Last updated: 2021-09-17

Checks: 7 0

Knit directory: soil_alcontar/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20210907) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 3d973fc. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/C369E4F4/
    Ignored:    .Rproj.user/shared/notebooks/0EF54E14-NOBORRAR/
    Ignored:    .Rproj.user/shared/notebooks/27FF967E-analysis_pre_post_epoca/
    Ignored:    .Rproj.user/shared/notebooks/33E25F73-analysis_resilience/
    Ignored:    .Rproj.user/shared/notebooks/3F603CAC-map/
    Ignored:    .Rproj.user/shared/notebooks/4672E36C-study_area/
    Ignored:    .Rproj.user/shared/notebooks/4A68381F-general_overview_soils/
    Ignored:    .Rproj.user/shared/notebooks/4E13660A-temporal_comparison/
    Ignored:    .Rproj.user/shared/notebooks/5D919DFD-analysis_zona_time_postFire/
    Ignored:    .Rproj.user/shared/notebooks/827D0727-analysis_pre_post/
    Ignored:    .Rproj.user/shared/notebooks/A3F813C2-index/
    Ignored:    .Rproj.user/shared/notebooks/D4E3AA10-analysis_zona_time/

Untracked files:
    Untracked:  analysis/NOBORRAR.Rmd
    Untracked:  analysis/analysis_pre_post_cache/
    Untracked:  analysis/test.Rmd
    Untracked:  data/spatial/lucdeme/
    Untracked:  data/spatial/test/
    Untracked:  map.Rmd
    Untracked:  output/anovas_pre_post_epoca.csv
    Untracked:  output/anovas_zona_time.csv
    Untracked:  output/anovas_zona_time_postFire.csv
    Untracked:  output/meanboot_pre_post_epoca.csv
    Untracked:  scripts/generate_3dview.R

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   data/spatial/.DS_Store
    Modified:   data/spatial/01_EP_ANDALUCIA/EP_Andalucía.dbf
    Deleted:    index.Rmd
    Modified:   scripts/00_prepare_data.R
    Modified:   temporal_comparison.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/analysis_pre_post_epoca.Rmd) and HTML (docs/analysis_pre_post_epoca.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 3d973fc ajpelu 2021-09-17 wflow_publish(“analysis/analysis_pre_post_epoca.Rmd”)
html 419d019 ajpelu 2021-09-15 Build site.
Rmd 980ca0e ajpelu 2021-09-15 add full contrasts interaction
html 3307f05 ajpelu 2021-09-14 Build site.
Rmd 56e0949 ajpelu 2021-09-14 change fechaQuema por estacion; momento por
html 389b963 ajpelu 2021-09-14 Build site.
Rmd 9ba7a9d ajpelu 2021-09-14 add analysis by date Fire

Prepare data

raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"), 
    sheet = "SEGUIMIENTO_SUELOS_sin_ouliers") %>% janitor::clean_names() %>% mutate(treatment_name = case_when(str_detect(geo_parcela_nombre, 
    "NP_") ~ "Autumn Burning / No Browsing", str_detect(geo_parcela_nombre, "PR_") ~ 
    "Spring Burning / Browsing", str_detect(geo_parcela_nombre, "P_") ~ "Autumn Burning / Browsing"), 
    zona = case_when(str_detect(geo_parcela_nombre, "NP_") ~ "QOt_NP", str_detect(geo_parcela_nombre, 
        "PR_") ~ "QPr_P", str_detect(geo_parcela_nombre, "P_") ~ "QOt_P"), estacion = case_when(str_detect(geo_parcela_nombre, 
        "NP_") ~ "Ot", str_detect(geo_parcela_nombre, "PR_") ~ "Pr", str_detect(geo_parcela_nombre, 
        "P_") ~ "Ot"), date = lubridate::ymd(fecha), fecha = case_when(pre_post_quema == 
        "Prequema" ~ "0 preQuema", pre_post_quema == "Postquema" ~ "1 postQuema"))
  • Select data pre- and intermediately post-fire (first post-fire sampling: “2018-12-20” and “2019-05-09” for autumn and spring fires respectively)
soil <- raw_soil %>% filter(date %in% lubridate::ymd(c("2018-12-11", "2018-12-20", 
    "2019-04-24", "2019-05-09"))) %>% mutate(zona = as.factor(zona), fecha = as.factor(fecha))
  • Structure of the data
             estacion
fecha         Ot Pr
  0 preQuema  48 24
  1 postQuema 48 24

