Statistical Computing in Functional Data Analysis: The<i>R</i>Package<b>fda.usc</b>

Febrero–Bande, Manuel; Fuente, Manuel Oviedo de la

doi:10.18637/jss.v051.i04

Cited by 297 publications

(165 citation statements)

References 15 publications

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“…The functional models (Models 5, 6 and 7) were estimated with OLS and implemented in the R-package fda.usc [11] with 21 basis. First, models were fitted to the entire data set to evaluate goodness-of-fit to the training data and were then implemented through the cross-validation described in the next section.…”

Section: Methodsmentioning

confidence: 99%

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data

et al. 2017

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BackgroundModern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars.ResultsThis study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT’s global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined.ConclusionsWe found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.

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Section: Methodsmentioning

confidence: 99%

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data

et al. 2017

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“…All the algorithms for computations and analyses were implemented in R statistical programming language [25] using the R packages MASS and fda.usc [26].…”

Section: Functional Pca and Functional Pls Regressionmentioning

confidence: 99%

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

Zhu¹

2017

AJAC

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In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.

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“…The parameter β is in the infinitely dimensional space of ℓ 2 functions (the Hilbert space of all square integral functions over a particular interval) [19].…”

Section: Functional Regression Modelmentioning

confidence: 99%

“…When a function belongs to ℓ 2 space, it can be represented by a basis of known functions { } k k φ ∈» [19]. B-spline is one such basis representation used to calculate the functional regression between a functional predictor (spectral reflectance) X(t) and the scalar response.…”

Section: Smoothing By Basis Representationmentioning

confidence: 99%

Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset

Jha¹,

Bajorski²,

Fokoué³

et al. 2017

OJS

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Viticulturists traditionally have a keen interest in studying the relationship between the biochemistry of grapevines' leaves/petioles and their associated spectral reflectance in order to understand the fruit ripening rate, water status, nutrient levels, and disease risk. In this paper, we implement imaging spectroscopy (hyperspectral) reflectance data, for the reflective 330 -2510 nm wavelength region (986 total spectral bands), to assess vineyard nutrient status; this constitutes a high dimensional dataset with a covariance matrix that is ill-conditioned. The identification of the variables (wavelength bands) that contribute useful information for nutrient assessment and prediction, plays a pivotal role in multivariate statistical modeling. In recent years, researchers have successfully developed many continuous, nearly unbiased, sparse and accurate variable selection methods to overcome this problem. This paper compares four regularized and one functional regression methods: Elastic Net, Multi-Step Adaptive Elastic Net, Minimax Concave Penalty, iterative Sure Independence Screening, and Functional Data Analysis for wavelength variable selection. Thereafter, the predictive performance of these regularized sparse models is enhanced using the stepwise regression. This comparative study of regression methods using a high-dimensional and highly correlated grapevine hyperspectral dataset revealed that the performance of Elastic Net for variable selection yields the best predictive ability.

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Statistical Computing in Functional Data Analysis: TheRPackagefda.usc

Cited by 297 publications

References 15 publications

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset

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