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Prepare data

raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"), 
    sheet = "SEGUIMIENTO_MUESTRAS_SUELOS") %>% 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_") ~ "NP", str_detect(geo_parcela_nombre, 
        "PR_") ~ "PR", str_detect(geo_parcela_nombre, "P_") ~ "P"), fecha = lubridate::ymd(fecha), 
    pre_post_quema = case_when(pre_post_quema == "Prequema" ~ "0 Pre", pre_post_quema == 
        "Postquema" ~ "1 Post"))

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(fecha %in% lubridate::ymd(c("2018-12-11", "2018-12-20", 
    "2019-04-24", "2019-05-09"))) %>% mutate(zona = as.factor(zona), pre_post_quema = as.factor(pre_post_quema))

Structure of the data

              zona
pre_post_quema NP  P PR
        0 Pre  24 24 24
        1 Post 24 24 24

Modellize

For each response variable, the approach modelling is

\(Y \sim zona (P|NP|PR) + Fecha(pre|post) + zona \times Fecha\)

using the “(1|zona: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

Check distribution

summary statistics
------
min:  2.961538   max:  23.90476 
median:  11.90231 
mean:  12.0709 
estimated sd:  4.08543 
estimated skewness:  0.1696677 
estimated kurtosis:  2.93929

Normality & Homocedasticity

[1] "Variances homogeneity?"

OK: Variances in each of the groups are the same (Bartlett Test, p = 0.257).

[1] "Normality?"

OK: residuals appear as normally distributed (p = 0.096).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.958).

Model

Type III Analysis of Variance Table with Satterthwaite's method
                    Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
pre_post_quema      236.42 236.416     1   129 26.1050 1.136e-06 ***
zona                  1.46   0.728     2     9  0.0803    0.9235    
pre_post_quema:zona 462.22 231.109     2   129 25.5190 4.593e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            13.4 0.676 12.1    11.88     14.8
 1 Post           10.8 0.676 12.1     9.32     12.3

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post     2.56 0.502 129 5.109   <.0001 

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger

$`emmeans of zona`
 zona emmean   SE df lower.CL upper.CL
 NP     12.4 1.09  9     9.97     14.9
 P      11.9 1.09  9     9.46     14.4
 PR     11.9 1.09  9     9.41     14.3

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate   SE df t.ratio p.value
 NP - P    0.5060 1.54  9 0.329   0.9424 
 NP - PR   0.5579 1.54  9 0.363   0.9306 
 P - PR    0.0518 1.54  9 0.034   0.9994 

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 0 Pre           11.99 1.17 12.1     9.44     14.5
 1 Post          12.86 1.17 12.1    10.31     15.4

zona = P:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 0 Pre           12.45 1.17 12.1     9.90     15.0
 1 Post          11.39 1.17 12.1     8.84     13.9

zona = PR:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 0 Pre           15.62 1.17 12.1    13.07     18.2
 1 Post           8.11 1.17 12.1     5.57     10.7

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   -0.872 0.869 129 -1.004  0.3171 

zona = P:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post    1.054 0.869 129  1.213  0.2273 

zona = PR:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post    7.507 0.869 129  8.641  <.0001 

Degrees-of-freedom method: kenward-roger

CIC

Check distribution

summary statistics
------
min:  8   max:  24 
median:  15 
mean:  14.99306 
estimated sd:  2.726448 
estimated skewness:  -0.08707462 
estimated kurtosis:  3.374256

Normality & Homocedasticity

[1] "Variances homogeneity?"

OK: Variances in each of the groups are the same (Bartlett Test, p = 0.313).

[1] "Normality?"

OK: residuals appear as normally distributed (p = 0.745).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.397).

