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Changelog

Source: NEWS.md

fda.usc 2.2.0

Modification in several Rd fiels: Lost braces in itemize.

classif.DD.aux.r, solve.ab has been renamed to solve_ab.

classif.kfold.R, all.vars1 has been renamed to all_vars1.

kmeans.fd.dist.R, predict.kmeans.fd has been renamed to predict_kmeans.fd.

kmeans.fd.R, kmeans.assig.groups has been renamed to kmeans_assig_groups.

kmeans.fd.R, kmeans.centers.update has been renamed to kmeans_centers_update.

kmeans.assig.groups.R, kmeans.assig.groups has been renamed to kmeans_assig_groups.

kmeans.assig.groups.R, kmeans.assig.groups has been renamed to kmeans_assig_groups.

plot.fdata.R, image.scale has been renamed to image_scale.

predict.gls.nlme.r, predict.gls has been renamed to predict_gls.

predict.gls.nlme.r, predict.mregre has been renamed to predict_._mregre.

predict.mfregre.r, predict.gls has been renamed to predict_gls.

quantile.outliers.pond.r, quantile_outliers.pond has been renamed to quantile_outliers_pond.

quantile.outliers.trim.r, quantile_outliers.trim has been renamed to quantile_outliers_trim.

export plot.lfdata function

fda.usc 2.1.0

  • fda.usc 2.1.0 is a major release with several new feature and fixed bugs.

  • fdata2basis() always return centred fdataobj and mean. The mean is computed using the basis.

  • New funtions: fEqMoments.test(), fmean.test.fdata(), cov.test.fdata() for checking the equality of means and/or covariance between two populations under gaussianity.

  • New funtions: fEqDistrib.test(), XYRP.test(), MMD.test(), MMDA.test(), fEqDistrib.test() for checking the equality of distributions between two functional populations.

  • The wavelength units in the Tecator dataset are labeled as nm in dataset description, but they are nm in the original description. (bug detected by vnmabus)

fda.usc 2.0.3

fda.usc 2.0.2

fda.usc 2.0.1

  • Modification in fdata() function to avoid class()== and class()!= instead use is(),

  • kmeans.fd() function:

    1. “par.ini” argument is depreciated, the user can use “method” argument.
    2. “cluster.size” argument are added.
    3. New internal function predict.kmeans.fd.

  • Bug corrected in internal function pred2glm2boost(), it is used for predictions of classiff.DD() outputs

  • New functions: Ops.ldata(), Math.ldata(), Summary.ldata(), mean.ldata() and mean.fdata() (deprecated ldata.mean(), mfdata.mean())

  • Modifications in ldata.cen()

  • A bug in S.LPR() has been fixed.

  • A bug in internal function wmestadis() used in fanova.onefactor() has been fixed.

fda.usc 2.0.0

Version 2.0.0 is a major release with several new features, including:

  • inprod.fdata() and metric.lp funcitons addapted to parallel backend.

  • zzz.R file includes .onAttach() function (welcome package message)

  • ops.fda.usc.R file includes ops.fda.usc() function that control general parameters of packages such as ncores argument.

  • par.fda.usc.R file is deleted: par.fda.usc is now an internal object created and modified by ops.fda.usc()

  • New function metric.DTW() computes distances between functional data using dynamic time warping (DTW) metric.WDTW() and metric.TWED() are extended version (not parallelized yet, pending to completed the Rd document)

  • New functions S.LPR() and S.LCR() for computing smoothing matrix S by nonparametric method.

  • The functions anova.hetero(), anova.onefactor(), anova.RPm(), influence.fdata(), influence.quan(), min.basis(), min.np() and unlist.fdata() are renamed fanova.hetero(), fanova.onefactor(), fanova.RPm(), influence.fregre.fd(), influence_quan(), optim.basis(), optim.np() and unlist_fdata()

  • optim.np() (deprecated min.np()) allows Local polynomial regression with correlated errors using the new parameter (correl=TRUE)

  • Kernel.correlated() new functions

  • New class: “ldata”:

  1. ldata() class definition.

