The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

ShiVa Example

Introduction

This vignette demonstrates how to use the ShiVa R package to detect evolutionary shifts in both optimal trait values (mean) and evolutionary variance under an Ornstein-Uhlenbeck (OU) model. We illustrate the process using a floral trait dataset from Euphorbiaceae species. It is available at phylolm.

library(ShiVa)
library(phylolm)
#> Warning: package 'phylolm' was built under R version 4.4.3
#> Loading required package: ape
#> Warning: package 'ape' was built under R version 4.4.3

Setup

Load the required packages:

data('flowerTree')
data('flowerSize')

Load example data

We load the phylogenetic tree and trait data. The trait is floral diameter, log-transformed.

Y = flowerSize$log_transformed_size
names(Y) = rownames(flowerSize)
tree = flowerTree
# normalize the tree
tree$edge.length = flowerTree$edge.length/max(node.depth.edgelength(flowerTree))

Run ShiVa

set.seed(111)
result = ShiVa(Y,tree, lambda.type = "lambda.min")
#> ====== Model Selection Round 1 ======
#> Trying lambda2 = 0.003697864 ...
#> [1] "233 steps to converge"
#> Selected lambda1 from CV: 1.127226 
#> [1] "204 steps to converge"
#>   shifts_mean = 6 
#>   shifts_var  = 7,15,16,29,31,37,41,44 
#>   log-likelihood = 10.83296 
#> 
#> ====== Model Selection Round 2 ======
#> Trying lambda2 = 0.005516564 ...
#> [1] "241 steps to converge"
#> Selected lambda1 from CV: 0.5590295 
#> [1] "247 steps to converge"
#>   shifts_mean = 1,6,7 
#>   shifts_var  = 7,15,31,37,41,44 
#>   log-likelihood = 19.75432 
#> 
#> ====== Model Selection Round 3 ======
#> Trying lambda2 = 0.008229747 ...
#> [1] "256 steps to converge"
#> Selected lambda1 from CV: 0.4852793 
#>   shifts_mean = 1,6,7 
#>   shifts_var  = 3,9,15,31,37,41,44 
#>   log-likelihood = 24.40707 
#> 
#> ====== Model Selection Round 4 ======
#> Trying lambda2 = 0.01227734 ...
#> [1] "297 steps to converge"
#> Selected lambda1 from CV: 1.129127 
#> [1] "282 steps to converge"
#>   shifts_mean = 6 
#>   shifts_var  = 7,15,31,37,41,44 
#>   log-likelihood = 8.136406 
#> 
#> ====== Model Selection Round 5 ======
#> Trying lambda2 = 0.01831564 ...
#> Selected lambda1 from CV: 0.7391688 
#> [1] "82 steps to converge"
#>   shifts_mean = 6,7 
#>   shifts_var  = 7,31 
#>   log-likelihood = 3.934407 
#> 
#> ====== Model Selection Round 6 ======
#> Trying lambda2 = 0.02732372 ...
#> [1] "128 steps to converge"
#> Selected lambda1 from CV: 0.7806718 
#> [1] "19 steps to converge"
#>   shifts_mean = 6,7 
#>   shifts_var  = none 
#>   log-likelihood = -3.42772 
#> 
#> ====== Model Selection Round 7 ======
#> Trying lambda2 = 0.0407622 ...
#> [1] "9 steps to converge"
#> Selected lambda1 from CV: 0.8721472 
#> [1] "22 steps to converge"
#>   shifts_mean = 6,7 
#>   shifts_var  = none 
#>   log-likelihood = -3.42772 
#> 
#> ====== Model Selection Round 8 ======
#> Trying lambda2 = 0.06081006 ...
#> [1] "12 steps to converge"
#> Selected lambda1 from CV: 0.8572609 
#> [1] "14 steps to converge"
#>   shifts_mean = 6,7 
#>   shifts_var  = none 
#>   log-likelihood = -3.42772 
#> 
#> ====== Model Selection Round 9 ======
#> Trying lambda2 = 0.09071795 ...
#> [1] "12 steps to converge"
#> Selected lambda1 from CV: 0.8570856 
#> [1] "14 steps to converge"
#>   shifts_mean = 6,7 
#>   shifts_var  = none 
#>   log-likelihood = -3.42772 
#> 
#> ====== Model Selection Round 10 ======
#> Trying lambda2 = 0.1353353 ...
#> [1] "12 steps to converge"
#> Selected lambda1 from CV: 0.8570856 
#> [1] "14 steps to converge"
#>   shifts_mean = 6,7 
#>   shifts_var  = none 
#>   log-likelihood = -3.42772 
#> 
#> 
#> ====== Backward Correction (Top 10 ) ======
#> Correcting model 1 with shifts_mean = 1,6,7 shifts_var = 3,9,15,31,37,41,44 ...
#> Correcting model 2 with shifts_mean = 6,7 shifts_var = 7,31 ...
#> Correcting model 3 with shifts_mean = 1,6,7 shifts_var = 7,15,31,37,41,44 ...
#> Correcting model 4 with shifts_mean = 6,7 shifts_var =  ...
#> Correcting model 5 with shifts_mean = 6 shifts_var = 7,15,31,37,41,44 ...
#> Correcting model 6 with shifts_mean = 6 shifts_var = 7,15,16,29,31,37,41,44 ...
#> ====== Selection Finished. Best BIC = 7.875518 ======

Visualize Detected Shifts

plot(result$best_model,title = "ShiVa")

Summarize Shifts

print(summary(result$best_model))
#> $alpha
#> [1] 1.1e-07
#> 
#> $sigma2
#> [1] 0.0067
#> 
#> $loglik
#> [1] 20.2
#> 
#> $BIC
#> [1] 7.88
#> 
#> $mean_shifts
#>   edge node      size
#> 1    6   29 0.2668755
#> 2    7   30 4.1463832
#> 
#> $var_shifts
#>   edge node      size
#> 1    9   31 0.1624866
#> 2   15   35 0.4933164
#> 3   37   46 0.1293234
#> 4   44   49 0.3449323
#> 
#> attr(,"class")
#> [1] "summary.ShiVaShiftModel"

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.