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%” |
0.3 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
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 |
0.4.1 Correlación general vs. Riqueza
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] systemfonts_1.0.0 plyr_1.8.6
[5] splines_4.0.2 gmp_0.6-2
[7] kSamples_1.2-9 ipmisc_5.0.2
[9] TH.data_1.0-10 digest_0.6.27
[11] SuppDists_1.1-9.5 htmltools_0.5.2
[13] fansi_0.4.2 magrittr_2.0.1
[15] memoise_2.0.0 paletteer_1.3.0
[17] openxlsx_4.2.3 sandwich_3.0-0
[19] colorspace_2.0-2 rvest_1.0.0
[21] ggrepel_0.9.1 textshaping_0.3.2
[23] haven_2.3.1 xfun_0.23
[25] callr_3.7.0 crayon_1.4.1
[27] jsonlite_1.7.2 zeallot_0.1.0
[29] survival_3.2-7 zoo_1.8-8
[31] glue_1.4.2 gtable_0.3.0
[33] emmeans_1.5.4 webshot_0.5.2
[35] MatrixModels_0.4-1 statsExpressions_1.1.0
[37] car_3.0-10 Rmpfr_0.8-2
[39] abind_1.4-5 scales_1.1.1.9000
[41] mvtnorm_1.1-1 DBI_1.1.1
[43] rstatix_0.6.0 PMCMRplus_1.9.0
[45] miniUI_0.1.1.1 Rcpp_1.0.7
[47] viridisLite_0.4.0 xtable_1.8-4
[49] performance_0.8.0 gridtext_0.1.4
[51] foreign_0.8-81 httr_1.4.2
[53] ellipsis_0.3.2 farver_2.1.0
[55] pkgconfig_2.0.3 reshape_0.8.8
[57] multcompView_0.1-8 sass_0.3.1
[59] dbplyr_2.1.1 utf8_1.1.4
[61] labeling_0.4.2 tidyselect_1.1.1
[63] rlang_0.4.12 later_1.1.0.1
[65] ggcorrplot_0.1.3 effectsize_0.4.5
[67] munsell_0.5.0 cellranger_1.1.0
[69] tools_4.0.2 cachem_1.0.4
[71] cli_2.5.0 generics_0.1.0
[73] broom_0.7.9 evaluate_0.14
[75] fastmap_1.1.0 ragg_1.1.1
[77] BWStest_0.2.2 yaml_2.2.1
[79] rematch2_2.1.2 processx_3.5.1
[81] knitr_1.31 fs_1.5.0
[83] zip_2.1.1 nlme_3.1-152
[85] WRS2_1.1-1 pbapply_1.4-3
[87] mime_0.10 whisker_0.4
[89] ggExtra_0.9 xml2_1.3.2
[91] correlation_0.6.1 compiler_4.0.2
[93] rstudioapi_0.13 curl_4.3
[95] ggsignif_0.6.0 reprex_2.0.0
[97] bslib_0.2.4 stringi_1.7.4
[99] highr_0.8 ps_1.5.0
[101] parameters_0.14.0 lattice_0.20-41
[103] Matrix_1.3-2 markdown_1.1
[105] vctrs_0.3.8 pillar_1.6.1
[107] lifecycle_1.0.1 mc2d_0.1-18
[109] jquerylib_0.1.3 estimability_1.3
[111] data.table_1.14.0 insight_0.14.4
[113] httpuv_1.5.5 patchwork_1.1.1
[115] R6_2.5.1 bookdown_0.21.6
[117] promises_1.2.0.1 rio_0.5.16
[119] BayesFactor_0.9.12-4.2 codetools_0.2-18
[121] MASS_7.3-53 gtools_3.8.2
[123] assertthat_0.2.1 rprojroot_2.0.2
[125] withr_2.4.1 multcomp_1.4-16
[127] mgcv_1.8-33 bayestestR_0.9.0
[129] parallel_4.0.2 hms_1.0.0
[131] grid_4.0.2 coda_0.19-4
[133] rmarkdown_2.8 carData_3.0-4
[135] git2r_0.28.0 getPass_0.2-2
[137] shiny_1.6.0 lubridate_1.7.10