Modelize

  • For each response variable, the approach modelling is

\(Y \sim estacion (Ot|Pr) + fecha(pre|post) + estacion \times fecha\)

  • using the “(1|estacion:geo_parcela_nombre)” as nested random effects

  • Then explore error distribution of the variable response and model diagnostics

  • Select the appropiate error distribution and use LMM or GLMM

  • Explore Post-hoc

  • Plot interactions

Humedad

Model

Type III Analysis of Variance Table with Satterthwaite's method
               Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
fecha          461.74  461.74     1   130 50.4197 7.257e-11 ***
estacion         0.53    0.53     1    10  0.0575    0.8153    
fecha:estacion 439.95  439.95     1   130 48.0407 1.748e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                    Sum Sq     Mean Sq NumDF DenDF     F value       Pr(>F)
fecha          461.7404592 461.7404592     1   130 50.41969020 7.256505e-11
estacion         0.5268064   0.5268064     1    10  0.05752456 8.152969e-01
fecha:estacion 439.9539281 439.9539281     1   130 48.04071273 1.748462e-10
               variable         factor
fecha           humedad          fecha
estacion        humedad       estacion
fecha:estacion  humedad fecha:estacion

Post-hoc

estacion fecha contrast estimate SE df t.ratio p.value
Ot . 1 postQuema - 0 preQuema -0.0907 0.6177 130.0000 -0.1468 0.9998
Pr . 1 postQuema - 0 preQuema -7.5065 0.8736 130.0000 -8.5927 0.0000
. 0 preQuema Pr - Ot 3.4031 1.3789 13.8239 2.4679 0.1048
. 1 postQuema Pr - Ot -4.0127 1.3789 13.8239 -2.9100 0.0454

CIC

Model

Type III Analysis of Variance Table with Satterthwaite's method
               Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
fecha          18.503  18.503     1   130  5.1118 0.02543 *
estacion       35.605  35.605     1    10  9.8361 0.01058 *
fecha:estacion  8.337   8.337     1   130  2.3031 0.13154  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                  Sum Sq   Mean Sq NumDF      DenDF  F value     Pr(>F)
fecha          18.503472 18.503472     1 130.000001 5.111751 0.02542616
estacion       35.604511 35.604511     1   9.999998 9.836067 0.01057742
fecha:estacion  8.336806  8.336806     1 130.000001 2.303118 0.13154272
               variable         factor
fecha               cic          fecha
estacion            cic       estacion
fecha:estacion      cic fecha:estacion

Post-hoc

estacion fecha contrast estimate SE df t.ratio p.value
Ot . 1 postQuema - 0 preQuema -1.2708 0.3884 130.0000 -3.2723 0.0055
Pr . 1 postQuema - 0 preQuema -0.2500 0.5492 130.0000 -0.4552 0.9849
. 0 preQuema Pr - Ot -3.4375 0.9921 12.7494 -3.4650 0.0171
. 1 postQuema Pr - Ot -2.4167 0.9921 12.7494 -2.4360 0.1159

C

Model

Type III Analysis of Variance Table with Satterthwaite's method
                Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
fecha          13.3214 13.3214     1   130  6.1796 0.01419 *
estacion        0.2542  0.2542     1    10  0.1179 0.73843  
fecha:estacion  0.5636  0.5636     1   130  0.2614 0.61001  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                   Sum Sq    Mean Sq NumDF      DenDF   F value     Pr(>F)
fecha          13.3214014 13.3214014     1 130.000000 6.1795805 0.01419189
estacion        0.2541669  0.2541669     1   9.999999 0.1179039 0.73842869
fecha:estacion  0.5635681  0.5635681     1 130.000000 0.2614300 0.61000674
                variable         factor
fecha          c_percent          fecha
estacion       c_percent       estacion
fecha:estacion c_percent fecha:estacion

Post-hoc

estacion fecha contrast estimate SE df t.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.7779 0.2997 130.0000 2.5956 0.0414
Pr . 1 postQuema - 0 preQuema 0.5125 0.4238 130.0000 1.2092 0.6463
. 0 preQuema Pr - Ot -0.1667 0.9097 11.8438 -0.1832 0.9996
. 1 postQuema Pr - Ot -0.4321 0.9097 11.8438 -0.4750 0.9838

