Monitoring habitat types by the mixed multinomial logit model using panel data

Habitats in the Wadden Sea, a world heritage area, are affected by land subsidence resulting from natural gas extraction and by sea level rise. Here we describe a method to monitor changes in habitat types by producing sequential maps based on point information followed by mapping using a multinomial logit regression model with abiotic variables of which maps are available as predictors.In a 70 ha study area a total of 904 vegetation samples has been collected in seven sampling rounds with an interval of 2–3 years. Half of the vegetation plots was permanent, violating the assumption of independent data in multinomial logistic regression. This paper shows how this dependency can be accounted for by adding a random effect to the multinomial logit (MLN) model, thus becoming a mixed multinomial logit (MMNL) model. In principle all regression coefficients can be taken as random, but in this study only the intercepts are treated as location-specific random variables (random intercepts model). With six habitat types we have five intercepts, so that the number of extra model parameters becomes 15, 5 variances and 10 covariances.The likelihood ratio test showed that the MMNL model fitted significantly better than the MNL model with the same fixed effects. McFadden-R² for the MMNL model was 0.467, versus 0.395 for the MNL model. The estimated coefficients of the MMNL and MNL model were comparable; those of altitude, the most important predictor, differed most. The MMNL model accounts for pseudo-replication at the permanent plots, which explains the larger standard errors of the MMNL coefficients. The habitat type at a given location-year combination was predicted by the habitat type with the largest predicted probability. The series of maps shows local trends in habitat types most likely driven by sea-level rise, soil subsidence, and a restoration project.We conclude that in environmental modeling of categorical variables using panel data, dependency of repeated observations at permanent plots should be accounted for. This will affect the estimated probabilities of the categories, and even stronger the standard errors of the regression coefficients.     

Asymptotic Interval Estimation of Some Cause-Specific Mortality Risk Measures in Epidemiologic Studies

The present paper describes the algebraic and statistical relationships between the population-based mortality risk measures, the Standardized Mortality Ratio (SMR) and the Standardized Risk Ratio (SRR), and their respective proportional mortality-based counterparts, the internally and externally Standardized Proportional Mortality Ratios (SPMR, S_ePMR). The paper shows, how under some reasonable assumptions, asymptotically precise inferences about population-based risk measures can be made from studies of proportional mortality. Through application of the asymptotic multivariate normal approximation to the multinomial distribution, shortest confidence intervals for the relative SMR (RSMR) involving the corresponding SPMR are constructed for any cause of death. This same technique is also used to construct asymptotic prediction intervals about the cause-specific S_ePMR which, with high probability, contains the corresponding relative SRR (RSRR). The utility of the proportional mortality measures SPMR and S_ePMR as estimators of the corresponding cause-specific risks RSMR and RSRR in occupational epidemiologic research is empirically evaluated using data from two recent occupational cohort studies. Asymptotic Bonferroni type simultaneous inferential techniques are also developed for these measures which facilitate the assessment of overall risk in the presence of several competing factors.     

Applications and extensions of Chao's moment estimator for the size of a closed population

This article revisits Chao's (1989, Biometrics45, 427-438) lower bound estimator for the size of a closed population in a mark-recapture experiment where the capture probabilities vary between animals (model M(h)). First, an extension of the lower bound to models featuring a time effect and heterogeneity in capture probabilities (M(th)) is proposed. The biases of these lower bounds are shown to be a function of the heterogeneity parameter for several loglinear models for M(th). Small-sample bias reduction techniques for Chao's lower bound estimator are also derived. The application of the loglinear model underlying Chao's estimator when heterogeneity has been detected in the primary periods of a robust design is then investigated. A test for the null hypothesis that Chao's loglinear model provides unbiased abundance estimators is provided. The strategy of systematically using Chao's loglinear model in the primary periods of a robust design where heterogeneity has been detected is investigated in a Monte Carlo experiment. Its impact on the estimation of the population sizes and of the survival rates is evaluated in a Monte Carlo experiment.     

黑曲霉生产β-葡萄糖苷酶发酵条件的研究产

经多项式回归分析,研究了不同浓度N源、C源、无机盐等对酶产量的影响,确定出最佳培养基配方为:麸皮4.9%,(NH₄)_2>SO₄0.4%,KH₂PO₄0.29%,CaCl₂0.05%,MgSO₄·7H₂O0.04%,FeSO₄·7H₂O 5mg·L^-1,ZnCl₂1.4mg·L^-1,0.2%油酸钠。并对培养温度、时间、培养基初始pH、通气量、接种量、接种方式等培养条件进行优化,使黑曲霉生产β葡萄糖苷酶的产量由17U·ml^-1增至21.3U·ml^-1.     

