David Ebert – författare

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.FeaturesContains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercisesDescribes statistical concepts clearly and concisely before applying them in RIncludes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

This guide is the only complete reference to the sharks, rays, and chimaeras found in California's waters - from the intertidal zone to 500 miles offshore. Species accounts give information on habitat and range, natural history, interactions with humans, nomenclature, and further references. 68 beautiful color illustrations show each shark, ray, and chimaera; accompanying line drawings highlight differences in teeth, underside of the head, and egg cases. It includes information on the California marine environment, ecology and conservation, and shark biology.

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide.

The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.

Features

Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises Describes statistical concepts clearly and concisely before applying them in R Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide.

The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.

Features

Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises Describes statistical concepts clearly and concisely before applying them in R Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book