The pls Package: Principal Component and Partial Least Squares Regression in R

The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General Public License (GPL). The user interface is modelled after the traditional formula interface, as exemplified by lm. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a exible crossvalidation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.

In the best interest of the child? Contradictions and tensions in social work

Author's accepted version (postprint)

Ilandføring av olje - en mulighetsanalyse. En studie av Johan Castberg

Masteroppgave i energy management - Universitetet i Nordland, 201

Does increasing the size of bi-weekly samples of records influence results when using the Global Trigger Tool? An observational study of retrospective record reviews of two different sample sizes

Source at http://dx.doi.org/10.1136/bmjopen-2015-010700.Objectives - To investigate the impact of increasing sample of records reviewed bi-weekly with the Global Trigger Tool method to identify adverse events in hospitalised patients. Design - Retrospective observational study. Setting - A Norwegian 524-bed general hospital trust. Participants - 1920 medical records selected from 1 January to 31 December 2010. Primary outcomes - Rate, type and severity of adverse events identified in two different samples sizes of records selected as 10 and 70 records, bi-weekly. Results - In the large sample, 1.45 (95% CI 1.07 to 1.97) times more adverse events per 1000 patient days (39.3 adverse events/1000 patient days) were identified than in the small sample (27.2 adverse events/1000 patient days). Hospital-acquired infections were the most common category of adverse events in both the samples, and the distributions of the other categories of adverse events did not differ significantly between the samples. The distribution of severity level of adverse events did not differ between the samples. Conclusions - The findings suggest that while the distribution of categories and severity are not dependent on the sample size, the rate of adverse events is. Further studies are needed to conclude if the optimal sample size may need to be adjusted based on the hospital size in order to detect a more accurate rate of adverse events

A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis

Prediction problems from spectra are largely encountered in chemometry. In addition to accurate predictions, it is often needed to extract information about which wavelengths in the spectra contribute in an effective way to the quality of the prediction. This implies to select wavelengths (or wavelength intervals), a problem associated to variable selection. In this paper, it is shown how this problem may be tackled in the specific case of smooth (for example infrared) spectra. The functional character of the spectra (their smoothness) is taken into account through a functional variable projection procedure. Contrarily to standard approaches, the projection is performed on a basis that is driven by the spectra themselves, in order to best fit their characteristics. The methodology is illustrated by two examples of functional projection, using Independent Component Analysis and functional variable clustering, respectively. The performances on two standard infrared spectra benchmarks are illustrated.Comment: A paraitr

Lime-cement stabilisation of Trondheim clays and its impact on carbon dioxide emissions

publishedVersio

Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy

The aim of this study was to develop robust chemometric models for the routine determination of dietary constituents of quinoa (Chenopodium quinoa Willd.) using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa grains of 77 cultivars were acquired while dietary constituents were determined by reference methods. Spectra were subjected to multiplicative scatter correction (MSC) or extended multiplicative signal correction (EMSC), and were (or not) treated by Savitzky-Golay (SG) filters. Latent variables were extracted by partial least squares regression (PLSR) or canonical powered partial least squares (CPPLS) algorithms, and the accuracy and predictability of all modelling strategies were compared. Smoothing the spectra improved the accuracy of the models for fat (root mean square error of cross-validation, RMSECV: 0.3190.327%), ashes (RMSECV: 0.2240.230%), and particularly for protein (RMSECV: 0.5180.564%) and carbohydrates (RMSECV: 0.5420.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.2480.335%) and ashes (RMSEP: 0.1370.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.3760.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6510.901]), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6500.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.4780.654]) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.6580.833]).Mr. Encina-Zelada acknowledges the financial aid provided by the Peruvian National Programme of Scholarships and Student Loans (PRONABEC) in the mode of PhD grants (Presidente de La República Grant Number 183308). Dr. Gonzales-Barron wishes to acknowledge the financial support provided by the Portuguese Foundation for Science and Technology (FCT) through the award of a five-year Investigator Fellowship (IF) in the mode of Development Grants (IF/00570)