scholarly journals Systematic Differences in Bucket Sea Surface Temperature Measurements among Nations Identified Using a Linear-Mixed-Effect Method

2019 ◽  
Vol 32 (9) ◽  
pp. 2569-2589 ◽  
Author(s):  
Duo Chan ◽  
Peter Huybers
Keyword(s):  
Sea Surface ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Noise Variance ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Engine Room ◽  
Effect Model ◽  
Six Nations ◽  
Effect Method

AbstractThe International Comprehensive Ocean–Atmosphere Dataset (ICOADS) is a cornerstone for estimating changes in sea surface temperatures (SST) over the instrumental era. Interest in determining SST changes to within 0.1°C makes detecting systematic offsets within ICOADS important. Previous studies have corrected for offsets among engine room intake, buoy, and wooden and canvas bucket measurements, as well as noted discrepancies among various other groupings of data. In this study, a systematic examination of differences in collocated bucket SST measurements from ICOADS3.0 is undertaken using a linear-mixed-effect model according to nations and more-resolved groupings. Six nations and a grouping for which nation metadata are missing, referred to as “deck 156,” together contribute 91% of all bucket measurements and have systematic offsets among one another of as much as 0.22°C. Measurements from the Netherlands and deck 156 are colder than the global average by −0.10° and −0.13°C, respectively, both at p < 0.01, whereas Russian measurements are offset warm by 0.10°C at p < 0.1. Furthermore, of the 31 nations whose measurements are present in more than one grouping of data (i.e., deck), 14 contain decks that show significant offsets at p < 0.1, including all major collecting nations. Results are found to be robust to assumptions regarding the independence and distribution of errors as well as to influences from the diurnal cycle and spatially heterogeneous noise variance. Correction for systematic offsets among these groupings should improve the accuracy of estimated SSTs and their trends.

scholarly journals Prediction of the herd somatic cell count of the following month using a linear mixed effect model

2010 ◽  
Vol 93 (1) ◽  
pp. 234-241 ◽  
Author(s):  
J.J. Lievaart ◽  
H.W. Barkema ◽  
J. van den Broek ◽  
J.A.P. Heesterbeek ◽  
W.D.J. Kremer
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Effect Model

scholarly journals Nintedanib in idiopathic and secondary pleuroparenchymal fibroelastosis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mouhamad Nasser ◽  
Salim Si-Mohamed ◽  
Ségolène Turquier ◽  
Julie Traclet ◽  
Kaïs Ahmad ◽  
...  
Keyword(s):  
Drug Therapy ◽  
Lung Function ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Mixed Effect ◽  
Lung Volumes ◽  
Pleuroparenchymal Fibroelastosis ◽  
Linear Mixed Effect ◽  
Effect Model ◽  
Relative Decline

Abstract Background Pleuroparenchymal fibroelastosis (PPFE) has a variable disease course with dismal prognosis in the majority of patients with no validated drug therapy. This study is to evaluate the effect of nintedanib in patients with idiopathic and secondary PPFE. Patients admitted to a tertiary care center (2010–2019) were included into this retrospective analysis if they had a multidisciplinary diagnosis of PPFE, had been followed-up for 3 months or more, and had lung function tests and chest CTs available for review. Changes in pulmonary function tests were assessed using non-parametric tests and linear mixed effect model. Lung volumes were measured with lobar segmentation using chest CT. Results Out of 21 patients with PPFE, nine had received nintedanib, six had received another treatment and another six patients were monitored without drug therapy. Annual FVC (% of predicted) relative decline was − 13.6 ± 13.4%/year before nintedanib and − 1.6 ± 6.02%/year during nintedanib treatment (p = 0.014), whereas no significant change in FVC% relative decline was found in patients receiving another treatment (− 13.25 ± 34 before vs − 16.61 ± 36.2%/year during treatment; p = 0.343). Using linear mixed effect model, the slope in FVC was − 0.97%/month (95% CI: − 1.42; − 0.52) before treatment and − 0.50%/month (95% CI: − 0.88; 0.13) on nintedanib, with a difference between groups of + 0.47%/month (95% CI: 0.16; 0.78), p = 0.004. The decline in the upper lung volumes measured by CT was − 233 mL/year ± 387 mL/year before nintedanib and − 149 mL/year ± 173 mL/year on nintedanib (p = 0.327). Nintedanib tolerability was unremarkable. Conclusion In patients with PPFE, nintedanib treatment might be associated with slower decline in lung function, paving the way for prospective, controlled studies.

Using the Linear Mixed-Effect Model Framework to Estimate Generalizability Variance Components in R

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 133-142 ◽  
Author(s):  
Zhehan Jiang
Keyword(s):  
Variance Components ◽  
Classical Test Theory ◽  
Test Theory ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Model Framework ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Mixed Effect Models ◽  
Effect Model

Abstract. Extending from classical test theory, G theory allows more sources of variations to be investigated and therefore provides the accuracy of generalizing observed scores to a broader universe. However, G theory has been used less due to the absence of analytic facilities for this purpose in popular statistical software packages. Besides, there is rarely a systematic G theory introduction in the linear mixed-effect model context, which is a widely taught technique in statistical analysis curricula. The present paper fits G theory into linear mixed-effect models and estimates the variance components via the well-known lme4 package in R. Concrete examples, modeling procedures, and R syntax are illustrated so that practitioners may use G theory for their studies. Realizing the G theory estimation in R provides more flexible features than other platforms, such that users need not rely on specialized software such as GENOVA and urGENOVA.

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