Fe

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df    F p.value
1          fecha 1, 130 0.28    .598
2       estacion  1, 10 0.46    .512
3 fecha:estacion 1, 130 0.21    .648
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df      F Pr(>F)
fecha               1    130 0.2796 0.5979
estacion            1     10 0.4631 0.5116
fecha:estacion      1    130 0.2093 0.6481

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.0169 0.0200 Inf 0.8433 0.8696
Pr . 1 postQuema - 0 preQuema 0.0011 0.0266 Inf 0.0408 1.0000
. 0 preQuema Pr - Ot -0.0285 0.0714 Inf -0.3989 0.9908
. 1 postQuema Pr - Ot -0.0443 0.0715 Inf -0.6193 0.9535

MO

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df         F p.value
1          fecha 1, 130      0.60    .439
2       estacion  1, 10 35.18 ***   <.001
3 fecha:estacion 1, 130      0.91    .341
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df       F    Pr(>F)    
fecha               1    130  0.6020 0.4392419    
estacion            1     10 35.1850 0.0001448 ***
fecha:estacion      1    130  0.9143 0.3407472    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema -0.0193 0.0152 Inf -1.2691 0.5994
Pr . 1 postQuema - 0 preQuema 0.0082 0.0433 Inf 0.1882 0.9995
. 0 preQuema Pr - Ot 0.1608 0.0375 Inf 4.2902 0.0001
. 1 postQuema Pr - Ot 0.1882 0.0377 Inf 4.9905 0.0000

K

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df      F p.value
1          fecha 1, 130   1.92    .168
2       estacion  1, 10 8.33 *    .016
3 fecha:estacion 1, 130   0.00    .976
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df      F  Pr(>F)  
fecha               1    130 1.9189 0.16835  
estacion            1     10 8.3254 0.01624 *
fecha:estacion      1    130 0.0009 0.97555  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.1833 0.1323 Inf 1.3860 0.5156
Pr . 1 postQuema - 0 preQuema 0.0554 0.1050 Inf 0.5280 0.9737
. 0 preQuema Pr - Ot -1.1566 0.5704 Inf -2.0277 0.1598
. 1 postQuema Pr - Ot -1.2845 0.5720 Inf -2.2456 0.0953

Mg

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df      F p.value
1          fecha 1, 130   0.96    .329
2       estacion  1, 10 4.01 +    .073
3 fecha:estacion 1, 130   0.13    .719
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df      F  Pr(>F)  
fecha               1    130 0.9587 0.32933  
estacion            1     10 4.0114 0.07304 .
fecha:estacion      1    130 0.1300 0.71903  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema -0.0643 0.0469 Inf -1.3715 0.5260
Pr . 1 postQuema - 0 preQuema -0.0146 0.0463 Inf -0.3147 0.9963
. 0 preQuema Pr - Ot -0.3632 0.1895 Inf -1.9168 0.2034
. 1 postQuema Pr - Ot -0.3134 0.1888 Inf -1.6602 0.3347

C/N

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df    F p.value
1          fecha 1, 130 0.05    .815
2       estacion  1, 10 0.56    .471
3 fecha:estacion 1, 130 0.80    .372
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df      F Pr(>F)
fecha               1    130 0.0550 0.8150
estacion            1     10 0.5601 0.4715
fecha:estacion      1    130 0.8031 0.3718

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.0024 0.0020 Inf 1.1713 0.6690
Pr . 1 postQuema - 0 preQuema -0.0011 0.0025 Inf -0.4350 0.9872
. 0 preQuema Pr - Ot -0.0021 0.0052 Inf -0.4105 0.9897
. 1 postQuema Pr - Ot -0.0056 0.0052 Inf -1.0743 0.7353

P

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df       F p.value
1          fecha 1, 130    0.45    .502
2       estacion  1, 10  8.17 *    .017
3 fecha:estacion 1, 130 9.31 **    .003
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df      F   Pr(>F)   
fecha               1    130 0.4536 0.501823   
estacion            1     10 8.1689 0.017011 * 
fecha:estacion      1    130 9.3060 0.002769 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.3057 0.0947 Inf 3.2274 0.0050
Pr . 1 postQuema - 0 preQuema -0.1388 0.1140 Inf -1.2173 0.6364
. 0 preQuema Pr - Ot 0.5604 0.1255 Inf 4.4635 0.0000
. 1 postQuema Pr - Ot 0.1158 0.1229 Inf 0.9422 0.8171

