Goodness Of Fit Statistics Research Articles

Evaluation of quasi-maximum likelihood and smearing estimator to improve sediment rating curve estimation

The basic sediment rating curve (SRC) method suffers from the lack of accuracy in most cases. Two estimation methods including quasi-maximum likelihood estimator (QMLE) and non-parametric or smearing estimator are investigated in this study to consider changes in basic SRC estimations. Four hydrometric stations datasets in Gorgan Gulf basin in northern part of Iran are selected as case study and graphical assessment, RMSE and Nash-Sutcliff statistics are applied for evaluation of different methods. Graphical assessment shows that the correction methods tend to decrease the distance between observed and predicted sediment load by basic method in three of four cases. In addition, goodness of fit statistics implies better results for the application of quasi-maximum likelihood estimator method compared to basic SRC and smearing methods.

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) .

Study of relationship quality dimensionality in the parcel delivery services market

The article is organized in five parts. First part is introduction. In this part the research problem, the aim of this work and objectives are formulated. The aim of this work is as follows: after having analyzed an integrative model of determinants of key relationship marketing outcomes, to perform confirmatory factor analysis of the scale in order to assess if the same dimensions structure exists for parcel delivery services. The objectives of this work are: 1) to analyze T. Hennig-Thurau et al. (2002) model of determinants of key relationship outcomes, 2) to do survey of Lithuanian parcel delivery services consumers and to measure relationship quality using this instrument, 3) to perform confirmatory factor analysis in order to determine if dimensions structure of the model is the same for Lithuanian parcel delivery services. The problem solved in this paper is as follows: an integrative model of determinants of key relationship marketing outcomes was developed using a wide variety of services to expand the generalization therefore it is possible to examine context-specific relationships of a single service type. Moreover, it is not known if the same relationship quality dimensions exist in other than North-American cultural context such as Lithuanian culture. In the second part of this article relationship quality model suggested by T. Hennig-Thurau et al. (2002) is analyzed and research hypotheses are formulated. Hypotheses are formulated in order to test relationships between dimensions and their discriminant validity. In the third part of this work research methods are described. Confirmatory factor analysis process is analyzed. In the fourth part of this article the results of study are analyzed. Various goodness of fit statistics such as degrees of freedom and chi-square test, RMSEA, NFI, NNFI, PNFI, CFI, IFI, RFI, standardized RMR, GFI, AGFI, PGFI, D2, Fornell-Larcker test is analyzed in order to accept or reject hypotheses and to assess relationships between dimensions and discriminant validity of these dimensions. Nested models are compared with the original model and differences in goodness of fit statistics are examined. In the fifth part of this work final conclusions are formulated about model’s reliability and validity in parcel delivery services setting.

A genetic programming-based QSPR model for predicting solubility parameters of polymers

In this study, linear and nonlinear quantitative structure-property relationship (QSPR) models, respectively called the multiple linear regression based QSPR (MLR-QSPR) model and the genetic programming based QSPR (GP-QSPR) model, were built to predict the solubility parameters of polymers with structure –(C1H2–C2R3R4)–, as function of some constitutional, topological and quantum chemical descriptors. The results from the internal validation analysis indicated that the GP-QSPR model has better goodness of fit statistics. The external and overall validation measures also confirmed that the GP-QSPR model significantly outperforms the MLR-QSPR model in terms of some performance metrics over the same testing data set, and that genetic programming has good potential to obtain more accurate models in QSPR studies.

Derivation and validation of the acute organ failure score to predict outcome in critically ill patients: a cohort study.

