Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

Caraka, Rezzy Eko; Shohaimi, Shamarina; Kurniawan, Isma Dwi; Herliansyah, Riki; Budiarto, Arif; Sari, Shinta Purnama; Pardamean, Bens

doi:10.1016/j.procs.2018.08.188

Cited by 23 publications

(10 citation statements)

References 7 publications

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“…We chose the two samples independent T-test to check whether there is a significant difference between activities on day and night as well as wet and dry seasons. To explain the distribution of activities in 24 hours, the effects of hours and night time (dummy variable where D=1 for 18.00-06.00, otherwise D=0) and to describe the interaction among activities, Generalized Linear Latent Variable Models (GLLVMs), a statistical model with random effects similar to multivariate Generalized Linear Mixed Models (GLMMs), was used (Caraka et al 2018). Warton et al (2015) explained that the GLLVMs has several advantages in modelling multivariate-correlated responses compared to GLLMs; the number of parameters estimated is significantly smaller than that of GLLM due to the assumption imposed on GLLM to have an unstructured variance-covariance matrix.…”

Section: Daily Activity Patterns Group Size and Group Patternsmentioning

confidence: 99%

Ecology and Conservation of the Endangered Banteng (Bos javanicus) in Indonesia Tropical Lowland Forest

et al. 2019

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Banteng, Bos javanicus, as wild cattle is a vital and importance source of germplasm in Indonesia. Various human activities currently threaten their conservation status. Nonetheless, no long-term monitoring programmes are in place for this species. Using distribution point and statistical analysis based on 46,116 camera trap days from December 2015 to January 2017, we aimed to provide habitat preferences, activity patterns and ecological data for banteng population in Ujung Kulon National Park (UKNP). It is the largest population of banteng in Indonesia and is living in a limited habitat area. According to the best occupancy model, the most suitable areas for this species were the secondary forest located at the center portion of UKNP. The presence of the invasive cluster sugar palm, Arenga obtusifolia, in dry season provides additional alternative food for banteng when its main food is scarcer in the forest. Banteng was cathemeral all year round, with the proportion of cathemeral records and the recording rate did not change with the protection of the level area, moon phase or season. To reduce the probability of encountering predators, banteng avoided the space use of dholes. Selection and avoidance of habitats was stronger than avoidance of the predator activity areas. Habitat competition from domestic cattle which grazed illegally in the national park appears to be a problem to the species since zoonosis appears from domestic cattle to banteng. Therefore, eﬀective law enforcement and an adequate conservation strategy are required to eliminate the impacts of both direct and indirect threats.

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Section: Daily Activity Patterns Group Size and Group Patternsmentioning

confidence: 99%

Ecology and Conservation of the Endangered Banteng (Bos javanicus) in Indonesia Tropical Lowland Forest

et al. 2019

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“…CC-BY-NC-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted March 29, 2021. ; https://doi.org/10.1101/2021.03.28.437086 doi: bioRxiv preprint variation thereof e.g., the negative binomial (Caraka et al, 2018) or zero-inflated count distribution for overdispered species counts, the binomial distribution (Golding et al, 2015) for presence-absences, and the Tweedie or Gamma distribution for biomass (Blakey et al, 2016) etc.…”

Section: Model-based Methods For Ordinationmentioning

confidence: 99%

“…Multivariate abundance data, also known as multi-species or community composition data, consist of abundance measurements (generally presence/absence, counts, cover or biomass) simultaneously collected for a large number of taxa. Due to the discrete nature of such data, each taxon is typically modelled with a distribution appropriate to its characteristics via a generalised linear model (GLM; McCullagh & Nelder, 1989) or some variation thereof e.g., the negative binomial (Caraka et al ., 2018) or zero-inflated count distribution for overdispered species counts, the binomial distribution (Golding et al ., 2015) for presence-absences, and the Tweedie or Gamma distribution for biomass (Blakey et al ., 2016) etc.…”

Section: Methodsmentioning

confidence: 99%

Fast model-based ordination with copulas

Popović

Hui

Warton

2021

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Visualising data is a vital part of analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modeling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence-absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted. Ordination is traditionally done using dissimilarity-based methods, most commonly non-metric multidimensional scaling (nMDS). In the last decade however, model-based methods for unconstrained ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients. Despite some major benefits, a major drawback of model-based ordination methods is their speed, as they typically taking much longer to return a result than dissimilarity-based methods, especially for large sample sizes. We introduce copula ordination, a new, scalable model-based approach to unconstrained ordination. This method has all the desirable properties of model-based ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula ordination is an order of magnitude faster than current model-based methods, and can even be faster than nMDS for large sample sizes, while being able to produce similar ordination plots and trends as these methods.

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“…GLLVM is the extended version of GLM's with a latent variable [32]. The marginal density of the manifest variables can be rewritten as Eq (7) [33].…”

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confidence: 99%

FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases

Caraka

Chen

Lee

et al. 2021

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Background: In the heart data mining and machine learning, dimension reduction is needed to remove the multicollinearity. Meanwhile, it has been proven to improves the interpretation of the parameter model. In addition, dimension reduction is also can increase the time of computing in high dimensional data. Methods: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital , following emergency department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM’s). Results: During the analysis, we measure the performance and calculate the time computing of GLLVM with employing variational approximation and Laplace approximation, and compare the different distributions including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedi, respectively. Conclusions: In a nutshell, GLLVM’s leads as best performance reaching the accuracy 98% comparing other methods. In line with this, we get the best model negative binomial and Variational approximation which provides the best accuracy by accruacy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

Cited by 23 publications

References 7 publications

Ecology and Conservation of the Endangered Banteng (Bos javanicus) in Indonesia Tropical Lowland Forest

Ecology and Conservation of the Endangered Banteng (Bos javanicus) in Indonesia Tropical Lowland Forest

Fast model-based ordination with copulas

FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases

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