  2. Redefined metric.ldata(), it computes distance for ldata object: list with m functional data mfdata() and univariate data included in a data frame called “df”

  3. New function metric.mfdata(): compute distance for mfdata class object: list with m functional data

  4. plot.ldata(): plots for ldata object, it allows drawing using a color bar.

  5. plot.mfdata(): plot formfdata object (internal function, pending to completed the Rd document)

  6. depth.modep(), depth.mode() call metric.lp() and metric.ldata() propperly

  7. New functions: subset.ldata(), is.lfdata(), [.lfdata(), [.ldata, is.ldata(), names.ldata() and c.ldata()

  • classic.tree() is replaced by the classic.rpart() (which requires the rpart library to be installed). The internal function classif.tree2boost() and the dependency of the rpart package are also removed

  • New functions and utilities in accuracy.r file.

  • New functions related with Machine Learning procedures (rdepend on packages not included in “fda.usc”):

  • Minor changes in classif.gkam() and fregre.gkam()

  • Settings in fregre.np(), fregre.np.cv() with type.S = S.KNN

  • New script file: FDA_REviewClasif_V2 classification example

  • Bug corrected in h.default() (specially using k-nearest neighbors smoothing, type.S=“S.KNN”)

  • “type.CV” and “par.CV()” arguments are removed in classif.np(), classif.kernel() and classif.knn()

  • dcor.xy.r/.Rd includes Rdnames

  • fdata2model.R shortcut to use in classif and fregre method

fda.usc 1.5.0

  • This version was released in Jan. 2019 to accompany Manuel Oviedo de la Fuente PhD Thesis, see Minerva (University of Santiago de Compostela) repository.

  • New function implemented: fregre.gsam.vs() accompany paper: Febrero-Bande, M., Gonz'{a}lez-Manteiga, W. and Oviedo de la Fuente, M. Variable selection in functional additive regression models, (2018).
    Computational Statistics, 1-19. DOI: 10.1007/s00180-018-0844-5

  • The current function fregre.basis.cv() returns an object called fregre.basis (same output as if the fregre.basis() function had been used) that uses the selected parameters according to the indicated criteria (see example below). The previous function version (up to version 1.5.0) has been renamed in the function “fregre.basis.cv.old”. It is marked as deprecated in the current version and will be deleted in the next version of the package, thanks to Beatriz Bueno.

  • New functions: plot.fregre.lm and summary.fregre.lm() solve errors in the summary of the in fregre.lm() function, thanks to Prof. Andros Kourtellos.

  • A bug in fregre.pc() has been fixed (thanks to Prof. Eduardo Garcia-Portugues).

fda.usc 1.4.0

  • This was published in December 2017 to accompany the document:

  • Ordonez, C., Oviedo de la Fuente, M., Roca-Pardinas, J., Rodriguez-Perez, J. R. (2017). Determining optimum wavelengths for leaf water content estimation from reflectance: A distance correlation approach. , (2018) 173,41-50 DOI: 10.1016/j.chemolab.2017.12.001.

  • New functions implemented: LMDC.select() and LMDC.regression().

  • Oviedo de la Fuente M, Febrero-Bande M, Muñoz MP, Domínguez À (2018) Predicting seasonal influenza transmission using functional regression models with temporal dependence. PLoS ONE 13(4): e0194250. DOI: 10.1371/journal.pone.0194250

    • The functions fregre.gls and fregre.igls implement the functional linear model with dependent errors (functional GLs and functional iterative GLS respectively).
    • The “fda.usc/inst/script/” folder contains the code that reproduces the results of the paper (see the scripts whose prefix is “PLOS”).

  • This package version also companion for the paper:

  • “Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes” Cuesta-Albertos et al., 2017). The package implements goodness-of-fit tests for the functional linear model with scalar response.

  • A bug in functional derivative by raw derivation (function fdata.deriv() with method=“diff”) has been fixed, thanks to Marcos Matabuena.

  • A bug in classif.knn() and predict.classif() has been fixed, thanks to Ricardo Recarey.

  • A bug in CV.S() function has been fixed, thanks to Miquel Carbajo.

