Last updated: 2022-04-01

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Introduction

Comparison of estimation methods for coverage, phytovolume, richness and diversity (shannon)

  • Prepara data

Cobertura

  • Summary values
Cobertura || Summary
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
Cobertura || Non-parametric Kruskal-Wallis ANOVA - Post-hoc Dunn’s-test with Bonferroni adjustment
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

Version Author Date
b4ebf2e ajpelu 2022-04-01

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.

Cobertura || Pairwise Modified signed-likelihood ratio test (SLRT) for equality of CVs
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

Fitovolumen

  • Summary values
Fitovolumen || Summary
metodo mean sd se cv median n
quadrat 778.57 1108.78 113.16 142.41 323.40 96
dronQ 421.70 678.47 69.25 160.89 125.90 96
line_intercept 531.04 274.90 79.36 51.77 543.13 12
dronT 450.63 141.74 40.92 31.45 408.39 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
23.40999 3.32e-05 3 Kruskal-Wallis rank sum test fitovolumen
  • Posteriormente computamos las pruebas post-hoc
Fitovolumen || Non-parametric Kruskal-Wallis ANOVA - Post-hoc Dunn’s-test with Bonferroni adjustment
H0 statistic p.value
quadrat = dronQ 4.20 0.0002
quadrat = line_intercept 0.74 1.0000
quadrat = dronT 0.68 1.0000
dronQ = line_intercept 2.72 0.0397
dronQ = dronT 2.66 0.0464
line_intercept = dronT 0.04 1.0000

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.

Fitovolumen || Pairwise Modified signed-likelihood ratio test (SLRT) for equality of CVs
V1 V2 MSLRT p_value
quadrat dronQ 0.27 0.60538
quadrat line_intercept 4.69 0.03041
quadrat dronT 10.28 0.00135
dronQ line_intercept 4.89 0.02694
dronQ dronT 9.99 0.00158
line_intercept dronT 1.94 0.16351

Richness

  • Summary values
Richness || Summary
metodo mean sd se cv median n
quadrat 10.57 3.89 0.40 36.77 10.0 96
line_intercept 13.00 3.10 0.90 23.88 13.0 12
point_quadrat 13.17 4.24 1.22 32.20 14.0 12
point_quadrat_extenso 31.50 7.99 2.31 25.38 29.5 12
quadrat_parcela 34.08 10.39 3.00 30.48 34.5 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
64.59165 0 4 Kruskal-Wallis rank sum test riqueza
  • Posteriormente computamos las pruebas post-hoc
Riqueza || Non-parametric Kruskal-Wallis ANOVA - Post-hoc Dunn’s-test with Bonferroni adjustment
H0 statistic p.value
quadrat = line_intercept 1.82 0.6853
quadrat = point_quadrat 1.68 0.9341
quadrat = point_quadrat_extenso 5.90 0.0000
quadrat = quadrat_parcela 6.02 0.0000
line_intercept = point_quadrat 0.11 1.0000
line_intercept = point_quadrat_extenso 3.06 0.0221
line_intercept = quadrat_parcela 3.15 0.0163
point_quadrat = point_quadrat_extenso 3.17 0.0153
point_quadrat = quadrat_parcela 3.26 0.0112
point_quadrat_extenso = quadrat_parcela 0.09 1.0000

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.

Riqueza || Pairwise Modified signed-likelihood ratio test (SLRT) for equality of CVs
V1 V2 MSLRT p_value
quadrat line_intercept 2.82 0.09335
quadrat point_quadrat 0.40 0.52492
quadrat point_quadrat_extenso 2.18 0.14018
quadrat quadrat_parcela 0.70 0.40309
line_intercept point_quadrat 0.80 0.37022
line_intercept point_quadrat_extenso 0.02 0.87866
line_intercept quadrat_parcela 0.54 0.46359
point_quadrat point_quadrat_extenso 0.50 0.47807
point_quadrat quadrat_parcela 0.01 0.90371
point_quadrat_extenso quadrat_parcela 0.30 0.58628

Shannon

  • Summary values
Richness || Summary
metodo mean sd se cv median n
quadrat 1.34 0.56 0.06 41.72 1.37 96
line_intercept 1.72 0.49 0.14 28.71 1.63 12
point_quadrat 1.99 0.45 0.13 22.82 1.95 12

Modelo

  • Aplicamos un modelo de ANOVA con comparaciones post-hoc aplicando test de Dunn (correcciones de Bonferroni).
  • Los resultados son los siguientes:
OK: There is not clear evidence for different variances across groups (Bartlett Test, p = 0.611).
OK: residuals appear as normally distributed (p = 0.168).
Parameter Sum_Squares df Mean_Square F p Eta2 Eta2_CI_low Eta2_CI_high
metodo 5.41 2 2.71 9.09 0.00021 0.134 0.046 0.227
Residuals 34.83 117 0.30 
The ANOVA (formula: value ~ metodo) suggests that:

  - The main effect of metodo is statistically significant and medium (F(2, 117) = 9.09, p < .001; Eta2 = 0.13, 90% CI [0.05, 0.23])

Effect sizes were labelled following Field's (2013) recommendations.
  • Posteriormente computamos las pruebas post-hoc
Diversidad || ANOVA - Post-hoc Bonferroni adjustment
contrast estimate SE df t.ratio p.value
quadrat - line_intercept -0.378 0.167 117 -2.27 0.0760
quadrat - point_quadrat -0.642 0.167 117 -3.84 0.0006
line_intercept - point_quadrat -0.263 0.223 117 -1.18 0.7181

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.

Diversidad || Pairwise Modified signed-likelihood ratio test (SLRT) for equality of CVs
V1 V2 MSLRT p_value
quadrat line_intercept 2.13 0.14465
quadrat point_quadrat 4.81 0.02832
line_intercept point_quadrat 0.48 0.48795 

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