Last updated: 2024-08-16
Checks: 7 0
Knit directory: muse/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200712)
was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 23e861e. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish
or
wflow_git_commit
). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: r_packages_4.3.3/
Ignored: r_packages_4.4.0/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/hclust.Rmd
) and HTML
(docs/hclust.html
) files. If you’ve configured a remote Git
repository (see ?wflow_git_remote
), click on the hyperlinks
in the table below to view the files as they were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 23e861e | Dave Tang | 2024-08-16 | Left-align column names |
html | 03b7e20 | Dave Tang | 2024-08-16 | Build site. |
Rmd | 5bf3bfd | Dave Tang | 2024-08-16 | Pros and cons of different linkage methods |
html | 850c62d | Dave Tang | 2024-08-16 | Build site. |
Rmd | ad9ead7 | Dave Tang | 2024-08-16 | Hierarchical clustering |
Hierarchical clustering is a bottom-up approach, by which similar observations and sub-classes are assembled iteratively. The order of the labels does not matter within sibling pairs. Horizontal distances are usually meaningless, while the vertical distances do encode some information. These properties are important to remember when making interpretations about neighbours that are not monophyletic (i.e., not in the same subtree or clade), but appear as neighbours in the plot.
An alternative, top-down, approach takes all the objects and splits them sequentially according to a chosen criterion. Such so-called recursive partitioning methods are often used to make decision trees. They can be useful for prediction: the goal is to split heterogeneous populations into more homogeneous subgroups by partitioning.
There are different choices of how to calculate distances between aggregates and each choice results in a different type of hierarchical clustering.
Method | Pros | Cons |
---|---|---|
Single linkage | Number of clusters | Comb-like trees |
Complete linkage | Compact classes | One observation can alter groups |
Average linkage | Similar size and variance | Not robust |
Centroid | Robust to outliers | Smaller number of clusters |
Ward’s | Minimises inertia | Classes small if high variability |
sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[9] ggplot2_3.5.1 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 stringi_1.8.4
[5] hms_1.1.3 digest_0.6.35 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_0.24.0 grid_4.4.0 fastmap_1.2.0 rprojroot_2.0.4
[13] jsonlite_1.8.8 processx_3.8.4 whisker_0.4.1 ps_1.7.6
[17] promises_1.3.0 httr_1.4.7 fansi_1.0.6 scales_1.3.0
[21] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.4 munsell_0.5.1
[25] withr_3.0.0 cachem_1.1.0 yaml_2.3.8 tools_4.4.0
[29] tzdb_0.4.0 colorspace_2.1-0 httpuv_1.6.15 vctrs_0.6.5
[33] R6_2.5.1 lifecycle_1.0.4 git2r_0.33.0 fs_1.6.4
[37] pkgconfig_2.0.3 callr_3.7.6 pillar_1.9.0 bslib_0.7.0
[41] later_1.3.2 gtable_0.3.5 glue_1.7.0 Rcpp_1.0.12
[45] xfun_0.44 tidyselect_1.2.1 rstudioapi_0.16.0 knitr_1.47
[49] htmltools_0.5.8.1 rmarkdown_2.27 compiler_4.4.0 getPass_0.2-4