Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package
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arXiv
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866916394877059072 |
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| author | Duong, Tarn |
| author_facet | Duong, Tarn |
| contents | Kernel smoothers are essential tools for data analysis due to their ability to convey complex statistical information with concise graphical visualisations. Their inclusion in the base distribution and in the many user-contributed add-on packages of the R statistical analysis environment caters well to many practitioners. Though there remain some important gaps for specialised data, most notably for tidy and geospatial data. The proposed eks package fills in these gaps. In addition to kernel density estimation, this package also caters for more complex data analysis situations, such as density derivative estimation, density-based classification (supervised learning) and mean shift clustering (unsupervised learning). We illustrate with experimental data how to obtain and to interpret the statistical visualisations for these kernel smoothing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_01686 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package Duong, Tarn Computation Applications 62G07, 62G10, 62H12 Kernel smoothers are essential tools for data analysis due to their ability to convey complex statistical information with concise graphical visualisations. Their inclusion in the base distribution and in the many user-contributed add-on packages of the R statistical analysis environment caters well to many practitioners. Though there remain some important gaps for specialised data, most notably for tidy and geospatial data. The proposed eks package fills in these gaps. In addition to kernel density estimation, this package also caters for more complex data analysis situations, such as density derivative estimation, density-based classification (supervised learning) and mean shift clustering (unsupervised learning). We illustrate with experimental data how to obtain and to interpret the statistical visualisations for these kernel smoothing methods. |
| title | Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package |
| topic | Computation Applications 62G07, 62G10, 62H12 |
| url | https://arxiv.org/abs/2203.01686 |