scholarly journals 2PL Model: Compare Generalized Linear Mixed Model with Latent Variable Model based IRT framework

2021 ◽  
Author(s):  
Jihong Zhang ◽  
Terry Ackerman ◽  
Yurou Wang
Keyword(s):  
Latent Variable ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
R Package ◽  
Estimation Accuracy ◽  
Variable Model ◽  
Large Scale Assessment ◽  
Measurement Models ◽  
Lower Accuracy

Fitting item response theory (IRT) models using the generalized mixed logistic regression model (GLMM) has become more popular in large-scale assessment because GLMM allows combining complicated multilevel structures (i.e., students are nested in classrooms which are nested in schools) with IRT measurement models. However, the estimation accuracy of item parameters between these two models is not well examined. This study aimed to compare the estimation results of the GLMM based 2PL model (using the PLmixed R package) with the traditional IRT model (using flexMIRT software) under different sample sizes (N= 500, 1000, 5000) and test length (J = 15, 21) conditions. The simulation results showed that for both the GLMM-based method and the traditional method, item threshold estimates had lower bias than item discrimination parameters. We also found that according to the simulation study, GLMM estimates via PLmixed had lower accuracy than traditional IRT modeling via flexMIRT for items with high discrimination.

scholarly journals Exome-wide association studies in general and long-lived populations identify genetic variants related to human age

2020 ◽  
Author(s):  
Patrick Sin-Chan ◽  
Nehal Gosalia ◽  
Chuan Gao ◽  
Cristopher V. Van Hout ◽  
Bin Ye ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Model Systems ◽  
P Value ◽  
Ashkenazi Jews ◽  
Association Analyses ◽  
Age Related

SUMMARYAging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on 58,470 participants of the DiscovEHR cohort. Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40×10−10) and ZYG11A rs74227999 (p = 2.50×10−08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions. To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males ≥ 92 years, Females ≥ 95years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants. Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.

scholarly journals Using linear mixed model and dummy variable  model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 101-115 ◽  
Author(s):  
L.Y. Fu ◽  
W.S. Zeng ◽  
S.Z. Tang ◽  
R.P. Sharma ◽  
H.K. Li
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Southern China ◽  
Forest Biomass ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Models

The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.  

scholarly journals A Bayesian linear mixed model for prediction of complex traits

2020 ◽  
Author(s):  
Yang Hai ◽  
Yalu Wen
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Rare Variants ◽  
Disease Risk ◽  
R Package ◽  
Underlying Disease ◽  
Supplementary Information ◽  
True Effect Size ◽  
Bayes Algorithm

Abstract Motivation Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. Results We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. Availability The R-package is available at https://github.com/yhai943/BLMM Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Early Growth Response of Nine Timber Species to Release in a Tropical Mountain Forest of Southern Ecuador

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 254 ◽  
Author(s):  
Omar Cabrera ◽  
Andreas Fries ◽  
Patrick Hildebrandt ◽  
Sven Günter ◽  
Reinhard Mosandl
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Growth Habit ◽  
R Package ◽  
Mountain Forest ◽  
Timber Species ◽  
Older Individuals ◽  
Humid Climate ◽  
Climate Conditions ◽  
Shade Tolerant Species

Research Highlights: This study determined that treatment “release from competitors” causes different reactions in selected timber species respective to diametrical growth, in which the initial size of the tree (diametric class) is important. Also, the growth habit and phenological traits (defoliation) of the species must be considered, which may have an influence on growth after release. Background and Objectives: The objective of the study was to analyze the diametric growth of nine timber species after their release to answer the following questions: (i) Can the diametric growth of the selected timber species be increased by release? (ii) Does the release cause different responses among the tree species? (iii) Are other factors important, such as the initial diameter at breast height (DBH) or the general climate conditions? Materials and Methods: Four-hundred and eighty-eight trees belonging to nine timber species were selected and monitored over a three-year period. Release was applied to 197 trees, whereas 251 trees served as control trees to evaluate the response of diametrical growth. To determine the response of the trees, a linear mixed model (GLMM, R package: LMER4) was used, which was adjusted by a one-way ANOVA test. Results: All species showed a similar annual cycle respective to diametric increases, which is due to the per-humid climate in the area. Precipitation is secondary for the diametric growth because sufficient rainfall occurs throughout year. What is more important, however, are variations in temperature. However, the species responded differently to release. This is because the initial DBH and growth habit are more important factors. Therefore, the species could be classified into three specific groups: Positive, negative and no response to release. Conclusions: Species which prefer open sites responded positively to release, while shade tolerant species and species with pronounced phenological traits responded negatively. The initial DBH was also an important factor for diametric increases. This is because trees of class I (20 cm to 30 cm DBH) responded positively to the treatment, whereas for bigger or older individuals, the differences decreased or became negative.