Sequentially Rejective Test Procedures for Detecting Outlying Cells in One- and Two-Sample Multinomial Experiments

For multiple testing of multinomial models in the case of one or two samples we propose using test procedures based on the principle described by MARCUS, PERITZ and GABRIEL (1976). These methods are based in each step of the sequentially rejective strategy on tests which exhaust the full α level (i.e. which are not conservative). The tests can be performed in a finite or asymptotic version.     

A comparative study of frontal bone morphology among Pleistocene hominin fossil groups

Features of the frontal bone that are conventionally used to distinguish among fossil hominin groups were quantitatively examined. Fifty-five fossil crania dating from the early to the late Pleistocene were analyzed. Using a modified pantograph, outlines of the frontal bone were collected along the midsagittal and two parasagittal planes. The profile from nasion to bregma, as well as two profiles above the medial and lateral sections of the orbit, respectively, extending from the orbital margin to the coronal suture were traced. The outlines were measured using Elliptical Fourier Function Analysis (EFFA), which enabled a quantification of aspects of the frontal bone that have historically been described primarily in nonmetric or linear terms. Four measurements were obtained: 1) overall morphology as expressed in the Fourier harmonic amplitudes; 2) maximum projection of the supraorbital torus at three points along the browridge (glabella and the medial and lateral aspects of the torus above the orbit); 3) maximum distance of the frontal squama from the frontal chord, capturing forehead curvature; and 4) nasion-bregma chord length. The results indicate that the midsagittal profile is significantly different among all Pleistocene groups in analyses that include both size and shape, as well as size-adjusted data. Homo erectus is significantly different from the late Pleistocene groups (Neandertals and early modern H. sapiens) in glabellar projection. Anatomically modern humans are significantly different from all other groups in both raw and size-standardized analyses of all three outlines that captured overall morphology, as well as forehead curvature and lateral supraorbital torus prominence, and middle Pleistocene Homo are significantly different in both medial and lateral overall parasagittal form. However, for the majority of analyses there were no significant differences among the Pleistocene archaic groups in supraorbital torus projection, frontal squama curvature, nasion-bregma chord length, or overall frontal bone morphology.     

Profiling of plasma and faeces by FT-IR to differentiate between heathland plant diets offered to zero-grazed sheep

To investigate the relationships between plant composition in a complex diet mixture and intake by sheep, blood and faeces were analysed by Fourier transform mid-infrared (FT-IR) spectroscopy. Each of four mature Welsh Mountain ewes was maintained, for a period of 9 days, on one of three diets comprising a mixture of heathland plants containing either 100 g/kg (H100), 200 g/kg (H200) or 300 g/kg (H300) heather (Calluna vulgaris) in the fresh matter. Spectra of blood and faeces from each animal were acquired and the data analysed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). We also investigated multinomial regression as an extension beyond the pattern recognition state. Animals offered the H300 diet could be consistently separated from animals fed on the H100 and H200 diets by PCA and HCA of plasma data, and these results were consistent with animals on the H300 diet consuming greater quantities of heather. Using faecal spectra, PCA and HCA discriminated between animals on the H200 diet and those on the H100 and H300 diets. Analysis of the plasma and faecal data by the multinomial model indicated that when PCs 4, 5 and 6 of the plasma data and PCs 3 and 4 of the faecal data were selected as covariates, leave-one-out cross-validation (LOO-CV) achieved an accurate dietary classification for 9 out of the 11 animals. In the present study, we have shown the ability to discriminate animals offered closely related diets on the basis of metabolic fingerprints of plasma and faeces.     

Unified maximum likelihood estimates for closed capture-recapture models using mixtures

Agresti (1994, Biometrics 50, 494-500) and Norris and Pollock (1996a, Biometrics 52, 639-649) suggested using methods of finite mixtures to partition the animals in a closed capture-recapture experiment into two or more groups with relatively homogeneous capture probabilities. This enabled them to fit the models Mh, Mbh (Norris and Pollock), and Mth (Agresti) of Otis et al. (1978, Wildlife Monographs 62, 1-135). In this article, finite mixture partitions of animals and/or samples are used to give a unified linear-logistic framework for fitting all eight models of Otis et al. by maximum likelihood. Likelihood ratio tests are available for model comparisons. For many data sets, a simple dichotomy of animals is enough to substantially correct for heterogeneity-induced bias in the estimation of population size, although there is the option of fitting more than two groups if the data warrant it.