Mise au point d'un tarif de cubage général pour les forêts québécoises : une approche pour mieux évaluer l'incertitude associée aux prévisions

2007 ◽  
Vol 83 (5) ◽  
pp. 754-765 ◽  
Author(s):  
Mathieu Fortin ◽  
Josianne DeBlois ◽  
Sylvain Bernier ◽  
Georges Blais
Keyword(s):  
Mixed Model ◽  
Random Effect ◽  
Tree Height ◽  
Sample Plot ◽  
Random Errors ◽  
Basic Premise ◽  
Volume Assessment ◽  
Detailed Assessment ◽  
Cruise Line ◽  
The Individual

Merchantable volume assessment is of prime importance in forest management and for the estimation of wood production in Quebec Crown forests. Currently, this assessment is undertaken at the individual stem level according to a statistical model commonly identified as the Perron general stock table. This polynomial model is based on tree diameter at breast height and tree height. However, the mathematical model form and the method used to calibrate it do not enable a correct and detailed assessment of the uncertainty associated with volume assessments. This study describes a new model which, accounts for errors associated with the use of estimated height in volume forecasts and also limits the propogation of errors to sample plot and cruise line. Random effects have been specified in the model in order to take into account spatial correlation between observations made at the sample plot and cruise line level. Results indicate sample plot and cruise line random errors constitute components of model error, which individually range from 2 % to 4 % of volume assessment. Consequently, the basic premise that errors associated with volume assessment of individual stems are compensated by volume summations at the sample plot level is not valid. Key words: mixed model, random effect, error propagation, variance

scholarly journals Factors associated with the weight of individual primal cuts and their inter-relationship in cattle

2019 ◽  
Vol 3 (4) ◽  
pp. 1593-1605 ◽  
Author(s):  
Michelle M Judge ◽  
Thierry Pabiou ◽  
Stephen Conroy ◽  
Rory Fanning ◽  
Martin Kinsella ◽  
...  
Keyword(s):  
Decision Support ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Carcass Weight ◽  
Holstein Friesian ◽  
Support Tools ◽  
Beef Herd ◽  
Fat Score ◽  
The Individual

Abstract Input parameters for decision support tools are comprised of, amongst others, knowledge of the associated factors and the extent of those associations with the animal-level feature of interest. The objective of the present study was to quantify the association between animal-level factors with primal cut yields in cattle and to understand the extent of the variability in primal cut yields independent carcass weight. The data used consisted of the weight of 14 primal carcass cuts (as well as carcass weight, conformation, and fat score) on up to 54,250 young cattle slaughtered between the years 2013 and 2017. Linear mixed models, with contemporary group of herd-sex-season of slaughter as a random effect, were used to quantify the associations between a range of model fixed effects with each primal cut separately. Fixed effects in the model were dam parity, heterosis coefficient, recombination loss, a covariate per breed representing the proportion of Angus, Belgian Blue, Charolais, Jersey, Hereford, Limousin, Simmental, and Holstein–Friesian and a three-way interaction between whether the animal was born in a dairy or beef herd, sex, and age at slaughter, with or without carcass weight as a covariate in the mixed model. The raw correlations among all cuts were all positive varying from 0.33 (between the bavette and the striploin) to 0.93 (between the topside and knuckle). The partial correlation among cuts, following adjustment for differences in carcass weight, varied from −0.36 to 0.74. Age at slaughter, sex, dam parity, and breed were all associated (P < 0.05) with the primal cut weight. Knowledge of the relationship between the individual primal cuts, and the solutions from the models developed in the study, could prove useful inputs for decision support systems to increase performance.

