BetaStability with rioja datasets
This vignette demonstrates the BetaStability package using
linearPred with the Ponds dataset from the rioja
package.
Installation
First, install the package from GitHub:
# Install from GitHub
# install.packages("devtools")
# devtools::install_github("gaoyu19920914/betaStability")
# OR install from BioConductor (in the future when it's available)
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("betaStability")Loading Required Packages
Load the BetaStability package and the rioja package for test data:
library(betaStability)
library(vegan)
library(ggplot2)
# Check if rioja is available and install it to load dataset
if (!requireNamespace("rioja", quietly = TRUE)) install.packages("rioja")
data("Ponds", package = "rioja")rioja dataset with linearPred
comtable <- Ponds$spec
envmeta <- Ponds$env[, 6:19]
# Calculate stability with linearPred
stability_rioja_linear <- betaStability(
comtable = comtable,
envmeta = envmeta,
method = "linearPred"
)
# Inspect the result
head(stability_rioja_linear)
#> stability_Linear
#> 4 -0.03592404
#> 7 0.11670026
#> 31 0.06042562
#> 34 0.04890066
#> 37 0.03335611
#> 42 -0.08761045
p_rioja <- plotStability(stability_rioja_linear, sitenames = Ponds$env$Name)
p_rioja
print(sessionInfo())
#> R version 4.6.1 (2026-06-24 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: Asia/Shanghai
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.3 vegan_2.7-6 permute_0.9-10
#> [4] betaStability_0.1.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 shape_1.4.6.1 xfun_0.61
#> [4] bslib_0.12.0 raster_3.6-32 BBmisc_1.13.1
#> [7] lattice_0.23-1 vctrs_0.7.3 tools_4.6.1
#> [10] generics_0.1.4 parallel_4.6.1 tibble_3.3.1
#> [13] cluster_2.1.8.3 pkgconfig_2.0.3 Matrix_1.7-6
#> [16] data.table_1.18.6.1 checkmate_2.3.4 RColorBrewer_1.1-3
#> [19] S7_0.2.2 lifecycle_1.0.5 compiler_4.6.1
#> [22] farver_2.1.2 stringr_1.6.0 terra_1.9-50
#> [25] codetools_0.2-20 htmltools_0.5.9 sass_0.4.10
#> [28] yaml_2.3.12 glmnet_5.0 pillar_1.11.1
#> [31] jquerylib_0.1.4 MASS_7.3-66 rioja_1.0-7
#> [34] gdm_1.6.0-7 cachem_1.1.0 elevatr_0.99.1
#> [37] iterators_1.0.14 foreach_1.5.2 nlme_3.1-171
#> [40] tidyselect_1.2.1 digest_0.6.39 stringi_1.8.9
#> [43] dplyr_1.2.1 reshape2_1.4.5 purrr_1.2.2
#> [46] labeling_0.4.3 splines_4.6.1 fastmap_1.2.0
#> [49] grid_4.6.1 cli_3.6.6 magrittr_2.0.5
#> [52] randomForest_4.7-1.2 survival_3.8-12 withr_3.0.3
#> [55] scales_1.4.0 backports_1.5.1 xgboost_3.2.1.1
#> [58] sp_2.2-3 usedist_0.4.0 rmarkdown_2.32
#> [61] otel_0.2.0 progressr_1.0.0 pbapply_1.7-5
#> [64] evaluate_1.0.5 knitr_1.52 doParallel_1.0.17
#> [67] mgcv_1.9-4 rlang_1.3.0 Rcpp_1.1.2
#> [70] glue_1.8.1 rstudioapi_0.19.0 jsonlite_2.0.0
#> [73] R6_2.6.1 plyr_1.8.9