Prediction models for ICU mortality rely heavily on physiologic variables that may not be available in large retrospective studies. An alternative approach when physiologic variables are absent stratifies mortality risk by acute organ failure classification. Retrospective cohort study. Two large teaching hospitals in Boston, MA. Ninety-two thousand eight hundred eighty-six patients aged 18 years old or older admitted between November 3, 1997, and February 25, 2011, who received critical care. None. The derivation cohort consisted of 35,566 patients from Brigham and Women's Hospital, and the validation cohort comprised 57,320 patients from Massachusetts General Hospital. Acute organ failure was determined for each patient based on International Classification of Diseases, 9th Revision, Clinical Modification code combinations. The main outcome measure was 30-day mortality. A clinical prediction model was created based on a logistic regression model describing the risk of 30-day mortality as a function of age, medical versus surgical patient type, Deyo-Charlson index, sepsis, and type acute organ failure (respiratory, renal, hepatic, hematologic, metabolic, and neurologic) after ICU admission. We computed goodness-of fit statistics and c-statistics as measures of model calibration and 30-day mortality discrimination, respectively. Thirty-day mortality occurred in 5,228 of 35,566 patients (14.7%) assigned to the derivation cohort. The clinical prediction model was predictive for 30-day mortality. The c-statistic for the clinical prediction model was 0.7447 (95% CI, 0.74-0.75) in the derivation cohort and 0.7356 (95% CI, 0.73-0.74) in the validation cohort. For both the derivation and validation cohorts, the Hosmer-Lemeshow chi-square p values indicated good model fit. In a smaller cohort of 444 patients with Acute Physiologic and Chronic Health Evaluation II scores determined, differences in model discrimination of 30-day mortality between the clinical prediction model and Acute Physiologic and Chronic Health Evaluation II were not significant (chi-square=0.76; p=0.38). An acute organ failure-based clinical prediction model shows good calibration and discrimination for 30-day mortality in the critically ill. The clinical prediction model compares favorably to Acute Physiologic and Chronic Health Evaluation II score in the prediction of 30-day mortality in the critically ill. This score may be useful for severity of illness risk adjustment in observational studies where physiologic data are unavailable.

Using Canopy Indices to Quantify the Economic Optimum Nitrogen Rate in Spring Wheat

In‐season N applications to spring wheat (Triticum aestivum L.) may increase profits and improve N fertilizer accuracy. The objectives were to develop a calibration tool employing normalized difference vegetative index (NDVI) and SPAD 502 chlorophyll meter (SPAD) measurements for calculating the differential from the economic optimum N rate (dEONR) at growth stages Z22, Z24, and Z31 to Z39 and provide N rate algorithms for use in applying N fertilizer at a variable rate. Sensing was conducted trials over 3 yr encompassing 10 site‐years across Southeastern Buenos Aires Province, Argentina. The relationship between sensor indices and dEONR was evaluated by fitting quadratic plateau (QP) regression models. Statistically significant QP models were determined at the Z24, Z31, and Z39 growth stages. Relative SPAD (rSPAD) and relative NDVI (rNDVI) reduced variation and improved the calibration of measured N stress with the dEONR. For Z31 and Z39, the rSPAD had the best goodness of fit statistics when compared to rNDVI [adjusted R2 (adjR2)= 0.67 and 0.57 at Z31 and 0.68 and 0.52 at Z39, respectively]. However, adjustment at Z24 was higher for rNDVI (adjR2 = 0.53 and 0.61 for rSPAD and rNDVI, respectively). A single QP model to estimate the dEONR with 58% confidence was adjusted for the Z31 and Z39 growth stages. This indicates that the same calibration for N rate determination based on rSPAD or rNDVI values can be used during stem elongation in spring wheat. This model can be used as an N rate algorithm for applying N fertilizer in‐season.