A comparative study of different covariance structure models for the analysis of repeated measurement data

2016 ◽  
Vol 10 (01) ◽  
pp. 1750007
Author(s):  
Yazhou Wu ◽  
Ling Zhang ◽  
Liang Zhou ◽  
Xiaoyu Liu ◽  
Ling Liu ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Measurement Data ◽  
Covariance Structure ◽  
Repeated Measurement ◽  
Covariance Structures ◽  
Analysis And Design ◽  
The One ◽  
The Individual

In repeated measurement data, the variables are not independent, and a certain auto-correlation typically exists between different levels of repeated measurement factors. The random error is composed of at least two parts, i.e. the individual random effect and the intra-individual multi-repeated measurement effect. Traditional statistical analysis methods (such as the [Formula: see text]-test and the one-way analysis of variance) are not applicable. The linear mixed model has been widely applied for the analysis and design of repeated measurement data. This paper focuses on medical examples and describes the selection of a covariance structure for the linear mixed model of repeated measurement in the modeling of different variance–covariance structures. By selecting different covariance structures, we can perform the parameter estimation and statistical test for the fixed effect of repeated measurement data, the parameters of random effects, and the covariance matrix. The results are analyzed and compared to provide a reference for applying the linear mixed model of repeated measurement to medical research.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals PSVII-18 Fatty acid composition of the longissimus muscle from Angus and Wagyu sired cattle at the 6th and 12th rib location

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 211-212
Author(s):  
Jerad Jaborek ◽  
Francis L Fluharty ◽  
Alejandro E Relling
Keyword(s):  
Fatty Acid ◽  
Mixed Model ◽  
Random Effect ◽  
Repeated Measurements ◽  
Longissimus Muscle ◽  
Trans Isomers ◽  
Fa Composition ◽  
Effect Of Treatment ◽  
Similar Body ◽  
Location Interaction

Abstract The fatty acid (FA) composition of the longissimus muscle (LM) of Angus and Wagyu sired cattle raised to a similar body weight (612 kg) were compared at the 6th and 12th rib locations. Angus sired steers represented T1, cattle from a Wagyu sire selected for growth represented T2, and cattle from a Wagyu sire selected for marbling represented T3. Data were analyzed mixed model with repeated measurements on animal (LM location); the model include the fixed effect of treatment, LM location, and their interaction, and random effect of sex. The percentage of 16:0, 18:1cis9, 18:3, and monounsaturated FA (MUFA) exhibited a treatment*LM location interaction (P ≤ 0.7), where T2 cattle had a greater percentage of 16:0 and a lesser percentage of 18:1cis9, 18:3, and MUFA at the 12th rib vs. 6th rib location compared with T1 and T3 cattle. The percentage of total FA lipid, polyunsaturated FA(PUFA), and PUFA:SFA ratio in the LM were greater (P ≤ 0.02) for T3 cattle compared with T1 and T2 cattle. The percentage of 18:0 was greater (P ≤ 0.01) for T1 cattle compared with T2 and T3 cattle, while T1 cattle had a greater (P ≤ 0.01) percentage of saturated FA (SFA) compared to T3 cattle. The percentage of 18:1cis9, other 18:1cis isomers, 18:2, MUFA, and MUFA:SFA ratio were greater (P ≤ 0.02) for T3 cattle compared with T1 cattle, with T2 cattle being intermediate. The percentage of total FA lipid, 18:0, 18:1 trans isomers, and SFA were greater (P ≤ 0.01) at the 6th rib LM location, while 14:1, 18:cis9, other 18:1 cis isomers, MUFA, MUFA:SFA, and PUFA:SFA ratio were greater (P ≤ 0.02) at the 12th rib LM location

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

scholarly journals $$\ell _2$$-penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency

Metrika ◽  
2021 ◽  
Author(s):  
Joscha Krause ◽  
Jan Pablo Burgard ◽  
Domingo Morales
Keyword(s):  
Mixed Model ◽  
Mean Squared Error ◽  
Routine Data ◽  
Parametric Bootstrap ◽  
Monte Carlo Simulation Study ◽  
Rank Deficiency ◽  
Prevalence Estimation ◽  
Fitting Algorithm ◽  
Individual Contributions ◽  
The Individual