  • A bug in anova.hetero() has been fixed, thanks to Beatriz Bueno.

fda.usc 1.3.0

  • Beta version functions to Fit Functional Linear Model Using Generalized Least Squares: fregre.gls(), fregre.igls(), GCCV.S(), predict.fregre.gls() and predict.fregre.igls(). Internal function “auxiliar”, “corSigma()”, “corStruct()”.

  • The functionality of the functions “+.fdata()”, “-.fdata()”, “*.fdata()” an “/.fdata” has been improved.

  • S3 functions for fdata class calculations: is.na.fdata() and anyNA.fdata. Function “count.na.fdata()” returns a vector with the number of “NA” of each curve.

  • Internal function “count.na” is deprecated.

  • fdata function converts “xtab” and “ftable” class object into “fdata” class object.

fda.usc 1.2.3

fda.usc 1.2.2

  • Warning message in fregre.basis.cv(), fregre.pc.cv() and fregre.pls.cv(), fregre.basis(), fregre.pc() and fregre.pls() functions when system is computationally singular.
  • A bug in predict.classif() function using a fitted object by classif.knn() has been fixed.
  • The argument “trim” is modified from 0.1 to 0.25 in the function quantile.outliers.trim
  • The internal distance functions: euclidean, manhattan, minkowski and maximum allow a vector of weights.
  • In classif.DD() function, the polynomial classifier (“DD1”, “DD2” and “DD3”) uses the original procedure proposed by Li et al. (2012), rotating the DD-plot (to exchange abscise and ordinate). The procedure extend to multi-class problems by incorporating the method of majority voting in the case of polynomial classifier and the method One vs the Rest in the logistic case (“glm” and “gam”).
  • The fregre.gkam() function only considers functional covariates (not implemented for non-functional covariates).
  • In the depth.FM() function it has been renamed the argument “dfunc” by “dfunc2”. subset.fdata() is a wrapper function of subset function.

fda.usc 1.2.1

  • The functions dcor.xy, dcor.test(), bcdcor.dist() and dcor.dist() (wrapper function of energy package) are added. fregre.gsam() function can be used without smoothed vairables.
  • A bug in “selec” argument of summary.fregre.gkam() has been fixed.
  • A bug in “h” argument of fregre.plm() has been fixed.
  • A bug in sigma=“vexponential” of rproc2fdata() has been fixed. The default values depth.RPp, depth.RP and rproc2fdata() have been modified.
  • A classif.DD() function uses the same bandwidth “h” for k groups in modal depth and same projections “proj” for k groups in RP depth.
  • A bug in predict.fregre.gsam() when PLS are previously estimated using norm=TRUE has been fixed.

fda.usc 1.2.0

New functions:

  • classif.DD(), fits Nonparametric Classification Procedure Based on DD-plot (depth-versus-depth plot) for G groups.
  • depth.FMp(), depth.modep and depth.RPp() functions provide the depth measure for a list of p–functional data objects.
  • metric.ldata(), computes distance for a list of p–functional data objects.
  • metric.hausdorff(), computes hausdorff distance.
  • Multivariate depth functions have been renamed: “depth.” to “mdepth.”.

fda.usc 1.1.0

New functions:

New dataset: Mithochondiral calcium overload (MCO) data set.

New utilities:

fda.usc 1.0.5

  • “pls” package dependency has been removed.
  • A bug in outlier.ltr() function has been fixed in the case of the rownames (of fdata) can not be converted to numeric values.
  • A bug in fregre.lm() function has been fixed in the case of one of the covariates is a factor and penalization argument is required (rn or lambda greater than zero).
  • The new argument “lambda” in fregre.lm() penalizes the derivative of second order of the functional data.
  • New arguments in predict.fregre.fd() and predict.fregre.lm() produce confidence or prediction intervals at the specified level mimicking predict.lm().
  • New argument “verbose” in min.basis(), min.np() and rproc2fdata().
  • The argument “mu” in rproc2fdata() allows vector and also fdata class object.

fda.usc 1.0.4

  • A bug in fregre.pls() function has been fixed in the case of the FPLS basis are created with the argument norm is TRUE (the curves are centred and scaled).
  • A bug in outliers.depth.trim() function has been fixed in the case of the procedure requires more than one iteration step.

