Model Evaluation with Weighted Threshold Optimization (and the “mewto” R package)

2021 ◽  
Author(s):  
Alexandru Monahov
Keyword(s):  
Model Evaluation ◽  
R Package ◽  
Threshold Optimization

scholarly journals Analyzing Repast Symphony models in R with RRepast package

2016 ◽  
Author(s):  
Antonio Prestes García ◽  
Alfonso Rodríguez-Patón
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Open Source ◽  
Model Evaluation ◽  
R Package ◽  
Experimental Setup ◽  
Model Output ◽  
Individual Based Models ◽  
Level Of Knowledge ◽  
High Level

AbstractIn order to produce dependable results, the output of models must be carefully evaluated and compared to the experimental data. One of the main goals of analyzing a model is understanding the effect of input factors on the model output. This task is carried out using a methodology known as sensitivity analysis. The analysis of Individual-based Models is hindered by the lack of simple tools allowing a complete and throughout evaluation without much effort. This kind of models tends to have a high level of complexity and the manual execution of a large experimental setup is generally not a feasible choice. Thus, it is required that model evaluation should ideally be simple and robust without demanding a high level of knowledge from modelers. In this work we present the RRepast, an open source GNU R package for executing, calibrating and analyzing Repast Symphony models directly from the R environment.

Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
C Roullier ◽  
Y Guitton ◽  
S Prado ◽  
O Grovel ◽  
YF Pouchus
Keyword(s):  
R Package ◽  
Halogenated Compound

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Systematics, Biogeography, and Morphological Character Evolution of the Hemiepiphytic Subfamily Monsteroideae (Araceae)

2019 ◽  
Vol 104 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Alejandro Zuluaga ◽  
Martin Llano ◽  
Ken Cameron
Keyword(s):  
Dna Sequences ◽  
Pacific Islands ◽  
R Package ◽  
Morphological Characters ◽  
Ancestral State ◽  
Seed Shape ◽  
Long Distance ◽  
The Pacific ◽  
And Migration ◽  
The Tropics

The subfamily Monsteroideae (Araceae) is the third richest clade in the family, with ca. 369 described species and ca. 700 estimated. It comprises mostly hemiepiphytic or epiphytic plants restricted to the tropics, with three intercontinental disjunctions. Using a dataset representing all 12 genera in Monsteroideae (126 taxa), and five plastid and two nuclear markers, we studied the systematics and historical biogeography of the group. We found high support for the monophyly of the three major clades (Spathiphylleae sister to Heteropsis Kunth and Rhaphidophora Hassk. clades), and for six of the genera within Monsteroideae. However, we found low rates of variation in the DNA sequences used and a lack of molecular markers suitable for species-level phylogenies in the group. We also performed ancestral state reconstruction of some morphological characters traditionally used for genera delimitation. Only seed shape and size, number of seeds, number of locules, and presence of endosperm showed utility in the classification of genera in Monsteroideae. We estimated ancestral ranges using a dispersal-extinction-cladogenesis model as implemented in the R package BioGeoBEARS and found evidence for a Gondwanan origin of the clade. One tropical disjunction (Monstera Adans. sister to Amydrium Schott–Epipremnum Schott) was found to be the product of a previous Boreotropical distribution. Two other disjunctions are more recent and likely due to long-distance dispersal: Spathiphyllum Schott (with Holochlamys Engl. nested within) represents a dispersal from South America to the Pacific Islands in Southeast Asia, and Rhaphidophora represents a dispersal from Asia to Africa. Future studies based on stronger phylogenetic reconstructions and complete morphological datasets are needed to explore the details of speciation and migration within and among areas in Asia.

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