• ETRI Journal
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• 제32권5호
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• pp.687-694
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• 2010

Breaks in presence (BIP) are those moments during virtual environment (VE) exposure in which participants become aware of their real world setting and their sense of presence in the VE becomes disrupted. In this study, we investigate participants' experience when they encounter technical anomalies during game play. We induced four technical anomalies and compared the BIP responses of a navigation mode game to that of a combat mode game. In our analysis, we applied a linear mixed model (LMM) and compared the results with those of a conventional regression model. Results indicate that participants felt varied levels of impact and recovery when experiencing the various technical anomalies. The impact of BIPs was clearly affected by the game mode, whereas recovery appears to be independent of game mode. The results obtained using the LMM did not differ significantly from those obtained using the general regression model; however, it was shown that treatment effects could be improved by consideration of random effects in the regression model.

• 한국수자원학회논문집
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• 제56권8호
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• pp.509-520
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• 2023

가뭄은 강수량, 토양수분 그리고 유출량 등 여러 가지 요인으로부터 영향을 받으며, 가뭄의 상황 판단을 위하여 다양한 가뭄지수가 널리 활용되고 있다. 가뭄지수의 산정에 활용되는 수문기상학적 자료와 가뭄지수 산정공식에 따라서 지수값은 달라지며, 가뭄 상황에 대한 판단에도 차이가 발생 할 수 있다. 본 연구에서는 국내외에서 널리 활용되는 표준강수지수(SPI)의 산정과정에서 결정해야 하는 강수량의 자료길이, 누적기간, 확률분포 모형, 매개변수 추정기법 등을 불확실성 영향 요인으로 가정하고, 각각의 조합에 대한 불확실성을 평균제곱근오차와 선형혼합모형(LMM)을 활용하여 평가하였다. 평균제곱오차는 SPI 산정과정에 발생되는 전반적인 오차를 추정하며, LMM은 영향 요인들의 상대적인 불확설성을 평가하는데 활용되었다. 그 결과, SPI 산정에 활용된 자료의 기간과 누적기간이 길어질수록 평균제곱오차가 감소하였다. LMM을 통하여 불확실성 영향요인들의 기여도를 비교한 결과, SPI의 불확실성에는 자료기간의 영향이 가장 크게 나타났다. 또한, 자료기간이 증가하면, 자료기간에 의한 불확실성은 감소하고 누적기간과 매개변수 추정기법에 의한 불확실성이 상대적으로 증가하였다. 본 연구 결과, SPI 산정과정에서 발생되는 불확실성을 줄이기 위해서는 장기간의 자료 확보가 우선이며, 자료의 특성을 적절히 반영하는 확률분포모형과 매개변수 추정기법이 적용되어야 한다.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1211-1218
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• 2008

Maximum likelihood estimator (MLE) and restricted maximum likelihood estimator (REMLE) approaches are available in analyzing the linear mixed model (LMM) like bioequivalence trials. US FDA (2001) guides that REMLE may be useful to assess bioequivalence (BE) test. This paper studies the statistical behaviors of the methods in assessing BE tests when some of observations are missing at random. The simulation results show that the REMLE maintains the given nominal level well and the MLE gives a bit higher power. Considering the levels and the powers, the REMLE approach is recommended when the sample sizes are small to moderate and the MLE approach should be used when the sample size is greater than 30.

• Genomics & Informatics
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• 제20권3호
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• 한국의류학회지
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• 제44권1호
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• pp.84-95
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• 2020

Wearing ease is a critical factor when designing special uniforms such as flight pilot's garment and should reflect occupational properties for better performance. This study measured skin surface on 31 areas in seven postures that refer to the pilot's occupational postures as well as made six prediction models including linear mixed model (LMM) for each body part to find the best fit model. Skin surface measured from 3D body scanned images of 11 male pilot participants. There were significantly positive and negative changes in various areas from standing posture (P1) to dynamic postures (P2-P7). Six models were designed in various compositions using stature and chest circumference as fixed effects and subject and posture as random effects. The best models were linear mixed models with one fixed effect (chest circumference or stature, varies with body parts) and two random effects (subject and posture). The results of this study provide reference data to set wearing ease for pilot's garment and suggests a new methodology in this research area, but verifying the effect of diverse independent variables is left for future studies.

• Journal of Forest and Environmental Science
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• 제32권3호
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• pp.280-290
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• 2016

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

• 한국환경생태학회지
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• 제38권4호
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• pp.426-433
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• 2024

본 연구에서는 식물에 의한 표면적 증가와 생리작용이 미세먼지 정화에 미치는 영향을 추정하기 위하여 대조구(Control; Type C)을 설정하고, 관엽식물(Spathiphyllum wallisii; Type P)과 인조식물(Artificial Plant; Shape of Spathiphyllum wallisii; Type A.P)을 활용하여 미세먼지 정화소요시간을 측정하고 비교·분석하였다. 그 결과, 각 실험구별 미세먼지 정화에 소요된 시간은 Type C에 비하여 Type A.P는 57~64%, Type P는 31~32% 수준으로 감소하였다. 이후, LMM(Liner Mix Model)을 활용하여 각 실험구별 시간변화에 따른 교호작용을 검정한 결과, 표면적증가와 시간변화(PM10 : t=3.123, p<0.05, PM2.5 : t=3.180, p<0.05), 생리작용과 시간변화(PM10 : t=4.065, p<0.05, PM2.5 : t=4.307, p<0.05)는 통계적으로 유의한 것으로 분석되어 각 요인과 시간변수의 교호작용이 있음을 확인할 수 있었다. 마지막으로 식물의 미세먼지 정화요인에 따른 효율은, 정화요인이 존재하지 않는 대조구(Type C)에 비하여 표면적 증가로 1.40배, 생리작용으로 1.95배, 총 평균 2.74배의 정화시간이 더 짧은 것으로 비선형회귀분석을 통해 추정하였다. 이상의 결과를 종합하여 식물체의 미세먼지 정화매커니즘 중 생리작용(방출 및 흡수 등)이 표면적 증가(흡착)보다 더 큰 영향을 미치고 있음을 예상하였으며, 이에 따라 미세먼지 정화 기능을 목적으로 하는 녹지에서 비배 및 관수관리등 녹지관리가 중요한 요인임을 피력하였다.