AbstractRegional prevalence estimation requires the use of suitable statistical methods on epidemiologic data with substantial local detail. Small area estimation with medical treatment records as covariates marks a promising combination for this purpose. However, medical routine data often has strong internal correlation due to diagnosis-related grouping in the records. Depending on the strength of the correlation, the space spanned by the covariates can become rank-deficient. In this case, prevalence estimates suffer from unacceptable uncertainty as the individual contributions of the covariates to the model cannot be identified properly. We propose an area-level logit mixed model for regional prevalence estimation with a new fitting algorithm to solve this problem. We extend the Laplace approximation to the log-likelihood by an $$\ell _2$$ ℓ 2 -penalty in order to stabilize the estimation process in the presence of covariate rank-deficiency. Empirical best predictors under the model and a parametric bootstrap for mean squared error estimation are presented. A Monte Carlo simulation study is conducted to evaluate the properties of our methodology in a controlled environment. We further provide an empirical application where the district-level prevalence of multiple sclerosis in Germany is estimated using health insurance records.

scholarly journals Seemingly Unrelated Mixed-Effects Biomass Models for Black Locust in West Poland

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 380
Author(s):  
Karol Bronisz ◽  
Szymon Bijak ◽  
Rafał Wojtan ◽  
Robert Tomusiak ◽  
Agnieszka Bronisz ◽  
...  
Keyword(s):  
Black Locust ◽  
Random Effect ◽  
Tree Height ◽  
Cross Model ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Dependent Variables ◽  
Dry Biomass ◽  
Biomass Components ◽  
Independent Variable

Information about tree biomass is important not only in the assessment of wood resources but also in the process of preparing forest management plans, as well as for estimating carbon stocks and their flow in forest ecosystems. The study aimed to develop empirical models for determining the dry mass of the aboveground parts of black locust trees and their components (stem, branches, and leaves). The research was carried out based on data collected in 13 stands (a total of 38 sample trees) of black locust located in western Poland. The model system was developed based on multivariate mixed-effect models using two approaches. In the first approach, biomass components and tree height were defined as dependent variables, while diameter at breast height was used as an independent variable. In the second approach, biomass components and diameter at breast height were dependent variables and tree height was defined as the independent variable. Both approaches enable the fixed-effect and cross-model random-effect prediction of aboveground dry biomass components of black locust. Cross-model random-effect prediction was obtained using additional measurements of two extreme trees, defined as trees characterized by the smallest and largest diameter at breast height in sample plot. This type of prediction is more precise (root mean square error for stem dry biomass for both approaches equals 77.603 and 188.139, respectively) than that of fixed-effects prediction (root mean square error for stem dry biomass for both approaches equals 238.716 and 206.933, respectively). The use of height as an independent variable increases the possibility of the practical application of the proposed solutions using remote data sources.

Timing of Pegfilgrastim: Association with Febrile Neutropenia in a Pediatric Solid and CNS Tumor Population

2021 ◽  
Vol 38 (6) ◽  
pp. 375-384
Author(s):  
Laura Schlenker ◽  
Renee C.B. Manworren
Keyword(s):  
Febrile Neutropenia ◽  
Mixed Model ◽  
Random Effect ◽  
Retrospective Chart Review ◽  
Patient Characteristics ◽  
Secondary Outcome ◽  
Pharmacokinetic Data ◽  
Risk Patients ◽  
Independent Variable ◽  
Dichotomous Outcomes

