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:bird: (retired) Morphometrics using R

Momocs 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Momocs <a href="http://momx.github.io/Momocs/"><img src="man/figures/logo.png" align="right" height="139" alt="Momocs website" /></a>

[![Lifecycle:
superseded](https://img.shields.io/badge/lifecycle-superseded-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html#superseded)
[![CRAN
status](https://www.r-pkg.org/badges/version/Momocs)](https://CRAN.R-project.org/package=Momocs)

### News

- Momocs is now retiring and, one day, will no longer be supported in
  favour of Momocs2 and the entire MomX ecosystem. (as of Jan 26)
- I’m available for consulting, training and collaboration, worldwide.
- Momocs is back on CRAN and no longer relies on the retired `rgeos`
  dependency
- The tutorial/introduction is back! Download it
  [there](https://github.com/MomX/Momocs/releases/download/v1.4.0/Momocs_intro.html)

### Installation

The last released version can be installed from
[CRAN](https://CRAN.R-project.org/package=Momocs) with:

``` r
install.packages("Momocs")
```

But I recommend using (and only support) the development version from
GitHub with:

``` r
# install.packages("devtools")
devtools::install_github("MomX/Momocs")
```

### Example

This is a basic example of a complete analysis doing: inspection,
normalization of raw outlines, elliptical Fourier transforms,
dimmensionality reduction and classification, using a single line.

``` r
library(Momocs)
```

``` r
devtools::load_all()
#> ℹ Loading Momocs
#> Registered S3 method overwritten by 'vegan':
#>   method     from      
#>   rev.hclust dendextend
```

``` r
hearts %T>%                    # A toy dataset
  stack() %>%                  # Take a family picture of raw outlines
  fgProcrustes() %>%           # Full generalized Procrustes alignment
  coo_slide(ldk = 2) %T>%      # Redefine a robust 1st point between the cheeks
  stack() %>%                  # Another picture of aligned outlines
  efourier(6, norm=FALSE) %>%  # Elliptical Fourier Transforms
  PCA() %T>%                   # Principal Component Analysis
  plot_PCA(~aut) %>%           # A PC1:2 plot
  LDA(~aut) %>%                # Linear Discriminant Analysis
  plot_CV()                    # And the confusion matrix after leave one out cross validation
```

![](https://raw.githubusercontent.com/MomX/Momocs/master/README-example-1.png)![](https://raw.githubusercontent.com/MomX/Momocs/master/README-example-2.png)![](https://raw.githubusercontent.com/MomX/Momocs/master/README-example-3.png)![](https://raw.githubusercontent.com/MomX/Momocs/master/README-example-4.png)

Momocs

Momocs 최신버젼 다운로드

오늘의 추천 프로젝트

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Momocs

Momocs 최신버젼 다운로드

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