Last updated: 2021-09-15

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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
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
3307f05 ajpelu 2021-09-14
389b963 ajpelu 2021-09-14

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

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
389b963 ajpelu 2021-09-14

Version Author Date
389b963 ajpelu 2021-09-14

Version Author Date
389b963 ajpelu 2021-09-14

Version Author Date
389b963 ajpelu 2021-09-14


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] gtsummary_1.4.2    plotrix_3.8-1      glmmTMB_1.0.2.1    kableExtra_1.3.1  
 [5] afex_0.28-1        performance_0.7.2  multcomp_1.4-16    TH.data_1.0-10    
 [9] mvtnorm_1.1-1      emmeans_1.5.4      lmerTest_3.1-3     lme4_1.1-27.1     
[13] Matrix_1.3-2       fitdistrplus_1.1-3 survival_3.2-7     MASS_7.3-53       
[17] ggpubr_0.4.0       janitor_2.1.0      here_1.0.1         forcats_0.5.1     
[21] stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4        readr_1.4.0       
[25] tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.5      tidyverse_1.3.1   
[29] rmdformats_1.0.1   knitr_1.31         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     boot_1.3-26        
 [64] zip_2.1.1           commonmark_1.7      rlang_0.4.10       
 [67] pkgconfig_2.0.3     evaluate_0.14       lattice_0.20-41    
 [70] labeling_0.4.2      tidyselect_1.1.1    plyr_1.8.6         
 [73] magrittr_2.0.1      bookdown_0.21.6     R6_2.5.0           
 [76] generics_0.1.0      DBI_1.1.1           pillar_1.6.1       
 [79] haven_2.3.1         whisker_0.4         foreign_0.8-81     
 [82] withr_2.4.1         abind_1.4-5         modelr_0.1.8       
 [85] crayon_1.4.1        car_3.0-10          utf8_1.1.4         
 [88] rmarkdown_2.8       grid_4.0.2          readxl_1.3.1       
 [91] data.table_1.14.0   git2r_0.28.0        webshot_0.5.2      
 [94] reprex_2.0.0        digest_0.6.27       xtable_1.8-4       
 [97] httpuv_1.5.5        numDeriv_2016.8-1.1 munsell_0.5.0      
[100] viridisLite_0.3.0   bslib_0.2.4