fda.usc 1.0.3

  • This version introduces new function classif.depth() that fits a nonparametric classification procedure based on maximum depth measure.
  • Penalized FPC an FPLS basis are computed in create.pc.basis() and create.pls.basis() by the new arguments “lambda” and “P”.
  • A bug in predict.fregre.fd() function has been fixed (in the case of the “object” is fitted using funtional partial least square basis).

fda.usc 1.0.2

  • Release 1.0.2 introduces new functions:
  • New argument “se.fit” in predict.fregre.fd() and predict.fregre.lm() function.
  • A bug in CV.S() function when “y” argument is a fdata object has been fixed.
  • This “bug” has involved the min.np() function.
  • In metric.lp() function NA values are returned, if the fdata has NA’s values.
  • In metric.lp() function supremum distance is computed, if “lp” argument is 0.

fda.usc 1.0.1

New functions:

  • New depth functions and its corresponding shortcut functions (see help(Descriptive) form more details):

  • depth.SD() provides the simplicial depth measure for bivariate data.

  • depth.PD() provides the depth measure using random projections for multivariate data.

  • depth.MhD() provides the Mahalanobis depth measure for multivariate data.

  • depth.HD() provides the half-space depth measure for multivariate data.

  • It introduces a new functions for functional PC and PLS regression:

  • fregre.ppc, fregre.ppls, fregre.ppc.cv, fregre.ppls.cv, and the auxiliary functions: fdata2ppc, fdata2ppls, P.penalty.

  • The function rber.gold() has been renamed by rwild() function.

  • ow, rwild() contructs the Wild bootstrap residuals.

  • order.fdata() is a wrapper function of order function.

New arguments and options:

New arguments “wild” and “type.wild” in fregre.bootstrap(). In fregre.glm(), fregre.gsam(), classif.glm2boost(), classif.gsam2boost() the “fdataobj” argument allows a multivariate data or functional data. * fregre.lm() allows penalization by “rn” parameter (ridge regression). * fregre.pc() and fregre.basis() allow weighted least squares by “weights” argument.

fda.usc 1.0.0

fda.usc 0.9.8.1

Release 0.9.8.1 introduces new functions flm.Ftest() and dfv.test(). The first performs a functional F-test and the second implements the test of Delsol, Ferraty and Vieu (2010).

Function flm.test() now has a better computational performance and function Aijr() has been replaced by Adot().

New argument “lambda” in fdata2fd() function.

New argument “rn” in create.pc.basis() function.

fregre.kgam() has been renamed to fregre.gkam().

fda.usc 0.9.8

Release 0.9.8 introduces a new function flm.test() that allows to test for the Functional Linear Model with scalar response for a given dataset. Is based on the new functions PCvM.statistic(), Aijr() and rber.gold().

A bug in fregre.kgam() has been fixed.

fda.usc 0.9.7

fda.usc 0.9.6

fda.usc 0.9.5

Release 0.9.5 improves fdata.bootstrap() function (better computational efficiency). It introduces a new functions: for Partial Linear Square (pls.fdata(), fregre.pls() and fregre.pls.cv()) and Simpson integration (int.simpson() and int.simpson2()). It modifies the functions metric.lp(), inprod.fdata(), summary.fregre.fd() and predict.fregre.fd().

fda.usc 0.9.4

Release 0.9.4 added 3 script files: Outliers_fdata.R, flm_beta_estimation_brownian_data.R and Classif_phoneme.R. It has introduced the functions fregre.glm() and predict.fregre.glm() which allow fit and predict respectively Functional Generalized Linear Models. It has introduced the functions create.pc.basis and create.fdata.basis() which allow to create basis objects for functional data of class “fdata”.

fda.usc 0.9

Release 0.9 introduces a new function h.default() that simplifies the calculation of the bandwidth parameter “h” in the functions: fregre.np(), fregre.np.cv() and fregre.plm().
In most of the functions has added a stop control when the dataset has missing data (NA’s). It adds the attribute “call” to the distance matrix calculated in metric.lp(), semimetric.basis() and semimetric.NPFDA() functions.