Small for gestational age births among South Indian women: temporal trend and risk factors from 1996 to 2010

BackgroundThe birth weight and gestational age at birth are two important variables that define neonatal morbidity and mortality. In developed countries, chronic maternal diseases like hypertension, diabetes mellitus, renal disease or collagen vascular disease is the most common cause of intrauterine growth restriction (IUGR). Maternal nutrition, pregnancy induced hypertension, chronic maternal infections, and other infections such as cytomegalovirus, parvovirus, rubella and malaria are the other causes of IUGR. The present study examines the secular trend of Small for Gestational Age (SGA) over 15 years and risk factors for SGA from a referral hospital in India.MethodsData from 1996 to 2010 was obtained from the labour room register. A rotational sampling scheme was used i.e. 12 months of the year were divided into 4 quarters. Taking into consideration all deliveries that met the inclusion criteria, babies whose birth weights were less than 10th percentile of the cut off values specific for gestational ages, were categorized as SGA. Only deliveries of live births that occurred between 22 and 42 weeks of pregnancy were considered in this study. Besides bivariate analyses, multivariable logistic regression analysis was done. Nagelkerke R2 statistics and Hosmer and Lemeshow chi-square statistics were used as goodness of fit statistics.ResultsBased on the data from 36,674 deliveries, the incidence of SGA was 11.4% in 1996 and 8.4% in 2010. Women who had multiple pregnancies had the higher odds of having SGA babies, 2.8 (2.3-3.3) times. The women with hypertensive disease had 1.8 (1.5-1.9) times higher odds of having SGA. Underweight women had 1.7 (1.3 - 2.1) times and anaemic mothers had 1.29 (1.01 - 1.6) times higher odds. The mothers who had cardiac disease were 1.4 (1.01 - 2.0) times at higher odds for SGA. In teenage pregnancies, the odds of SGA was 1.3 (1.1 - 1.5) times higher than mothers in the age group 20 to 35 years.ConclusionsThere is a significant reduction in the incidence of SGA by 26% over 15 years. The women with the above modifiable risk factors need to be identified early and provided with health education on optimal birth weight.

Graphical Tools to Assess Goodness-of-Fit in Non-Location-Scale Distributions

Goodness-of-fit (GOF) techniques are used for assessment whether a distribution is suitable to describe a data set or not. These techniques have been studied for distributions belonging to the location-scale family. However, one could be interested in making this assessment for distributions that do not belong to this family. We review the available GOF tests and propose graphical tools based on these tests for censored and uncensored data from non-location-scale distributions. Anderson-Darling, Cramer-von Mises, Kolmogorov-Smirnov, Kuiper, Michael and Watson GOF statistics are considered. We apply the proposed results to real-world data sets to illustrate their potential, with emphasis on some Birnbaum-Saunders distributions.

Regressional Estimation of Cotton Sirospun Yarn Properties from Fibre Properties

Abstract In this paper, it is aimed at determining the equations and models for estimating the sirospun yarn quality characteristics from the yarn production parameters and cotton fibre properties, which are focused on fibre bundle measurements represented by HVI (high volume instrument). For this purpose, a total of 270 sirospun yarn samples were produced on the same ring spinning machine under the same conditions at Ege University, by using 11 different cotton blends and three different strand spacing settings, in four different yarn counts and in three different twist coefficients. The sirospun yarn and cotton fibre property interactions were investigated by correlation analysis. For the prediction of yarn quality characteristics, multivariate linear regression methods were performed. As a result of the study, equations were generated for the prediction of yarn tenacity, breaking elongation, unevenness and hairiness by using fibre and yarn properties. After the goodness of fit statistics, very large determination coefficients (R2 and adjusted R2) were observed.

On the validity of popular masculinity rating scales with gay men.

During the past decade, greater quantitative attention has been given to how gay men's lives are affected by traditional notions of masculinity. Consequently, it is important that masculinity-related measures that are often used in research are valid for use with gay men. This study examined the factor structures, loadings, and psychometric properties of three commonly used masculinity-related measures: the Gender Role Conflict Scale, the Conformity to Masculine Norms Inventory, and the Reference Group Identity Dependence Scale. Data were collected via an online survey of 920 self-identified gay men (M(age) = 32.48 years, SD = 11.73). Confirmatory factor analyses indicated that while the goodness of fit statistics did not always indicate the model fit, there were similar endorsements of items across the three masculinity scales and subscale factor loadings consistent with published studies using mostly heterosexual male samples. Implications for future masculinity scale research on gay men are discussed.