N

Model

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: n_percent
                Chisq Df Pr(>Chisq)   
fecha          5.1208  1   0.023641 * 
estacion       7.2940  1   0.006919 **
fecha:estacion 2.6712  1   0.102179   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df t.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.3240 0.1161 138 2.7895 0.0239
Pr . 1 postQuema - 0 preQuema -0.0182 0.1741 138 -0.1042 1.0000
. 0 preQuema Pr - Ot -0.1088 0.1493 138 -0.7291 0.9194
. 1 postQuema Pr - Ot -0.4509 0.1468 138 -3.0717 0.0102

Na

Model

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: na_percent
                Chisq Df Pr(>Chisq)
fecha          0.7349  1     0.3913
estacion       2.5709  1     0.1088
fecha:estacion 0.0700  1     0.7913

Post-hoc

estacion fecha contrast estimate SE df t.ratio p.value
Ot . 1 postQuema - 0 preQuema -0.0936 0.1124 138 -0.8327 0.8759
Pr . 1 postQuema - 0 preQuema -0.0463 0.1387 138 -0.3340 0.9954
. 0 preQuema Pr - Ot 0.3170 0.2293 138 1.3825 0.5232
. 1 postQuema Pr - Ot 0.3642 0.2309 138 1.5773 0.3921

pH agua

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_agua_eez ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df        F p.value
1          fecha 1, 130     0.99    .322
2       estacion  1, 10   9.29 *    .012
3 fecha:estacion 1, 130 10.51 **    .002
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_agua_eez ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df       F   Pr(>F)   
fecha               1    130  0.9889 0.321856   
estacion            1     10  9.2903 0.012294 * 
fecha:estacion      1    130 10.5108 0.001508 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema 0.0009 5e-04 Inf 1.9941 0.1722
Pr . 1 postQuema - 0 preQuema -0.0017 6e-04 Inf -2.6282 0.0339
. 0 preQuema Pr - Ot 0.0001 6e-04 Inf 0.1261 0.9999
. 1 postQuema Pr - Ot -0.0025 6e-04 Inf -4.0923 0.0002

pH KCl

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_k_cl ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
          Effect     df         F p.value
1          fecha 1, 130 45.36 ***   <.001
2       estacion  1, 10    5.70 *    .038
3 fecha:estacion 1, 130   9.39 **    .003
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_k_cl ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
               num Df den Df       F    Pr(>F)    
fecha               1    130 45.3634 4.776e-10 ***
estacion            1     10  5.7006  0.038115 *  
fecha:estacion      1    130  9.3932  0.002649 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

estacion fecha contrast estimate SE df z.ratio p.value
Ot . 1 postQuema - 0 preQuema -0.0015 0.0004 Inf -3.2907 0.0040
Pr . 1 postQuema - 0 preQuema -0.0038 0.0006 Inf -6.1052 0.0000
. 0 preQuema Pr - Ot -0.0010 0.0013 Inf -0.7957 0.8916
. 1 postQuema Pr - Ot -0.0033 0.0013 Inf -2.6488 0.0319

NH4

  • Prepare data
  • We have only data for Autumn fire
  • The approach will be the following: Apply non-parametric Wilcoxon test to compare pre and postFire
        fecha
estacion 0 preQuema 1 postQuema
      Ot         48          43

Model

NO3

Model

General Overview

Mean + SE table

Characteristic 0 preQuema 1 postQuema
Ot, N = 481 Pr, N = 241 Ot, N = 481 Pr, N = 241
humedad 12.22 (0.50) 15.62 (0.63) 12.13 (0.59) 8.11 (0.53)
n_nh4 0.62 (0.03) NA (NA) 2.91 (0.36) NA (NA)
n_no3 0.92 (0.07) NA (NA) 0.81 (0.07) NA (NA)
fe_percent 1.86 (0.07) 1.95 (0.08) 1.80 (0.04) 1.95 (0.09)
k_percent 0.45 (0.03) 0.79 (0.06) 0.41 (0.03) 0.75 (0.06)
mg_percent 1.29 (0.09) 2.00 (0.14) 1.42 (0.12) 2.06 (0.18)
na_percent 0.04 (0.00) 0.06 (0.01) 0.04 (0.00) 0.06 (0.01)
n_percent 0.22 (0.01) 0.19 (0.02) 0.29 (0.02) 0.18 (0.01)
c_percent 7.22 (0.30) 7.05 (0.31) 8.00 (0.29) 7.57 (0.38)
c_n 39.72 (2.77) 41.62 (3.62) 36.14 (3.32) 43.72 (3.06)
cic 16.60 (0.34) 13.17 (0.49) 15.33 (0.29) 12.92 (0.55)
p 3.83 (0.17) 6.75 (0.92) 5.20 (0.24) 5.88 (0.25)
mo 5.37 (0.30) 2.87 (0.32) 6.00 (0.39) 2.80 (0.16)
p_h_k_cl 7.46 (0.02) 7.52 (0.04) 7.54 (0.02) 7.74 (0.03)
p_h_agua_eez 7.97 (0.02) 7.97 (0.04) 7.91 (0.02) 8.07 (0.02)

