1,721,034 research outputs found
The performance of multiple imputations for different number of imputations
No full textMultiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage
process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step
in the first stage is important. Hence, this study aimed to examine the performance of multiple imputation method at
different numbers of imputations. Monotone missing data pattern was created in the study by deleting approximately 24%
of the observations from the continuous result variable with complete data. At the first stage of the multiple imputation
method, monotone regression imputation at different numbers of imputations (m=3, 5, 10 and 50) was performed. In the
second stage, parameter estimations and their standard errors were obtained by applying general linear model to each
of the complete data sets obtained. In the final stage, the obtained results were pooled and the effect of the numbers of
imputations on parameter estimations and their standard errors were evaluated on the basis of these results. In conclusion,
efficiency of parameter estimations at the number of imputation m=50 was determined as about 99%. Hence, at the
determined missing observation rate, increase was determined in efficiency and performance of the multiple imputation
method as the number of imputations increased
Extreme variability modelling of overdispersed germination count experiments
No full textGermination tests are carried out for a wide variety of purposes in weed control. The variability in seed germination counts raises the overdispersion problem. The objective of this study is to compare different approaches used in solving overdispersion and to offer practical solutions to researchers. The data sets were created from seed germination counts, which examined the allelopathic effect of white cabbage (Brassica oleracea L. var. capitata L.) on the germination of some culture and weed seeds. Methanol and aqueous concentrations (30%, 40%, 50%) of dry and fresh white cabbage were used. Assuming the Poisson distribution in the generalized linear mixed model, overdispersion problem was determined in redroot pigweed (Amaranthus retroflexus L.), lamb's quarters (Chenopodium album L.) and sugar beet (Beta vulgaris L.) Equidispersion was determined in corn (Zea mays L.) and it was perfectly adapted to the Poisson distribution. In order to overcome the overdispersion problem, generalized Poisson distribution outperformed negative binomial distribution. The increase concentration in the generalized Poisson in weeds, fresh cabbage methanol and aqueous applications were very effective reducing germination (p < 0.05). The best results in weed seeds were obtained at 50%. Unlike weeds, 30% concentration of dry cabbage methanol and aqueous were considered as the upper limit in order not to adversely affect germination in Z. mays and B. vulgaris. Consequently, in germination tests, the problem of overdispersion is inevitable as a result of excessive variability. For germination count data, generalized Poisson distribution is viable option and powerful alternative to accurately describe mean-variance relationship
DETERMINATION OF APPROPRIATE COVARIANCE STRUCTURES IN RANDOM SLOPE AND INTERCEPT MODEL APPLIED IN REPEATED MEASURES
No full textThis study aims to determine variance-covariance structures of dependent variable in data set containing repeated measures and to compare covariance parameter estimation methods. To this end, random intercept and slope model which is among the special cases of linear mixed model was formed and the time variable was involved into the model in a continuous and categorical manner. Also, compound symmetry (CS), toeplitz (TOEP), first-order autoregressive (AR(1)), homogeneous variance-covariance models and unstructured (UN), heterogeneous compound symmetry (CSH), heterogeneous toeplitz (TOEPH), heterogeneous first-order autoregressive (ARH(1)), first-order ante-dependence (ANTE(1)) and unstructured correlation (UNR) heterogeneous variance-covariance models were performed in order to determine the variance-covariance structure between the repeated measures. In addition, comparison of ML and REML was carried out as covariance parameter estimation method. Consequently, random intercept and slope model (RISM) was found to be the most appropriate one in modeling the repeated measure data when ML was used as the parameter estimation method and UN, CSH, ARH(1), TOEPH, ANTE(1), UNR as the covariance models
Using Generalized Procrustes Analysis for Evaluation of Sensory Characteristic Data of Lamb Meat
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