scholarly journals Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

2017 ◽  
Author(s):  
Wei Zhou ◽  
Jonas B. Nielsen ◽  
Lars G. Fritsche ◽  
Rounak Dey ◽  
Maiken E. Gabrielsen ◽  
...  
Keyword(s):  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Case Control ◽  
Error Rates ◽  
European Ancestry ◽  
Computational Time ◽  
Type I ◽  
Genome Wide Association Studies

AbstractIn genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly – producing large type I error rates – in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

scholarly journals Influence of environmental variability on harbour porpoise movement

2020 ◽  
Vol 648 ◽  
pp. 207-219 ◽  
Author(s):  
D Stalder ◽  
FM van Beest ◽  
S Sveegaard ◽  
R Dietz ◽  
J Teilmann ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Marine Mammals ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Environmental Variability ◽  
Conservation Status ◽  
Movement Patterns ◽  
Environmental Parameters ◽  
Harbour Porpoise

The harbour porpoise Phocoena phocoena is a small marine predator with a high conservation status in Europe and the USA. To protect the species effectively, it is crucial to understand its movement patterns and how the distribution of intensively used foraging areas can be predicted from environmental conditions. Here, we investigated the influence of both static and dynamic environmental conditions on large-scale harbour porpoise movements in the North Sea. We used long-term movement data from 57 individuals tracked during 1999-2017 in a state-space model to estimate the underlying behavioural states, i.e. whether animals used area-restricted or directed movements. Subsequently, we assessed whether the probability of using area-restricted movements was related to environmental conditions using a generalized linear mixed model. Harbour porpoises were more likely to use area-restricted movements in areas with low salinity levels, relatively high chlorophyll a concentrations and low current velocity, and in areas with steep bottom slopes, suggesting that such areas are important foraging grounds for porpoises. Our study identifies environmental parameters of relevance for predicting harbour porpoise foraging hot spots over space and time in a dynamic system. The study illustrates how movement patterns and data on environmental conditions can be combined, which is valuable to the conservation of marine mammals.

scholarly journals A Joint Learning Approach to Intelligent Job Interview Assessment

2018 ◽  
Author(s):  
Dazhong Shen ◽  
Hengshu Zhu ◽  
Chen Zhu ◽  
Tong Xu ◽  
Chao Ma ◽  
...  
Keyword(s):  
Real World ◽  
Latent Variable ◽  
Large Scale ◽  
Real World Data ◽  
Job Interviews ◽  
Variable Model ◽  
Job Interview ◽  
Joint Learning ◽  
Interview Process ◽  
The Right

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this paper, we propose a novel approach to intelligent job interview assessment by learning the large-scale real-world interview data. Specifically, we develop a latent variable model named Joint Learning Model on Interview Assessment (JLMIA) to jointly model job description, candidate resume and interview assessment. JLMIA can effectively learn the representative perspectives of different job interview processes from the successful job application records in history. Therefore, a variety of applications in job interviews can be enabled, such as person-job fit and interview question recommendation. Extensive experiments conducted on real-world data clearly validate the effectiveness of JLMIA, which can lead to substantially less bias in job interviews and provide a valuable understanding of job interview assessment.

scholarly journals lsasim: an R package for simulating large-scale assessment data

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Tyler H. Matta ◽  
Leslie Rutkowski ◽  
David Rutkowski ◽  
Yuan-Ling Liaw
Keyword(s):  
Large Scale ◽  
R Package ◽  
Assessment Data ◽  
Large Scale Assessment ◽  
Scale Assessment
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