1 Mean (std.error)

Characteristic Ot Pr
0 preQuema, N = 481 1 postQuema, N = 481 0 preQuema, N = 241 1 postQuema, N = 241
humedad 12.22 (0.50) 12.13 (0.59) 15.62 (0.63) 8.11 (0.53)
n_nh4 0.62 (0.03) 2.91 (0.36) NA (NA) NA (NA)
n_no3 0.92 (0.07) 0.81 (0.07) NA (NA) NA (NA)
fe_percent 1.86 (0.07) 1.80 (0.04) 1.95 (0.08) 1.95 (0.09)
k_percent 0.45 (0.03) 0.41 (0.03) 0.79 (0.06) 0.75 (0.06)
mg_percent 1.29 (0.09) 1.42 (0.12) 2.00 (0.14) 2.06 (0.18)
na_percent 0.04 (0.00) 0.04 (0.00) 0.06 (0.01) 0.06 (0.01)
n_percent 0.22 (0.01) 0.29 (0.02) 0.19 (0.02) 0.18 (0.01)
c_percent 7.22 (0.30) 8.00 (0.29) 7.05 (0.31) 7.57 (0.38)
c_n 39.72 (2.77) 36.14 (3.32) 41.62 (3.62) 43.72 (3.06)
cic 16.60 (0.34) 15.33 (0.29) 13.17 (0.49) 12.92 (0.55)
p 3.83 (0.17) 5.20 (0.24) 6.75 (0.92) 5.88 (0.25)
mo 5.37 (0.30) 6.00 (0.39) 2.87 (0.32) 2.80 (0.16)
p_h_k_cl 7.46 (0.02) 7.54 (0.02) 7.52 (0.04) 7.74 (0.03)
p_h_agua_eez 7.97 (0.02) 7.91 (0.02) 7.97 (0.04) 8.07 (0.02)

1 Mean (std.error)

Figures

Version Author Date
419d019 ajpelu 2021-09-15
3307f05 ajpelu 2021-09-14
389b963 ajpelu 2021-09-14

Version Author Date
419d019 ajpelu 2021-09-15
3307f05 ajpelu 2021-09-14
389b963 ajpelu 2021-09-14

Version Author Date
419d019 ajpelu 2021-09-15

Anovas table

estacion
fecha
estacion x fecha
Variables F p F p F p
c_n 0.560 0.471 0.055 0.815 0.803 0.372
cic 9.836 0.011 5.112 0.025 2.303 0.132
k_percent 8.325 0.016 1.919 0.168 0.001 0.976
mg_percent 4.011 0.073 0.959 0.329 0.130 0.719
mo 35.185 0.000 0.602 0.439 0.914 0.341
n_nh4 NA NA 403.000 0.000 NA NA
n_no3 NA NA 1198.500 0.187 NA NA
p 8.169 0.017 0.454 0.502 9.306 0.003
p_h_agua_eez 9.290 0.012 0.989 0.322 10.511 0.002
p_h_k_cl 5.701 0.038 45.363 0.000 9.393 0.003
humedad 0.058 0.815 50.420 0.000 48.041 0.000
n_percent 7.294 0.007 5.121 0.024 2.671 0.102
c_percent 0.118 0.738 6.180 0.014 0.261 0.610
na_percent 2.571 0.109 0.735 0.391 0.070 0.791

Gráficos feos feísimos

Version Author Date
419d019 ajpelu 2021-09-15
389b963 ajpelu 2021-09-14

Version Author Date
419d019 ajpelu 2021-09-15
389b963 ajpelu 2021-09-14

Version Author Date
419d019 ajpelu 2021-09-15
389b963 ajpelu 2021-09-14

Version Author Date
419d019 ajpelu 2021-09-15
389b963 ajpelu 2021-09-14

Version Author Date
419d019 ajpelu 2021-09-15

Boostrapping methods

  • Get mean and IC 95% by boostraping

  • We generate a function to compute the mean and the ci (both by bootstrapping) for each soil variable and groupped by estacion and fecha.

