Prepare analysis and figures for VIII Spanish Forest Meeting
ajpelu
2022-01-13
Last updated: 2022-01-14
Checks: 7 0
Knit directory:
dronveg_alcontar/
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0.1 Prepara Datos
Usamos datos de cobertura vegetal de las parcelas de campo (cob.campo) y datos derivados de dron (cob.dron).
De los datos de dron, utilizamos el método de estimación denominado COBERTURA (ver análisis preliminar)
Los datos de campo corresponden al muestreo realizado el 19/05/2021.
El vuelo del dron se realizó el día 21/05/2021.
Los rangos de cobertura se han reclasifiaco de acuerdo a:
| RANGO_INFOCA | Nombre | Cobertura |
|---|---|---|
| 1 | “Matorral claro” | “<25%” |
| 2 | “Matorral medio” | “25-50%” |
| 3 | “Espartal denso” | “>75%” |
| 4 | “Aulagar denso” | “>75%” |
0.2 Correlación General
| Version | Author | Date |
|---|---|---|
| ed90502 | ajpelu | 2022-01-14 |
| Version | Author | Date |
|---|---|---|
| ed90502 | ajpelu | 2022-01-14 |
0.3 Correlación por Rangos
Explorar como varía la correlación en los diferentes rangos de cobertura
Computar el RMSE, y el RMSE normalizado. El RMSE es dependiente de la escala, por lo que necesitaríamos normalizar para poder comparar entre las clases de cobertura.
| Rango de cobertura | RMSE | min | max | norm. RMSE % |
|---|---|---|---|---|
| Aulagar denso (>75%) | 12.69 | 10 | 61 | 24.89 |
| Espartal denso (>75%) | 7.63 | 9 | 87 | 9.78 |
| Matorral claro (<25%) | 7.30 | 6 | 28 | 33.18 |
| Matorral medio (25-50%) | 10.89 | 9 | 63 | 20.16 |
- Generamos las ecuaciones para la gráfica
| Version | Author | Date |
|---|---|---|
| ed90502 | ajpelu | 2022-01-14 |
0.4 Influencia de otras variables en la Variación de la correlación
¿Existe alguna relación entre la correlación y otras variables? Podría interesarnos explorar cómo otras variables podrían influir en la correlación dron-campo, por ejemplo la riqueza o la pendiente. Se pueden utilizar varios enfoques (análisis exploratorio, residuos, etc.). En nuestro caso utilizamos la correlación entre los residuos de la correlación y las diferentes variables.
- Calculamos los residuos y los residuos absolutos
m <- lm(cob.dron ~ cob.campo, data=df)
df <- df %>% modelr::add_residuals(m) %>%
mutate(resid.abs = abs(resid))
dfres <- df %>% dplyr::select(coverclass, Diversidad = shannon, Riqueza = rich, Pendiente = slope, resid, resid.abs) %>%
pivot_longer(cols = c("Diversidad", "Riqueza", "Pendiente")) %>%
mutate(variable = fct_relevel(name, c("Diversidad", "Riqueza", "Pendiente")))- Hacemos gráfico de las tres variables
| Version | Author | Date |
|---|---|---|
| ed90502 | ajpelu | 2022-01-14 |
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] modelr_0.1.8 ggpubr_0.4.0 ggtext_0.1.1 kableExtra_1.3.1
[5] Metrics_0.1.4 ggstatsplot_0.7.2 readxl_1.3.1 here_1.0.1
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] pairwiseComparisons_3.1.3 backports_1.2.1
[3] plyr_1.8.6 splines_4.0.2
[5] gmp_0.6-2 kSamples_1.2-9
[7] ipmisc_5.0.2 TH.data_1.0-10
[9] digest_0.6.27 SuppDists_1.1-9.5
[11] htmltools_0.5.2 fansi_0.4.2
[13] magrittr_2.0.1 memoise_2.0.0
[15] paletteer_1.3.0 openxlsx_4.2.3
[17] sandwich_3.0-0 colorspace_2.0-2
[19] rvest_1.0.0 ggrepel_0.9.1
[21] haven_2.3.1 xfun_0.23
[23] callr_3.7.0 crayon_1.4.1
[25] jsonlite_1.7.2 zeallot_0.1.0
[27] survival_3.2-7 zoo_1.8-8
[29] glue_1.4.2 gtable_0.3.0
[31] emmeans_1.5.4 webshot_0.5.2
[33] MatrixModels_0.4-1 statsExpressions_1.1.0
[35] car_3.0-10 Rmpfr_0.8-2
[37] abind_1.4-5 scales_1.1.1.9000
[39] mvtnorm_1.1-1 DBI_1.1.1
[41] rstatix_0.6.0 PMCMRplus_1.9.0
[43] miniUI_0.1.1.1 Rcpp_1.0.7
[45] viridisLite_0.4.0 xtable_1.8-4
[47] performance_0.8.0 gridtext_0.1.4
[49] foreign_0.8-81 httr_1.4.2
[51] ellipsis_0.3.2 farver_2.1.0
[53] pkgconfig_2.0.3 reshape_0.8.8
[55] multcompView_0.1-8 sass_0.3.1
[57] dbplyr_2.1.1 utf8_1.1.4
[59] labeling_0.4.2 tidyselect_1.1.1
[61] rlang_0.4.12 later_1.1.0.1
[63] ggcorrplot_0.1.3 effectsize_0.4.5
[65] munsell_0.5.0 cellranger_1.1.0
[67] tools_4.0.2 cachem_1.0.4
[69] cli_2.5.0 generics_0.1.0
[71] broom_0.7.9 evaluate_0.14
[73] fastmap_1.1.0 BWStest_0.2.2
[75] yaml_2.2.1 rematch2_2.1.2
[77] processx_3.5.1 knitr_1.31
[79] fs_1.5.0 zip_2.1.1
[81] nlme_3.1-152 WRS2_1.1-1
[83] pbapply_1.4-3 mime_0.10
[85] whisker_0.4 ggExtra_0.9
[87] xml2_1.3.2 correlation_0.6.1
[89] compiler_4.0.2 rstudioapi_0.13
[91] curl_4.3 ggsignif_0.6.0
[93] reprex_2.0.0 bslib_0.2.4
[95] stringi_1.7.4 highr_0.8
[97] ps_1.5.0 parameters_0.14.0
[99] lattice_0.20-41 Matrix_1.3-2
[101] markdown_1.1 vctrs_0.3.8
[103] pillar_1.6.1 lifecycle_1.0.1
[105] mc2d_0.1-18 jquerylib_0.1.3
[107] estimability_1.3 data.table_1.14.0
[109] insight_0.14.4 httpuv_1.5.5
[111] patchwork_1.1.1 R6_2.5.1
[113] bookdown_0.21.6 promises_1.2.0.1
[115] rio_0.5.16 BayesFactor_0.9.12-4.2
[117] codetools_0.2-18 MASS_7.3-53
[119] gtools_3.8.2 assertthat_0.2.1
[121] rprojroot_2.0.2 withr_2.4.1
[123] multcomp_1.4-16 mgcv_1.8-33
[125] bayestestR_0.9.0 parallel_4.0.2
[127] hms_1.0.0 grid_4.0.2
[129] coda_0.19-4 rmarkdown_2.8
[131] carData_3.0-4 git2r_0.28.0
[133] getPass_0.2-2 shiny_1.6.0
[135] lubridate_1.7.10