Last updated: 2022-01-14

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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