  • Then we generated a dataframe with this info and plot

Plots with CI 95 % (bootstraping bca)


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] boot_1.3-26        magrittr_2.0.1     gtsummary_1.4.2    plotrix_3.8-1     
 [5] glmmTMB_1.0.2.1    kableExtra_1.3.1   afex_0.28-1        performance_0.7.2 
 [9] multcomp_1.4-16    TH.data_1.0-10     mvtnorm_1.1-1      emmeans_1.5.4     
[13] lmerTest_3.1-3     lme4_1.1-27.1      Matrix_1.3-2       fitdistrplus_1.1-3
[17] survival_3.2-7     MASS_7.3-53        ggpubr_0.4.0       janitor_2.1.0     
[21] here_1.0.1         forcats_0.5.1      stringr_1.4.0      dplyr_1.0.6       
[25] purrr_0.3.4        readr_1.4.0        tidyr_1.1.3        tibble_3.1.2      
[29] ggplot2_3.3.5      tidyverse_1.3.1    rmdformats_1.0.1   knitr_1.31        
[33] workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] minqa_1.2.4         colorspace_2.0-0    ggsignif_0.6.0     
 [4] ellipsis_0.3.2      rio_0.5.16          rprojroot_2.0.2    
 [7] estimability_1.3    snakecase_0.11.0    fs_1.5.0           
[10] rstudioapi_0.13     farver_2.0.3        fansi_0.4.2        
[13] lubridate_1.7.10    xml2_1.3.2          codetools_0.2-18   
[16] splines_4.0.2       jsonlite_1.7.2      nloptr_1.2.2.2     
[19] gt_0.3.0            pbkrtest_0.5-0.1    broom_0.7.9        
[22] dbplyr_2.1.1        compiler_4.0.2      httr_1.4.2         
[25] backports_1.2.1     assertthat_0.2.1    fastmap_1.1.0      
[28] cli_2.5.0           formatR_1.8         later_1.1.0.1      
[31] htmltools_0.5.2     tools_4.0.2         coda_0.19-4        
[34] gtable_0.3.0        glue_1.4.2          reshape2_1.4.4     
[37] Rcpp_1.0.7          carData_3.0-4       cellranger_1.1.0   
[40] jquerylib_0.1.3     vctrs_0.3.8         nlme_3.1-152       
[43] broom.helpers_1.3.0 insight_0.14.4      xfun_0.23          
[46] openxlsx_4.2.3      rvest_1.0.0         lifecycle_1.0.0    
[49] rstatix_0.6.0       zoo_1.8-8           scales_1.1.1       
[52] hms_1.0.0           promises_1.2.0.1    parallel_4.0.2     
[55] sandwich_3.0-0      TMB_1.7.19          yaml_2.2.1         
[58] curl_4.3            sass_0.3.1          stringi_1.7.4      
[61] highr_0.8           checkmate_2.0.0     zip_2.1.1          
[64] commonmark_1.7      rlang_0.4.10        pkgconfig_2.0.3    
[67] evaluate_0.14       lattice_0.20-41     labeling_0.4.2     
[70] tidyselect_1.1.1    plyr_1.8.6          bookdown_0.21.6    
[73] R6_2.5.0            generics_0.1.0      DBI_1.1.1          
[76] pillar_1.6.1        haven_2.3.1         whisker_0.4        
[79] foreign_0.8-81      withr_2.4.1         abind_1.4-5        
[82] modelr_0.1.8        crayon_1.4.1        car_3.0-10         
[85] utf8_1.1.4          rmarkdown_2.8       grid_4.0.2         
[88] readxl_1.3.1        data.table_1.14.0   git2r_0.28.0       
[91] webshot_0.5.2       reprex_2.0.0        digest_0.6.27      
[94] xtable_1.8-4        httpuv_1.5.5        numDeriv_2016.8-1.1
[97] munsell_0.5.0       viridisLite_0.3.0   bslib_0.2.4