Abusive supervision and links to nurse intentions to quit.

To investigate forms of abusive supervision, namely personal attacks, task attacks, and isolation, and their links to outcomes for nurses, including job satisfaction, psychological strain, and intentions to quit. Cross-sectional survey design. Data collected from July to November 2012. Two hundred and fifty public sector nurses employed at five general acute Australian hospitals completed the survey (response rate of 33%). Structural equation modeling on the forms of abusive supervision (personal, task, isolation) and nurse outcomes indicated goodness of fit statistics that confirmed a well-fitting model, explaining 40% of the variance in intent to quit, 30% in job satisfaction, and 33% in strain. An indirect relationship from personal attacks to intentions to quit, via strain, was observed. Task attacks were related directly, and indirectly via job satisfaction, to increased intentions to quit. Surprisingly, isolation was positively related to job satisfaction. Abusive supervision impacted nurse outcomes. Specifically, personal abuse had personal and health impacts; work-focused abuse had work-oriented effects. Applying appraisal theory suggests that personal attacks are primarily assessed as stressful and unchangeable; task-oriented attacks are assessed as stressful, but changeable; and isolation is assessed as benign. The findings highlight the impact of abusive supervision, especially task attacks, on outcomes important to nurse retention. The findings can be used to devise programs to educate, train, and support supervisors and their subordinates to adhere to zero tolerance policies toward antisocial workplace behaviors and encourage reporting incidents.

English

The primary objective of this study was to examine the effect of missing data on goodness of fit statistics in confirmatory factor analysis (CFA). For this aim, four missing data handling methods; listwise deletion, full information maximum likelihood, regression imputation and expectation maximization (EM) imputation were examined in terms of sample size and proportion of missing data. It is evident from the results that when the proportions of missingness %1 or less, listwise deletion can be preferred. For more proportions of missingness, full information maximum likelihood (FIML) imputation method shows visible performance and gives closest fit indices to original fit indices. For this reason, FIML imputation method can be preferred in CFA.   Key words:  Missing data, goodness of fit, confirmatory factor analysis, incomplate data, and missing value.

Sepsis severity score: an internationally derived scoring system from the surviving sepsis campaign database*.

As the Surviving Sepsis Campaign was assessing patient-level data over multiple countries, we sought to evaluate the use of a pragmatic and parsimonious severity-of-illness scoring system for patients with sepsis in an attempt to provide appropriate comparisons with practical application. Prospective, observational evaluation. Data from 23,438 patients with suspected or confirmed sepsis from 218 hospitals in 18 countries were evaluated. This analysis was conducted on prospective data submitted to a database from January 2005 through March 2010. None. Maximum likelihood logistic regression was used to estimate model coefficients, and these were then used to develop a Sepsis Severity Score. The probability of hospital mortality was estimated using the Sepsis Severity Score as the sole variable in a logistic regression model. Univariable logistic regression determined which variables were included in the multivariable predictor model. The scale of continuous variables was assessed using fractional polynomials. Two-way interactions between variables were considered for model inclusion if the interaction p value is less than 0.05. The prediction model was developed based on randomly selecting 90% of available patients and was validated on the remaining 10%, as well as by using a bootstrapping technique. The p values for the Hosmer-Lemeshow goodnessof-fit statistic in the developmental and validation datasets were considerably greater than 0.05, suggesting good calibration. Development and validation areas under the receiver operator curve curves were 0.736 and 0.748, respectively. Observed and estimated probabilities of hospital mortality for the total population were both 0.334. The validation and the developmental datasets were gradually compared over deciles of predicted mortality and found to be very similar. The Sepsis Severity Score accurately estimated the probability of hospital mortality in severe sepsis and septic shock patients. It performed well with respect to calibration and discrimination, which remained consistent over deciles. It functioned well over international geographic regions. This robust, population-specific evaluation of international severe sepsis patients provides an effective and accurate mortality estimate allowing for appropriate quality comparisons with practical clinical and research application.

