spicefp | R Documentation |

This function is used to implement the spiceFP approach. This approach transforms 2 (by default) or 3 functional predictors into candidate explonatory matrices in order to identify joint classes of influence. It can take functional predictors and partitioning functions as inputs in order to create candidate matrices to be evaluated. The user can choose among the existing partitioning functions (as logbreaks) or provide his own partitioning functions specific to the functional predictors under consideration. The user can also directly provide candidate matrices already constructed as desired.

spicefp( y, fp1, fp2, fp3 = NULL, fun1, fun2, fun3 = NULL, parlists, xcentering = TRUE, xscaling = FALSE, candmatrices = NULL, K = 2, criterion = "AIC_", penratios = c(1/10, 1/5, 1/2, 1, 2, 5, 10), nknots = 50, appropriate.df = NULL, penfun = NULL, dim.finemesh = c(1000, 1000), file_name = paste0("parametertable", 1:2), ncores = parallel::detectCores() - 1, write.external.file = TRUE )

`y` |
a numerical vector. Contains the dependent variable. This vector will be used as response variable in the construction of models involving each candidate matrix. |

`fp1` |
a numerical matrix with in columns observations of one statistical individual to partition. Each column corresponds to the functional predictor observation for one statistical individual. The order of the statistical individuals is the same as in fp2. It is assumed that no data are missing and that all functional predictors are observed on an equidistant (time) scale. |

`fp2` |
a numerical matrix with the same number of columns and rows as fp1. Columns are also observations. The order of the statistical individuals is the same as in fp1. |

`fp3` |
NULL by default. A numerical matrix with the same number of columns and rows as fp1 and fp2. The order of the statistical individuals is the same as in fp1 and fp2. |

`fun1` |
a function object with 2 arguments. First argument is fp1 and the second is a list of parameters that will help to partition fp1, such as the number of class intervals, etc. For example using the logbreaks function, the list of parameters is equivalent to list(alpha, J). All the arguments to be varied for the creation of different candidate matrices must be stored in the parameter list. The other arguments must be set by default. |

`fun2` |
a function object with 2 arguments. First argument is fp2 and the second is a list of parameters. |

`fun3` |
NULL by default. Same as fun1 and fun2, a function with 2 arguments fp3 and a list of parameters. |

`parlists` |
a list of 2 elements when fp3 and fun3 are equal to NULL or of 3 elements when fp3 and fun3 are provided. All the elements of parlists are lists that have the same length. Each list contains all the lists of parameters that have to be used to create different candidates. The first element of parlists concerns the first functional predictor fp1, the second element is relative to fp2 and the third to fp3. |

`xcentering` |
TRUE by default. Defined whether or not the variables in the new candidate matrices should be centered. |

`xscaling` |
FALSE by default. Defined whether or not the variables in the candidate matrices should be scaled. |

`candmatrices` |
NULL by default. List. Output of the "candidates" function. The spiceFP dimension is its first element. The second contains many lists of one candidate matrix and related vector with index and numbers of class intervals used per predictor. The other elements of the lists are the inputs of "candidates" function. If the user does not need the "candidates" function for the creation of candmatrices, it is possible to build a list while making sure that it respects the same structure as well as the names of the outputs of the "candidates" function. In this case, only the first two elements of the list are essential: spicefp.dimension and candidates. The remaining elements can be NULL. |

`K` |
number of iterations of the spiceFP approach. Equal to 2 by default. |

`criterion` |
character. One of "AIC_", "BIC_", "Cp_". The criterion to be used in each iteration in order to identify the best candidate matrix and to estimate the regulation parameters. This criterion is used to perform model selection as well as variable selection. |

`penratios` |
a numeric vector with values greater than or equal to 0. It represents the ratio between the regularization parameters of parsimony and fusion. When penratios=0, it corresponds to the pure fusion. The higher its value, the more parsimonious the model is. |

`nknots` |
integer. For one value in penratios vector, it represents the
number of models that will be constructed for each candidate matrix. It is
the argument "nlam" of |

`appropriate.df` |
(appropriate degree of freedom) NULL by default. When used,
nknots must be NULL. It is
the argument "df" of |

