For a more detailed tutorial on population structure and community structure inference please visit: https://xinghuq.github.io/DA/
This package implements five supervised learning approaches that are suitable for ecological and evolutionary inference both visually and statistically. Five approaches in our vignette: Linear discriminant analysis of principal components (DAPC), linear discriminant analysis of kernel principal components (DAKPC), local (Fisher) linear discriminant analysis (LFDA), local (Fisher) discriminant analysis of kernel principal components (LFDAKPC) and kernel local (Fisher) linear discriminant analysis (KLFDA).
Welcome any feedback and pull request.
library(devtools)
install_github("xinghuq/DA")
library("DA")x <- iris[,-5] # this matrix contains all the predictors to be transformed
y <- iris[,5] # this should be a vector that represents different classes
```{r fig1, fig.height = 5, fig.width = 10, fig.align = “center”} library(adegenet) iris_dapc=dapc(iris[,-5],grp=iris[,5],n.pca=3, n.da=3)
#plot the data projection on the components library(plotly) cols=rainbow(length(unique(iris[,5]))) p1 <- plot_ly(as.data.frame(iris_dapc\(ind.coord), x =iris_dapc\)ind.coord[,1], y =iris_dapc$ind.coord[,2], color = iris[,5],colors=cols[iris[,5]],symbol = iris[,5],symbols = 1:3L) %>% add_markers() %>% layout(scene = list(xaxis = list(title = ‘LDA1’), yaxis = list(title = ‘LDA2’))) p1
### Discriminant analysis of kernel principal components (DAKPC)
```{r fig2, fig.height = 5, fig.width = 10, fig.align = "center"}
iris_ldakpc=LDAKPC(iris[,-5],y=iris[,5],n.pc=3)
p2 <- plot_ly(as.data.frame(iris_ldakpc$LDs), x =iris_ldakpc$LDs[,1], y =iris_ldakpc$LDs[,2], color = iris[,5],colors=cols[iris[,5]],symbol = iris[,5],symbols = 1:3L) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'LDA1'),
yaxis = list(title = 'LDA2')))
p2```{r fig3, fig.height = 5, fig.width = 10, fig.align = “center”} iris_lfda=LFDA(iris[,-5],y=iris[,5],r=3,tol=1)
p3 <- plot_ly(as.data.frame(iris_lfda\(Z), x =iris_lfda\)Z[,1], y =iris_lfda$Z[,2], color = iris[,5],colors=cols[iris[,5]],symbol = iris[,5],symbols = 1:3L) %>% add_markers() %>% layout(scene = list(xaxis = list(title = ‘LDA1’), yaxis = list(title = ‘LDA2’))) p3
## Local (fisher) discriminant analysis of kernel principal components (LFDAKPC)
```{r fig4, fig.height = 5, fig.width = 10, fig.align = "center"}
iris_lfdakpc=LFDAKPC(iris[,-5],y=iris[,5],n.pc=3,tol=1)
p4 <- plot_ly(as.data.frame(iris_lfdakpc$$LFDAKPC$Z), x =iris_lfdakpc$$LFDAKPC$Z[,1], y =iris_lfdakpc$$LFDAKPC$Z[,2], color = iris[,5],colors=cols[iris[,5]],symbol = iris[,5],symbols = 1:3L) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'LDA1'),
yaxis = list(title = 'LDA2')))
p4
The default kernel is polydot(degree = 1, scale = 1, offset = 1). Users can set the kernel based on their own purpose.
```{r fig5, fig.height = 5, fig.width = 10, fig.align = “center”} iris_klfda=KLFDA(as.matrix(iris[,-5]),as.matrix(iris[,5]),r=3,tol=1E-10,prior = NULL)
p5 <- plot_ly(as.data.frame(iris_klfda\(Z), x =iris_klfda\)Z[,1], y =iris_klfda$Z[,2], color = iris[,5],colors=cols[iris[,5]],symbol = iris[,5],symbols = 1:3L) %>% add_markers() %>% layout(scene = list(xaxis = list(title = ‘LDA1’), yaxis = list(title = ‘LDA2’))) p5 ``` Note we did not show the discrimination results,users can look into the discriminated classes and their posterior possibility from the results.
Qin. X, Wu. M, Lock, T. R, Kallenbach, R. 2020. DA: Ecological and evolutionary inference using machine learning approaches both visually and statistically. R package version 1.2.0.