Last updated: 2021-09-17

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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
Characteristic	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
Characteristic	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

Pre- and post-fire anaylisis of soils by Fire date

Prepare data

Modelize

Humedad

Model

Post-hoc

CIC

Model

Post-hoc

C

Model

Post-hoc

Fe

Model

Post-hoc

MO

Model

Post-hoc

K

Model

Post-hoc

Mg

Model

Post-hoc

C/N

Model

Post-hoc

P

Model

Post-hoc

N

Model

Post-hoc

Na

Model

Post-hoc

pH agua

Model

Post-hoc

pH KCl

Model

Post-hoc

NH4

Model

NO3

Model

General Overview

Mean + SE table

Figures

Anovas table

Gráficos feos feísimos

Boostrapping methods

Plots with CI 95 % (bootstraping bca)