Principal components analysis and partial least squares regression

Dunn, William J.; Scott, Donald R.; Glen, W. Graham

doi:10.1016/0898-5529(89)90004-3

Cited by 106 publications

(25 citation statements)

References 6 publications

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“…Integrated with the analytical functions of GIS, logistic regression can be used to evaluate the impact of these factors on urban growth. However, logistic regression cannot eliminate the correlation of spatial variables, while PCA can eliminate spatial correlation only to a certain degree, and the principal components found in the independent variables may not adequately explain the dependent variables [35]. The proposed PLS method can extract variables that are uncorrelated from amongst the explanatory variables, and also between the explanatory and response factors [36,37], resulting in the discovery of transition rules from a number of driving factors that are usually highly correlated.…”

Section: Discussionmentioning

confidence: 99%

“…PCA was used to reduce the effect of multicollinearity among spatial variables and obtain more reasonable CA parameters [34], yielding an improvement in performance when compared to the logistic-CA model. Statisticians have pointed out that the PCA method produces principal components that reflect only the covariance structure between the independent variables [35], and, as a consequence, the extracted components may only weakly explain the variance of the independent variable corresponding to the dependent variable in the regression.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Feng

Liu

Chen

et al. 2016

IJGI

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Abstract:We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Feng

Liu

Chen

et al. 2016

IJGI

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“…PCA and OPLS‐DA were used to describe variations in the data with minimum latent variables that were demonstrated with SIMCA‐P + (CA,USA). This facilitated grouping or classification of the fingerprints and detected outliers .…”

Section: Methodsmentioning

confidence: 99%

Chemical fingerprint of Ganmaoling granule by double-wavelength ultra high performance liquid chromatography and ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry

Lou

Yingyi

et al. 2015

J. Sep. Science

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A method incorporating double-wavelength ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry was developed for the investigation of the chemical fingerprint of Ganmaoling granule. The chromatographic separations were performed on an ACQUITY UPLC HSS C18 column (2.1 × 50 mm, 1.8 μm) at 30°C using gradient elution with water/formic acid (1%) and acetonitrile at a flow rate of 0.4 mL/min. A total of 11 chemical constituents of Ganmaoling granule were identified from their molecular weight, UV spectra, tandem mass spectrometry data, and retention behavior by comparing the results with those of the reference standards or literature. And 25 peaks were selected as the common peaks for fingerprint analysis to evaluate the similarities among 25 batches of Ganmaoling granule. The results of principal component analysis and orthogonal projection to latent structures discriminant analysis showed that the important chemical markers that could distinguish the different batches were revealed as 4,5-di-O-caffeoylquinic acid, 3,5-di-O-caffeoylquinic acid, and 4-O-caffeoylquinic acid. This is the first report of the ultra high performance liquid chromatography chemical fingerprint and component identification of Ganmaoling granule, which could lay a foundation for further studies of Ganmaoling granule.

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“…In this case, the use of principal component regression (PCR) might be considered an appropriate choice. PCR is an extension of principal component analysis (PCA; see Dunn Iii, Scott, & Glen, 1989), in which correlated variables are grouped into sets of uncorrelated variables known as the principal components. In PCR, the same techniques that are applied in PCA are used to project predictors into its principal components, and then use this reduced dimensionality (the components) in the regression of the response variable.…”

Section: Usefulness Of Usefulness Of Usefulness Of Usefulness Of Plsrmentioning

confidence: 99%

Partial least squares regression in the social sciences

Sawatsky¹,

Clyde²,

Meek³

2015

TQMP

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Abstract Abstract Partial least square regression (PLSR) is a statistical modeling technique that extracts latent factors to explain both predictor and response variation. PLSR is particularly useful as a data exploration technique because it is highly flexible (e.g., there are few assumptions, variables can be highly collinear). While gaining importance across a diverse number of fields, its application in the social sciences has been limited. Here, we provide a brief introduction to PLSR, directed towards a novice audience with limited exposure to the technique; demonstrate its utility as an alternative to more classic approaches (multiple linear regression, principal component regression); and apply the technique to a hypothetical dataset using JMP statistical software (with references to SAS software).

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Principal components analysis and partial least squares regression

Cited by 106 publications

References 6 publications

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Chemical fingerprint of Ganmaoling granule by double-wavelength ultra high performance liquid chromatography and ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry

Partial least squares regression in the social sciences

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