Use of Structural Equation Modeling to Measure Severity of Single-Vehicle Crashes

Injury severity and vehicle damage are two of the main indicators of the level of crash severity. Other factors, such as driver characteristics, roadway conditions, highway geometry, environmental factors, vehicle type, and roadside objects, may also be directly or indirectly related to crash severity. All these factors interact in such complicated ways that it is often difficult to identify their interrelationships. The aim of this study was to examine the relationships between these contributors and the severity of single-vehicle crashes. Structural equation modeling (SEM) offers the opportunity to explore the complex relationships between variables by handling endogenous variables and exogenous variables simultaneously. Furthermore, SEM allows latent variables to be included in the model and bridges the gap between dependent and explanatory variables. In this study, the number of latent variables was defined by the understanding of collision force, kinetic energy, and mechanical process of a collision, as well as statistical goodness of fit that was based on available data. Three SEM models (one with one latent variable, one with two, and one with three) representing the hypothesized relationships between collision force, speed of a vehicle, and severity of a crash were developed and evaluated in an attempt to unravel the relationships between exogenous factors and severity of single-vehicle crashes. On the basis of goodness of fit and model predictive power, the model with two latent variables outperformed the other two. Additional insights about model selection were provided through the development and comparison of the three models.

A water resources model to explore the implications of energy alternatives in the southwestern US

This letter documents the development and validation of a climate-driven, southwestern-US-wide water resources planning model that is being used to explore the implications of extended drought and climate warming on the allocation of water among competing uses. These model uses include a separate accounting for irrigated agriculture; municipal indoor use based on local population and per-capita consumption; climate-driven municipal outdoor turf and amenity watering; and thermoelectric cooling. The model simulates the natural and managed flows of rivers throughout the southwest, including the South Platte, the Arkansas, the Colorado, the Green, the Salt, the Sacramento, the San Joaquin, the Owens, and more than 50 others. Calibration was performed on parameters of land cover, snow accumulation and melt, and water capacity and hydraulic conductivity of soil horizons. Goodness of fit statistics and other measures of performance are shown for a select number of locations and are used to summarize the model’s ability to represent monthly streamflow, reservoir storages, surface and ground water deliveries, etc, under 1980–2010 levels of sectoral water use.

Evaluation of IScore validity in a Greek cohort of patients with type 2 diabetes

BackgroundDiabetes constitutes a risk factor for stroke that also aggravates stroke prognosis. Several prognostic models have been developed for the evaluation of neurologic status, severity, short-term functional outcome and mortality of stroke patients. IScore is a novel tool recently developed in order to predict mortality rates within 30 days and 1 year after ischemic stroke and diabetes is not included in the scoring scale of IScore. The aim of the present study was to evaluate and compare IScore validity in ischemic stroke patients with and without diabetes.MethodsThis prospective study included 312 consecutive Caucasian patients with type 2 diabetes and 222 Caucasian patients without diabetes admitted for ischemic stroke in a tertiary Greek hospital. Thirty-day and 1-year IScores were individually calculated for each patient and actual mortality was monitored at the same time intervals. IScore’s predictive ability and calibration was evaluated and compared for ischemic stroke patients with and without diabetes. The performance of IScore for predicting 30 and 1-year mortality between patients with and without diabetes was assessed by determining the calibration and discrimination of the score. The area under the receiver operating characteristic curve was used to evaluate the discriminative ability of IScore for patients with and without diabetes, whereas the calibration of IScore was assessed by the Hosmer–Lemeshow goodness-of fit statistic.ResultsBaseline population characteristics and mortality rates did not differ significantly for both cohorts. IScore values were significantly higher for patients with diabetes at 30 days and 1 year after ischemic stroke and patients with diabetes presented more frequently with lacunar strokes. Based on ROC curves analysis IScore’s predictive ability for 30 day mortality was excellent, without statistically significant difference, for both cohorts. Predictive ability for 1 year mortality was also excellent for both groups with significantly better ability for patients with diabetes especially at high score values. Calibration of the model was good for both groups of patients.ConclusionsIScore accurately predicts mortality in acute ischemic stroke Caucasian patients with and without diabetes with higher efficacy in predicting 1 year mortality in patients with diabetes especially with high scores.

