compara_methods
ajpelu
2022-02-02
Last updated: 2022-04-01
Checks: 7 0
Knit directory: veg_alcontar/
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| Rmd | a6c71fa | ajpelu | 2022-04-01 | add analysis_SUDOE cobertura |
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| Rmd | 179f390 | ajpelu | 2022-02-02 | genera plots compara metodos |
Introduction
Comparison of estimation methods for coverage, phytovolume, richness and diversity (shannon)
- Prepara data
Cobertura
- Summary values
| metodo | mean | sd | se | cv | median | n |
|---|---|---|---|---|---|---|
| quadrat | 29.92 | 18.47 | 1.89 | 61.75 | 26.50 | 96 |
| dronQ | 23.55 | 21.01 | 2.14 | 89.23 | 15.74 | 96 |
| line_intercept | 27.19 | 6.49 | 1.87 | 23.86 | 29.67 | 12 |
| point_quadrat | 55.50 | 7.62 | 2.20 | 13.73 | 56.00 | 12 |
| dronT | 17.46 | 2.71 | 0.78 | 15.51 | 17.07 | 12 |
Modelo
- Aplicamos un modelo de Kruskal-Wallis con comparaciones post-hoc aplicando test de Dunn (correcciones de Bonferroni).
- Los resultados son los siguientes:
| statistic | p.value | parameter | method | mi_variable |
|---|---|---|---|---|
| 36.66969 | 2e-07 | 4 | Kruskal-Wallis rank sum test | cobertura |
- Posteriormente computamos las pruebas post-hoc
| H0 | statistic | p.value |
|---|---|---|
| quadrat = dronQ | 3.49 | 0.0048 |
| quadrat = line_intercept | 0.43 | 1.0000 |
| quadrat = point_quadrat | 3.63 | 0.0028 |
| quadrat = dronT | 2.02 | 0.4370 |
| dronQ = line_intercept | 2.08 | 0.3796 |
| dronQ = point_quadrat | 5.28 | 0.0000 |
| dronQ = dronT | 0.37 | 1.0000 |
| line_intercept = point_quadrat | 2.40 | 0.1633 |
| line_intercept = dronT | 1.84 | 0.6648 |
| point_quadrat = dronT | 4.24 | 0.0002 |
Coeficiente de Variación
Analizamos los datos de CV, si son diferentes significativamente. Aplicamos el test MSLRT (Modified signed-likelihood ratio test) para cada uno de los pares de métodos.
| V1 | V2 | MSLRT | p_value |
|---|---|---|---|
| quadrat | dronQ | 6.13 | 0.01326 |
| quadrat | line_intercept | 9.22 | 0.00240 |
| quadrat | point_quadrat | 19.22 | 0.00001 |
| quadrat | dronT | 16.88 | 0.00004 |
| dronQ | line_intercept | 13.98 | 0.00019 |
| dronQ | point_quadrat | 24.60 | 0.00000 |
| dronQ | dronT | 22.16 | 0.00000 |
| line_intercept | point_quadrat | 2.88 | 0.08977 |
| line_intercept | dronT | 1.77 | 0.18398 |
| point_quadrat | dronT | 0.14 | 0.70956 |
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] multcompView_0.1-8 PMCMRplus_1.9.3 PMCMR_4.3 statmod_1.4.36
[5] tweedie_2.3.3 report_0.3.0 kableExtra_1.3.1 cvequality_0.2.0
[9] performance_0.8.0 ggdist_3.0.1 Metrics_0.1.4 ggstatsplot_0.7.2
[13] colorspace_2.0-2 ggforce_0.3.2 ggdark_0.2.1 janitor_2.1.0
[17] here_1.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6
[21] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2
[25] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 pairwiseComparisons_3.1.3
[3] backports_1.2.1 systemfonts_1.0.0
[5] plyr_1.8.6 splines_4.0.2
[7] gmp_0.6-2 kSamples_1.2-9
[9] ipmisc_5.0.2 TH.data_1.0-10
[11] digest_0.6.27 SuppDists_1.1-9.5
[13] htmltools_0.5.2 fansi_0.4.2
[15] magrittr_2.0.1 memoise_2.0.0
[17] paletteer_1.3.0 modelr_0.1.8
[19] sandwich_3.0-0 rvest_1.0.0
[21] ggrepel_0.9.1 textshaping_0.3.2
[23] haven_2.3.1 xfun_0.23
[25] prismatic_1.0.0 callr_3.7.0
[27] crayon_1.4.1 jsonlite_1.7.2
[29] zeallot_0.1.0 survival_3.2-7
[31] zoo_1.8-8 glue_1.4.2
[33] polyclip_1.10-0 gtable_0.3.0
[35] emmeans_1.5.4 webshot_0.5.2
[37] MatrixModels_0.4-1 statsExpressions_1.1.0
[39] distributional_0.3.0 Rmpfr_0.8-2
[41] scales_1.1.1.9000 mvtnorm_1.1-1
[43] DBI_1.1.1 Rcpp_1.0.7
[45] viridisLite_0.4.0 xtable_1.8-4
[47] httr_1.4.2 ellipsis_0.3.2
[49] pkgconfig_2.0.3 reshape_0.8.8
[51] farver_2.1.0 sass_0.3.1
[53] dbplyr_2.1.1 utf8_1.1.4
[55] labeling_0.4.2 tidyselect_1.1.1
[57] rlang_0.4.12 later_1.1.0.1
[59] ggcorrplot_0.1.3 effectsize_0.4.5
[61] munsell_0.5.0 cellranger_1.1.0
[63] tools_4.0.2 cachem_1.0.4
[65] cli_2.5.0 generics_0.1.0
[67] broom_0.7.9 evaluate_0.14
[69] fastmap_1.1.0 ragg_1.1.1
[71] BWStest_0.2.2 yaml_2.2.1
[73] rematch2_2.1.2 processx_3.5.1
[75] knitr_1.31 fs_1.5.0
[77] WRS2_1.1-1 pbapply_1.4-3
[79] whisker_0.4 xml2_1.3.2
[81] correlation_0.6.1 compiler_4.0.2
[83] rstudioapi_0.13 ggsignif_0.6.0
[85] reprex_2.0.0 tweenr_1.0.1
[87] bslib_0.2.4 stringi_1.7.4
[89] highr_0.8 ps_1.5.0
[91] parameters_0.14.0 lattice_0.20-41
[93] Matrix_1.3-2 vctrs_0.3.8
[95] pillar_1.6.1 lifecycle_1.0.1
[97] mc2d_0.1-18 jquerylib_0.1.3
[99] estimability_1.3 insight_0.14.4
[101] httpuv_1.5.5 patchwork_1.1.1
[103] R6_2.5.1 promises_1.2.0.1
[105] BayesFactor_0.9.12-4.2 codetools_0.2-18
[107] MASS_7.3-53 gtools_3.8.2
[109] assertthat_0.2.1 rprojroot_2.0.2
[111] withr_2.4.1 multcomp_1.4-16
[113] bayestestR_0.9.0 parallel_4.0.2
[115] hms_1.0.0 grid_4.0.2
[117] coda_0.19-4 rmarkdown_2.8
[119] snakecase_0.11.0 git2r_0.28.0
[121] getPass_0.2-2 lubridate_1.7.10