Background: While recommended timing of pegfilgrastim administration is ≥24 h after chemotherapy, patient barriers to next day administration, available adult evidence, and pharmacokinetic data have led to earlier administration in some pediatric patients with solid and central nervous system tumors. The purpose of this study was to compare patient outcomes by timing of pegfilgrastim after chemotherapy. Methods: A retrospective chart review examined timing of 932 pegfilgrastim administrations to 182 patients, 0–29 years of age. The primary outcome was febrile neutropenia (FN); the secondary outcome was neutropenic delays (ND) ≥7 days to next chemotherapy cycle. To account for multiple pegfilgrastim administrations per patient, a generalized mixed model was used with a logit link for the dichotomous outcomes (FN & ND), timing as the dichotomous independent variable, and random effect for patient. Results: FN occurred in 196 of 916 cycles (21.4%); and ND in 19 of 805 cycles (2.4%). The fixed effect of pegfilgrastim administration < or ≥24 h after chemotherapy was not significant, p = .50; however, earlier or later than 20 h was significant, p = .005. FN odds were significantly higher when pegfilgrastim was given <20 h (OR 1.78, 95% CI: 1.19–2.65) after chemotherapy, which may be attributable to differences in chemotherapy toxicity regardless of pegfilgrastim timing. Discussion: While attempts should be made to administer pegfilgrastim ≥24 h after chemotherapy, if barriers exist, modified timing based on individual patient characteristics should be considered. Prospective randomized trials are needed to identify lower risk patients for early pegfilgrastim administration.

PSV-B-18 Characterization of the fecal microbiome of healthy adult beagle dogs supplemented with graded levels of bacillus coagulans GBI-30, 6086

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 337-338
Author(s):  
Heather L Acuff ◽  
Tara N Gaire ◽  
Tyler Doerksen ◽  
Andrea Lu ◽  
Michael P Hays ◽  
...  
Keyword(s):  
Relative Abundance ◽  
Fixed Effects ◽  
Mixed Model ◽  
Healthy Adult ◽  
Random Effect ◽  
Bacillus Coagulans ◽  
Community Diversity ◽  
Rrna Gene ◽  
Beagle Dogs ◽  
Fecal Microbiome

Abstract This study aimed to evaluate the effect of Bacillus coagulans GBI-30, 6086 on the fecal microbiome of healthy adult dogs. Extruded diets containing graded levels of probiotic applied either to the base ration before extrusion or as a topical coating post-extrusion were randomly assigned to ten individually-housed Beagle dogs (7 castrated males, 3 spayed females) of similar age (5.75 ± 0.23 yr) and body weight (12.3 ± 1.5 kg) in a 5 x 5 replicated Latin square with 16-d adaptation and 5-d total fecal collection for each period. Five dietary treatments were formulated to deliver a dose of 0-, 6-, 7-, 8-, or 9-log10 CFU·dog-1·d-1. Fresh fecal samples (n=50) were analyzed by 16S rRNA gene pyrosequencing. Community diversity was evaluated in R (v4.0.3, R Core Team, 2019). Relative abundance data were analyzed using a mixed model (v9.4, SAS Institute, Inc., Cary, NC) with treatment and period as fixed effects and dog as a random effect. Results were considered significant at P &lt; 0.05. Predominant phyla were Firmicutes (mean 81.2% ± 5), Actinobacteria (mean 9.9% ± 4.4), Bacteroidetes (mean 4.5% ± 1.7), Proteobacteria (mean 1.3% ± 0.7), and Fusobacteria (mean 1.1% ± 0.6). No evidence of shifts in predominant phyla, class, family, or genus taxonomic levels were observed except for the Bacillus genus, which had a greater relative abundance (P = 0.0189) in the low probiotic coating and high probiotic coating treatment groups compared to the extruded probiotic group. Alpha-diversity indices (Richness, Chao1, ACE, Shannon, Simpson, Inverse Simpson, and Fisher) and beta-diversity metrics (principal coordinate analysis and multi-dimensional scaling) were similar for all treatments. This data indicates that supplementation with Bacillus coagulans GBI-30, 6086 at a dose of up to 9 log10 CFU·d-1 did not alter the overall diversity of the fecal microbiome of healthy adult dogs over a 21-d period.

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