Testing Graphical Causal Models Using the R Package “dagitty”

Ankur Ankan; Inge M. N. Wortel; Johannes Textor

doi:10.1002/cpz1.45

phylopath: Easy phylogenetic path analysis in R

PeerJ ◽

10.7717/peerj.4718 ◽

2018 ◽

Vol 6 ◽

pp. e4718 ◽

Cited By ~ 34

Author(s):

Wouter van der Bijl

Keyword(s):

Experimental Data ◽

Path Analysis ◽

Observational Data ◽

Brain Size ◽

R Package ◽

Causal Models ◽

Binary Variables ◽

The Relationship

Confirmatory path analysis allows researchers to evaluate and compare causal models using observational data. This tool has great value for comparative biologists since they are often unable to gather experimental data on macro-evolutionary hypotheses, but is cumbersome and error-prone to perform. I introducephylopath, an R package that implements phylogenetic path analysis (PPA) as described by von Hardenberg & Gonzalez-Voyer (2013). In addition to the published method, I provide support for the inclusion of binary variables. I illustrate PPA andphylopathby recreating part of a study on the relationship between brain size and vulnerability to extinction. The package aims to make the analysis straight-forward, providing convenience functions, and several plotting methods, which I hope will encourage the spread of the method.

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A Program to Help Find Causal Models of Nonexperimental Data

Contemporary Psychology ◽

10.1037/030807 ◽

1989 ◽

Vol 34 (12) ◽

pp. 1086-1087

Author(s):

Charles F. Chubb

Keyword(s):

Causal Models

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Male and Female Causal Models of Pilot Skill Acquisition: A Preliminary Evaluation

PsycEXTRA Dataset ◽

10.1037/e443652005-001 ◽

1997 ◽

Author(s):

Thomas R. Carretta ◽

Malcolm James Ree

Keyword(s):

Skill Acquisition ◽

Causal Models ◽

Preliminary Evaluation ◽

Male And Female

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

Planta Medica ◽

10.1055/s-0036-1596648 ◽

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

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

10.32942/osf.io/epqa7 ◽

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.

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Faculty Opinions recommendation of TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725335383.793503769 ◽

2015 ◽

Author(s):

Zdeněk Valenta

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

R Package ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Targeted Drug

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

Annals of the Missouri Botanical Garden ◽

10.3417/2018269 ◽

2019 ◽

Vol 104 (1) ◽

pp. 33-48 ◽

Cited By ~ 4

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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