A frailty model for interaction between multiple events

Most models for categorical data rely on linear models in which higher order interactions are limited, usually to second order terms. Here we explore a dataset where second order interactions lead unavoidably to high order interactions. However in certain cases these interactions have a simple form which can be explained by population heterogeneity or ‘frailty’ and the Rasch model which uses a logistic structure is one such model. However its parameters are difficult to interpret. When event probabilities are low, we develop a simple model based on a multiplicative structure and a one-parameter gamma frailty model is developed to accommodate this sort of data. Moment estimators estimates are also provided for more general cases and goodness of fit statistics are given. An example involving 35 different human papillomavirus types in 33614 women with normal cervical cytology smears (stratified by age) is explored.

Validation of the Episodic Disability Framework with adults living with HIV

Purpose: To assess the validity of dimensions of disability in the Episodic Disability Framework, a conceptual framework derived from the perspective of adults living with HIV. Methods: We conducted confirmatory factor analyses with 913 adults living with HIV in an observational cohort study called the Ontario HIV Treatment Network Cohort Study (OCS). We tested hypotheses that dimensions of disability in the Episodic Disability Framework were represented by a group of measured variables in the observational database. Results: A model comprised of four latent variables and 43 indicator variables with one cross-loading was superior to models with fewer latent variables and more indicator variables and supported the validity of disability dimensions: physical health symptoms (represented by 21 indicator variables), mental health symptoms (10 variables), difficulties with day-to-day activities (5 variables) and challenges to social inclusion (8 variables). Overall goodness of fit statistics were χ2 = 2621.50 (p < 0.001), Comparative Fit Index = 0.912, Tucker Lewis Index = 0.907 and root mean square error of approximation = 0.048. Dimensions of disability correlated with each other ranging from r = 0.44 (between physical symptoms and challenges to social inclusion) to r = 0.81 (between physical symptoms and difficulties with day-to-day activities). Conclusions: This study supports the validity of four disability dimensions in the Episodic Disability Framework. This framework provides a new way to conceptualize disability and can lay the foundation for developing a future HIV disability measure for clinical and health services research.Implications for RehabilitationThe Episodic Disability Framework is the first known conceptual framework of disability developed from the perspective of adults living with HIV.Results from this confirmatory factor analysis support the validity of four dimensions of disability experienced by adults living with HIV in the Episodic Disability Framework including: physical symptoms and impairments, mental health symptoms and impairments, difficulties carrying out day-to-day activities, and challenges to social inclusion.The Episodic Disability Framework provides a new way to conceptualize disability experienced by adults living with HIV. Clinicians can use this Framework to better understand episodic disability experienced by adults living with HIV. Clinicians can frame their assessments of disability to include physical and mental health symptoms and impairments as well as consider a patient’s ability to participate in society, and indicate areas to apply interventions or strategies to prevent or mitigate disability experienced by adults living with HIV.

