that is being interpolated. It can be set to either "utility", "cost" or "benefit". When set to "cost" we assume that the diagonal of the surface (where y=y.pred) is zero. Therefore, in this case, the user doesn't need to set the control.parms parameter. |

`control.parms` |
These parameters are necessary for utility and benefit surfaces. control.parms can be obtained with a call to function phi.control. This provides a list with the parameters used for defining the relevance function phi. The points provided through these parameters are used for interpolating the utility surface because the relevance function matches the diagonal of the utility, i.e., the relevance function phi corresponds to the utility of accurate predictions (y = y.pred). Alternatively, the user may build the control.parms list. When the user selects a cost surface, control.parms can simply be NULL. In this case, we assume that the surface diagonal is zero. If control.parms are not NULL, then specified points are used. See examples section. |

`minds` |
The lower bound of the target variable considered for interpolation. A new minds value may be necessary when trues and/or preds provided have lower values than minds. This is handled by extrapolation and a warning is issued (see details). |

`maxds` |
The upper bound of the target variable considered for interpolation. A new maxds value may be necessary when trues and/or preds provided have values higher than maxds. This is handled by extrapolation and a warning is issued (see details). |

`m.pts` |
A 3-column matrix with interpolating points for the off-diagonal cases (i.e., y != y.pred), provided by the user. The first column has the y value, the second column the y.pred value and the third column has the corresponding utility value. At least, the off diagonal domain boundary points (i.e., points (minds, maxds, util) and (maxds, minds, util) ) must be provided in this matrix. Moreover, the points provided through this parameter must be in [minds, maxds] range. |

`method` |
A character indicating which interpolation method should be used. Can be one of: "bilinear", "splines", "idw" or "krige". See details section for a description of the available methods. |

`visual` |
Logical. If TRUE a plot of the utility surface isometrics obtained and the points provided is displayed. If FALSE (the default) no image is plotted. |

`eps` |
Numeric value for the precision considered during the interpolation. Defaults to 0.1. Only relevant if a plot is displayed, or when full.output is set to TRUE. See details section. |

`full.output` |
Logical. If FALSE (the default) only the results from points provided through parameters trues and preds are returned. If TRUE a matrix with the utility of all points in domain (considering the eps provided) are returned. See details section. |

`method`

parameter:The parameter

`method`

allows the user to select from a set of interpolation methods. The available methods are as follows:-

`bilinear`

: local fitting of a polynomial surface of degree 1 obtained through loess function of stats package.-

`splines`

: multilevel B-splines interpolation method obtained through MBA R package.-

`idw`

: inverse distance weighted interpolation obtained through R package gstat.-

`krige`

: automatic kriging obtained using automap R package.- extrapolation:
when trues or preds provided are outside the range [minds, maxds] the function performs an extrapolation of the domain. To achieve this, four new points are added that extend the initial target variable domain ([minds, maxds]). This extrapolation is performed as follows:

- first: determine inc.fac, the distance necessary to increase (the largest value needed increase the axes to include all trues and preds provided);

- second: define the new target variable domain ([minds - inc.fac, maxds + inc.fac]);

- third: add two new diagonal points evaluating the relevance function on these new points (i.e. add (minds-inc.fac, minds-nc.fac, phi(minds-inc.fac, minds.inc.fac)) and (maxds+inc.fac, maxds+inc.fac, phi(maxds+inc.fac, maxds+inc.fac)));

- fourth: add two new off-diagonal points using the new min and max values of the domain and the utility provided by the user for the two mandatory points (minds, maxds) and (maxds, minds).

In order to avoid this extrapolation, the user must ensure that the values provided in trues and preds vectors are inside the [minds, maxds] range provided.

`full.output`

parameter:This parameter is used to select which utility values are returned. There are two options for this parameter:

- FALSE: This means that the user is only interested in obtaining the utility surface values of some points (y, y.pred). In this case, the y and y.pred should be provided through parameters trues and preds and the function returns a vector with the utility for the corresponding points.

- TRUE: The user is interested in obtaining the utility surface values on a grid of equally spaced values of the target variable domain. In this case, there is no need for specifying parameters trues and preds, because the goal is not to observe the utility of these points. Parameters trues and preds can be set to NULL in this case. The function returns a lXl matrix with the utility of all points in a grid defined as follows. The l equally spaced points are a sequence that starts at minds-0.01, ends at maxds+0.01 and are incremented by eps value.

The function returns a vector with utility of the points provided through the vectors trues and preds.

