scholarly journals Generalized Linear Mixed Model Analysis of Acute Respiratory Infection Data on Children

2020 ◽  
pp. 110-115
Author(s):  
Tiyas Yulita ◽  
Tika Widayanti
Keyword(s):  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Combination Method ◽  
Predictor Variables ◽  
Minimum Bias ◽  
Response Variable ◽  
Shrinkage Coefficient

Statistical modeling often involves data which has a distribution of the exponential family. Generalized Linear Model (GLM) was developed to model these data by using a link function between the mean of the response variable and the linear form of the predictor variable. If the data of the response variable comes from several census blocks that are taken randomly, then the diversity between census blocks should not be ignored because it can increase bias. The Generalized Linear Mixed Model (GLMM) is a method that can capture a variety of random effects. However, it does not rule out if there are many predictor variables involved in the model and we use GLMMLasso as a combination method of GLMM and Lasso to shrink the parameter coefficients to zero, it is used to reduce the variance. In this study, a simulation was conducted to GLMMLasso use different numbers of predictor variables and different values of shrinkage coefficients to determine which shrinkage coefficient values have a minimum bias on parameter prediction. Acute Respiratory Infection (API) data on children in Jakarta is used to know the factors that could cause increased cases. The simulation result is the shrinkage coefficient which produces a minimum bias is 30, and the R2 value of data analysis on the model is 99.24%

scholarly journals A generalized linear mixed model for understanding determinant factors of student's interest in pursuing bachelor's degree at Universitas Syiah Kuala

2021 ◽  
Vol 21 (2) ◽  
pp. 72-80
Author(s):  
ASEP RUSYANA ◽  
KHAIRIL ANWAR NOTODIPUTRO ◽  
BAGUS SARTONO
Keyword(s):  
High Schools ◽  
Normal Distribution ◽  
Random Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Likelihood Method ◽  
Response Variable ◽  
Banda Aceh

Generalized Linear Mixed Model (GLMM) is a framework that has a response variable, fixed effects, and random effects. The response variable comes from an exponential family, whereas random effects have a normal distribution. Estimating parameters can be calculated using the maximum likelihood method using the Laplace approach or the Gauss-Hermite Quadrature (GHQ) approach. The purpose of this study was to identify factors that trigger student's interest to continue studying at Universitas Syiah Kuala (USK) using both techniques.  The GLMM is suitable for the data because the variable response has a Bernoulli distribution, and the random effects are assumed to be having a normal distribution. Also, the model helps identify the relationship between the dependent variable and the predictors. This study utilizes data from six high schools in Banda Aceh city drawn using a two-stage sampling technique. Stage 1, we randomly chose six out of sixteen public senior high schools in Banda Aceh. Stage 2, we selected students from each school from four different major classes. The GLMM model includes one binary response variable, five numerical fixed-effects, and two random effects. The response variable is the interest of high school students to continue study at USK (yes or no). The five fixed effects in the model including scores of collaboration (C), Action (A), Emotion (E), Purposes (P), and Hope (H).  Finally, the random effects are schools (S) and majors (M). In this study, both Laplace and GHQ techniques produce identical results. The predictors that can explain student interest are A, E, and H. These predictors have a positive effect. The random effects of schools and majors are not significantly different from zero. The model with three significant predictors is better than the complete predictor model.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

scholarly journals Reducing Aggression by Developing Emotional and Inhibitory Control

2021 ◽  
Vol 18 (10) ◽  
pp. 5263
Author(s):  
Miriam Romero-López ◽  
María Carmen Pichardo ◽  
Ana Justicia-Arráez ◽  
Judit Bembibre-Serrano
Keyword(s):  
Psychosocial Development ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Emotional Control ◽  
Control Group ◽  
Post Intervention ◽  
Mixed Model Analysis ◽  
Intervention Measures ◽  
Experimental Group

The objective of this study is to measure the effectiveness of a program on improving inhibitory and emotional control among children. In addition, it is assessed whether the improvement of these skills has an effect on the reduction of aggressive behavior in pre-school children. The participants were 100 children, 50 belonging to the control group and 50 to the experimental group, aged between 5 and 6 years. Pre-intervention and post-intervention measures of inhibitory and emotional control (BRIEF-P) and aggression (BASC) were taken. A Generalized Linear Mixed Model analysis (GLMM) was performed and found that children in the experimental group scored higher on inhibitory and emotional control compared to their peers in the control group. In addition, these improvements have an effect on the decrease in aggressiveness. In conclusion, preventive research should have among its priorities the design of such program given their implications for psychosocial development.

scholarly journals Changes in Dairy Cow Behavior with and without Assistance at Calving

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 722
Author(s):  
Bethan Cavendish ◽  
John McDonagh ◽  
Georgios Tzimiropoulos ◽  
Kimberley R. Slinger ◽  
Zoë J. Huggett ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Video Surveillance ◽  
Dairy Cow ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Behavioral Data ◽  
Behavioral Patterns ◽  
Holstein Friesian ◽  
Time Periods

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition.

scholarly journals Analyzing Child Malnutrition in Bangladesh: Generalized Linear Mixed Model Approach

2016 ◽  
Vol 64 (2) ◽  
pp. 163-167
Author(s):  
Tahmidul Islam ◽  
Md Golam Rabbani ◽  
Wasimul Bari
Keyword(s):  
Mixed Model ◽  
Sampling Design ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Child Malnutrition ◽  
Demographic Health Survey ◽  
Factors Affecting ◽  
Mixed Model Approach ◽  
Potential Factors ◽  
Model Approach

Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model performances have also been compared. Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

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