One Hundred Years of Isotope Geochronology, and Counting

In 1913, Frederick Soddy's research on the fundamentals of radioactivity led to the discovery of “isotopes.” Later that same year, Arthur Holmes published his now famous book The Age of the Earth, in which he applied this new science of radioactivity to the quantification of geologic time. Combined, these two landmark events did much to establish the field of “isotope geochronology” – the science that underpins our knowledge of the absolute age of most Earth (and extraterrestrial) materials. In celebrating the centenary, this issue brings together modern perspectives on the continually evolving field of isotope geochronology – a discipline that reflects and responds to the demands of studies ranging from the early evolution of the Solar System to our understanding of Quaternary climate change, and the 4.5 billion years in between. * Accuracy : The closeness of agreement between a measured quantity value and a true quantity value Decay constant (λ) : The reciprocal value of the average lifetime of a radionuclide, which is the time over which a population of parent radionuclides is reduced by 1/e times its initial value. The half-life of a radionuclide is equal to ln(2)/λ. Isotope dilution thermal ionisation mass spectrometry (ID-TIMS) : A method of isotopic analysis in which an artificial or enriched isotopic tracer is added to a dissolved sample (e.g. zircon) to make a homogeneous isotopic mixture, the isotopic composition of which is analysed using TIMS. This technique is currently the form of isotopic measurement with the highest precision and accuracy, but it requires complete dissolution of the sample. LA–ICP–MS (laser ablation inductively coupled plasma mass spectrometry) : A microanalytical method that employs a focused laser beam to ablate material from samples. The ejected matter is ionised using a plasma before being passed through to a mass spectrometer. MSWD (mean squared weighted deviation) : A goodness of fit statistic that compares the sum of the squares of the deviations of a set of measurements from their mean value to the corresponding sum of the variances of each measurement Precision : The closeness of agreement between indications or measured quantity values obtained by replicate measurements on the same or similar objects under specified conditions Secondary ion mass spectrometry (SIMS) : Also referred to as an ion microprobe or microscope, SIMS measures the chemical or isotopic composition of small sample volumes by focusing a beam of high-energy primary ions onto a polished sample surface, ablating atoms and molecules, and generating secondary ions that are analysed by mass spectrometry. The high spatial resolution offered by SIMS (commonly <30 μm wide and <1 μm deep during analysis of geological materials) allows in situ analysis of geological materials. The method is relatively non-destructive, allowing multiple analyses to be performed within single grains or zones within grains, but has lower analytical precision than ID-TIMS. Radioactive decay : Nuclear reactions by which an atomic nucleus transforms via emission of ionising particles and electromagnetic radiation. Radioactive decay is a stochastic (i.e. random) process at the level of single atoms, in that it is impossible to predict when a given atom will decay. However, the probability that a given atom will decay is constant over time. Uncertainty (of measurements) : A parameter characterising the dispersion of the quantity values being attributed to the subject of a measurement, which can include components arising from both random and systematic measurement errors. Random errors are those that in replicate measurements vary in an unpredictable manner; systematic errors are those that remain constant or vary in a predictable manner across replicate measurements.

Evaluation of Growth Functions on Japanese Quail Lines

The aim of present study was to fit the best predictive equation to describe the growth curve of different Japanese quail lines. Moreover the effect of short-term divergent selection on the growth curve parameters was investigated. The quail lines utilized in the current study were two divergently selected lines for high (HW) and low (LW) 4-wk body weight (BW) and a control line (C). Determination and adjusted determination coefficients, relative error mean and standard deviation, mean square error, Akaike’s information criteria and Schwarz Bayesian information criteria were used to evaluate the accuracy of prediction with the growth functions of Hyperbolastic (H1, H2, H3), Richards, Gompertz, Logistic and Von bertalanffy. Based on model behavior and statistical performance, the Gompertz and Logistic functions were not able to show a suitable fit for all three lines. The overall goodness of fit statistics in the HW line showed that the Richards function has the best fit to the data followed by H3, H2, H1, Von bertalanffy, Gompertz and Logistic functions, respectively. The overall results in the LW and C lines were similar to the HW line, except that Logistic function provided a better fit to the data than Gompertz. The study of growth pattern using Richards function revealed that short-term divergent selection altered the growth trajectory of selected lines through the changing of shape parameters and relative intensity of growth rates.