`penfun` |
function with 2 arguments (dim1, dim2) when dealing with 2 dimensional spiceFP, or with 3 arguments (dim1, dim2, dim3) when dealing with 3 dimensional spiceFP. The argument order in the penalty function is associated with the order of numbers of class intervals used per predictor in the second element of candmatrices argument. NULL by default. When penfun=NULL, getD2dSparse of genlasso or getD3dSparse is used according to the dimension of spiceFP. |

`dim.finemesh` |
numeric vector of length 2 or 3. This vector informs about the dimension of the fine-mesh arrays (or matrices) that will be used for the visualization of the sum of the coefficients selected at different iterations. |

`file_name` |
character vector. Of length K, it contains the list of names that will be used to name the files containing informations on the candidate matrix models |

`ncores` |
numbers of cores that will be used for parallel computation. By default, it is equal to detectCores()-1. |

`write.external.file` |
logical. indicates whether the result table related to each iteration should be written as a file (txt) in your working directory. It is recommended to use write.external.file=TRUE when evaluating a large number of candidate matrices (more than 100) in order to keep memory available. |

Three main steps are involved to implement spiceFP: transformation of functional predictors, creation of a graph of contiguity constraints and identification of the best class intervals and related regression coefficients.

Returns a list with:

- Candidate.Matrices
a list with candidate matrices and their characteristics. same as candmatrices if it has been provided.

- Evaluations
List of length less than or equal to K. Each element of the list contains information about an iteration. Contains the results related to the evaluation of the candidate matrices. These include the name of the file where the model information is stored, the best candidate matrix and related coefficients, the partition vector that indexes it, the

*X β*estimation, the residuals, etc.- coef.NA
List of length less than or equal to K. For each iteration, it contains the coefficient vector where the coefficient value of never-observed joint modalities is NA

- coef.NA.finemeshed
List of length less than or equal to K. For each iteration, the coefficient vector is transformed into fine-mesh array or matrix allowing arithmetic operations to be performed between coefficients coming from different partitions

- spicefp.coef
fine-mesh array or matrix. Sum of the coefficients selected at all iterations

##linbreaks: a function allowing to obtain breaks linearly linbreaks<-function(x,n){ sort(round(seq(trunc(min(x)), ceiling(max(x)+0.001), length.out =unlist(n)+1), 1) ) } # In this example, we will evaluate 2 candidates with 14 temperature # classes and 15 irradiance classes. The irradiance breaks are obtained # according to a log scale (logbreaks function) with different alpha # parameters for each candidate (0.005, 0.01). ## Data and inputs tpr.nclass=14 irdc.nclass=15 irdc.alpha=c(0.005, 0.01) p2<-expand.grid(tpr.nclass, irdc.alpha, irdc.nclass) parlist.tpr<-split(p2[,1], seq(nrow(p2))) parlist.irdc<-split(p2[,2:3], seq(nrow(p2))) parlist.irdc<-lapply( parlist.irdc,function(x){ list(x[[1]],x[[2]])} ) m.irdc <- as.matrix(Irradiance[,-c(1)]) m.tpr <- as.matrix(Temperature[,-c(1)]) # For the constructed models, only two regularization parameter ratios # penratios=c(1/25,5) are used. In a real case, we will have to evaluate # more candidates and regularization parameters ratio. start_time_sp <- Sys.time() ex_sp<-spicefp(y=FerariIndex_Difference$fi_dif, fp1=m.irdc, fp2=m.tpr, fun1=logbreaks, fun2=linbreaks, parlists=list(parlist.irdc, parlist.tpr), penratios=c(1/25,5), appropriate.df=NULL, nknots = 100, ncores =2, write.external.file=FALSE) duration_sp <- Sys.time() - start_time_sp # View(ex_sp$Evaluations[[1]]$Evaluation.results$evaluation.result) # View(ex_sp$Evaluations[[2]]$Evaluation.results$evaluation.result) # Visualization of the coefficients g<-ex_sp$spicefp.coef g.x<-as.numeric(rownames(g)) g.y<-as.numeric(colnames(g)) #library(fields) #plot(c(10,2000),c(15,45),type= "n", axes = FALSE, # xlab = "Irradiance (mmol/m²/s - Logarithmic scale)", # ylab = "Temperature (°C)",log = "x") #rect(min(g.x),min(g.y),max(g.x),max(g.y), col="black", border=NA) #image.plot(g.x,g.y,g, horizontal = FALSE, # col=designer.colors(256, c("blue","white","red")), # add = TRUE) #axis(1) ; axis(2) closeAllConnections()

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