Paula Branco [email protected], Rita Ribeiro [email protected] and Luis Torgo [email protected]

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## Not run:
# examples with a utility surface
data(Boston, package = "MASS")
tgt <- which(colnames(Boston) == "medv")
sp <- sample(1:nrow(Boston), as.integer(0.7*nrow(Boston)))
train <- Boston[sp,]
test <- Boston[-sp,]
control.parms <- phi.control(Boston[,tgt], method="extremes", extr.type="both")
# the boundaries of the domain considered
minds <- min(Boston[,tgt])-5
maxds <- max(Boston[,tgt])+5
# build m.pts to include at least the utility of the
# points (minds, maxds) and (maxds, minds)
m.pts <- matrix(c(minds, maxds, -1, maxds, minds, 0),
byrow=TRUE, ncol=3)
trues <- test[,tgt]
library(randomForest)
model <- randomForest(medv~., train)
preds <- predict(model, test)
resLIN <- UtilInterpol(trues, preds, type="util", control.parms, minds, maxds, m.pts,
method = "bilinear", visual=TRUE)
resIDW <- UtilInterpol(trues, preds, type="util", control.parms, minds, maxds, m.pts,
method = "idw", visual=TRUE)
resSPL <- UtilInterpol(trues, preds, type="util", control.parms, minds, maxds, m.pts,
method = "spl", visual=TRUE)
resKRIGE <- UtilInterpol(trues, preds, type="util", control.parms, minds, maxds, m.pts,
method = "krige", visual=TRUE)
# examples with a cost surface
data(Boston, package = "MASS")
tgt <- which(colnames(Boston) == "medv")
sp <- sample(1:nrow(Boston), as.integer(0.7*nrow(Boston)))
train <- Boston[sp,]
test <- Boston[-sp,]
# the boundaries of the domain considered
minds <- min(Boston[,tgt])-5
maxds <- max(Boston[,tgt])+5
# build m.pts to include at least the utility of the
# points (minds, maxds) and (maxds, minds)
m.pts <- matrix(c(minds, maxds, 5, maxds, minds, 20),
byrow=TRUE, ncol=3)
trues <- test[,tgt]
# train a model and predict on test set
library(randomForest)
model <- randomForest(medv~., train)
preds <- predict(model, test)
costLIN <- UtilInterpol(trues, preds, type="cost", control.parms=NULL, minds, maxds, m.pts,
method = "bilinear", visual=TRUE )
costSPL <- UtilInterpol(trues, preds, type="cost", control.parms=NULL, minds, maxds, m.pts,
method = "spl", visual=TRUE)
costKRIGE <- UtilInterpol(trues, preds, type="cost", control.parms=NULL, minds, maxds, m.pts,
method = "krige", visual=TRUE)
costIDW <- UtilInterpol(trues, preds, type="cost", control.parms=NULL, minds, maxds, m.pts,
method = "idw", visual=TRUE)
# if the user has a cost matrix and wants to specify the control.parms:
my.pts <- matrix(c(0, 0, 0, 10, 0, 0, 20, 0, 0, 45, 0, 0), byrow=TRUE, ncol=3)
control.parms <- phi.control(trues, method="range", control.pts = my.pts)
costLIN <- UtilInterpol(trues, preds, type="cost", control.parms=control.parms,
minds, maxds, m.pts, method = "bilinear", visual=TRUE )
# first trues and preds
trues[1:5]
preds[1:5]
trues[1:5]-preds[1:5]
# first cost results on these predictions for cost surface costIDW
costIDW[1:5]
# a summary of these prediction costs:
summary(costIDW)
#example with a benefit surface
# define control.parms either by defining a list with 3 named elements
# or by calling phi.control function with method range and passing
# the selected control.pts
control.parms <- list(method="range", npts=5,
control.pts=c(0,1,0,10,5,0.5,20,10,0.5,30,30,0,50,30,0))
m.pts <- matrix(c(minds, maxds, 0, maxds, minds, 0),
byrow=TRUE, ncol=3)
benLIN <- UtilInterpol(trues, preds, type="ben", control.parms, minds, maxds, m.pts,
method = "bilinear", visual=TRUE)
benIDW <- UtilInterpol(trues, preds, type="ben", control.parms, minds, maxds, m.pts,
method = "idw", visual=TRUE)
benSPL <- UtilInterpol(trues, preds, type="ben", control.parms, minds, maxds, m.pts,
method = "spl", visual=TRUE)
benKRIGE <- UtilInterpol(trues, preds, type="ben", control.parms, minds, maxds, m.pts,
method = "krige", visual=TRUE)
## End(Not run)
``` |

UBL documentation built on July 13, 2017, 5:02 p.m.

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