response.surface.lsd | R Documentation |

This function produces a data object for the three-dimensional graphical representations of the meta-model response surfaces for a set of factors (parameters), including the confidence interval for the surfaces.

response.surface.lsd( data, model, sa, gridSz = 25, defPos = 2, factor1 = 0, factor2 = 0, factor3 = 0 )

`data` |
an object created by a previous call to |

`model` |
an object created by a previous call to |

`sa` |
an object created by a previous call to |

`gridSz` |
integer: the number of divisions in the 3D wire frame grid. The default is 25. |

`defPos` |
1, 2, 3: the position of the default/calibration configuration on the 3 plot sequence. The default is 2 (center position). |

`factor1` |
integer: the index of the first most-important factor: 0 = automatic selection (according to the Sobol index, the default); any other number = the selected factor index, according to DoE factor order. |

`factor2` |
integer: the index of the second most-important factor: 0 = automatic selection (according to the Sobol index, the default); any other number = the selected factor index, according to DoE factor order. |

`factor3` |
integer: the index of the third most-important factor: 0 = automatic selection (according to the Sobol index, the default); any other number = the selected factor index, according to DoE factor order. |

This function produces data for three different wire frame 3D plots. In the 3 plots, the x-y plan is defined by the 2 most-important factors (calculated or set by the user in `sobol.decomposition.lsd`

) and the z axis represents the response variable chosen. The three different plots shows the response surface for three values of the third most-important factor: the minimum, the default/calibration and the maximum. The order the three response surfaces are shown is defined by `defPos`

.

The automatically set most-important factors can be overridden by any factors chosen by the user by the usage of the arguments `factor1`

, `factor2`

and `factor3`

. This way, the response surfaces can be represented for a combination of any 3 factors (parameters) in the model.

The function returns an object/list of class `response`

containing three similar objects, one for each 3D plot, each of them comprised of:

`calib` |
the predicted meta-model response values on each point of the 3D grid. |

`factor` |
the predicted values for each individual factor. |

`default` |
the predicted values for the default/calibration configuration. |

See the note in LSDsensitivity-package for step-by-step instructions on how to perform the complete sensitivity analysis process using LSD and R.

Marcelo C. Pereira [aut, cre] (<https://orcid.org/0000-0002-8069-2734>)

`read.doe.lsd()`

,
`kriging.model.lsd()`

,
`polynomial.model.lsd()`

,
`sobol.decomposition.lsd()`

# get the example directory name path <- system.file( "extdata/sobol", package = "LSDsensitivity" ) # Steps to use this function: # 1. define the variables you want to use in the analysis # 2. load data from a LSD simulation saved results using read.doe.lsd # 3. fit a Kriging (or polynomial) meta-model using kriging.model.lsd # 4. identify the most influential factors applying sobol.decomposition.lsd # 5. calculate the response surface for the selected factors using model.limits.lsd # 6. plot the response surface lsdVars <- c( "var1", "var2", "var3" ) # the definition of existing variables dataSet <- read.doe.lsd( path, # data files folder "Sim3", # data files base name (same as .lsd file) "var3", # variable name to perform the sensitivity analysis does = 2, # number of experiments (data + external validation) saveVars = lsdVars ) # LSD variables to keep in dataset model <- kriging.model.lsd( dataSet ) # estimate best Kriging meta-model SA <- sobol.decomposition.lsd( dataSet, model ) # find Sobol indexes resp <- response.surface.lsd( dataSet, # LSD experimental data set model, # estimated meta-model SA ) # Sobol sensitivity analysis results theta3d <- 310 # horizontal view angle phi3d <- 30 # vertical view angle grid3d <- 25 for( i in 1 : 3 ) { # do for each top factor # plot 3D grid charts zMat <- matrix( resp$calib[[ i ]]$mean, grid3d, grid3d, byrow = TRUE ) zlim <- range( zMat, na.rm = TRUE ) vt <- persp( resp$grid[[ 1 ]], resp$grid[[ 2 ]], zMat, col = "gray90", xlab = colnames( dataSet$doe )[ SA$topEffect[ 1 ] ], zlim = zlim, ylab = colnames( dataSet$doe )[ SA$topEffect[ 2 ] ], zlab = dataSet$saVarName, theta = theta3d, phi = phi3d, ticktype = "detailed" ) }

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