Model

Type III Analysis of Variance Table with Satterthwaite's method
                    Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
pre_post_quema      31.174 31.1736     1   129  8.7568 0.003671 **
zona                40.297 20.1484     2     9  5.6598 0.025614 * 
pre_post_quema:zona 19.681  9.8403     2   129  2.7642 0.066764 . 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            15.5 0.462 11.5     14.4     16.5
 1 Post           14.5 0.462 11.5     13.5     15.5

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post    0.931 0.314 129 2.959   0.0037 

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger

$`emmeans of zona`
 zona emmean    SE df lower.CL upper.CL
 NP     15.4 0.753  9     13.7     17.1
 P      16.6 0.753  9     14.9     18.3
 PR     13.0 0.753  9     11.3     14.7

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate   SE df t.ratio p.value
 NP - P     -1.19 1.06  9 -1.115  0.5291 
 NP - PR     2.33 1.06  9  2.191  0.1262 
 P - PR      3.52 1.06  9  3.307  0.0225 

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            15.7 0.801 11.5     13.9     17.4
 1 Post           15.1 0.801 11.5     13.3     16.8

zona = P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            17.5 0.801 11.5     15.8     19.3
 1 Post           15.6 0.801 11.5     13.8     17.3

zona = PR:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            13.2 0.801 11.5     11.4     14.9
 1 Post           12.9 0.801 11.5     11.2     14.7

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post    0.583 0.545 129 1.071   0.2862 

zona = P:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post    1.958 0.545 129 3.595   0.0005 

zona = PR:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post    0.250 0.545 129 0.459   0.6470 

Degrees-of-freedom method: kenward-roger

% C

Check distribution

summary statistics
------
min:  3.35   max:  12.5 
median:  7.57 
mean:  7.509167 
estimated sd:  1.9505 
estimated skewness:  0.03328586 
estimated kurtosis:  2.503996

Normality & Homocedasticity

[1] "Variances homogeneity?"

OK: Variances in each of the groups are the same (Bartlett Test, p = 0.434).

[1] "Normality?"

OK: residuals appear as normally distributed (p = 0.373).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.623).

Model

Type III Analysis of Variance Table with Satterthwaite's method
                     Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
pre_post_quema      17.1120 17.1120     1   129  7.8770 0.005784 **
zona                 5.3708  2.6854     2     9  1.2361 0.335481   
pre_post_quema:zona  0.5673  0.2837     2   129  0.1306 0.877707   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            7.16 0.405 10.9     6.27     8.06
 1 Post           7.85 0.405 10.9     6.96     8.75

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   -0.689 0.246 129 -2.807  0.0058 

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger

$`emmeans of zona`
 zona emmean    SE df lower.CL upper.CL
 NP     6.89 0.669  9     5.37     8.40
 P      8.33 0.669  9     6.82     9.84
 PR     7.31 0.669  9     5.80     8.82

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate    SE df t.ratio p.value
 NP - P    -1.446 0.945  9 -1.529  0.3233 
 NP - PR   -0.424 0.945  9 -0.448  0.8965 
 P - PR     1.022 0.945  9  1.081  0.5483 

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            6.50 0.702 10.9     4.96     8.05
 1 Post           7.27 0.702 10.9     5.72     8.81

zona = P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            7.94 0.702 10.9     6.39     9.48
 1 Post           8.73 0.702 10.9     7.18    10.27

zona = PR:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 Pre            7.05 0.702 10.9     5.51     8.60
 1 Post           7.57 0.702 10.9     6.02     9.11

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   -0.765 0.425 129 -1.799  0.0744 

zona = P:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   -0.790 0.425 129 -1.858  0.0655 

zona = PR:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   -0.512 0.425 129 -1.205  0.2306 

Degrees-of-freedom method: kenward-roger

% Fe

Check distribution

summary statistics
------
min:  0.341   max:  3.35 
median:  1.858 
mean:  1.87166 
estimated sd:  0.4077216 
estimated skewness:  0.6162434 
estimated kurtosis:  6.394979

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.918).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df    F p.value
1      pre_post_quema 1, 129 0.52    .473
2                zona   2, 9 0.69    .526
3 pre_post_quema:zona 2, 129 0.17    .845
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre           0.549 0.00599 Inf     0.537     0.561
 1 Post          0.560 0.00818 Inf     0.544     0.576

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post  -0.0112 0.00566 Inf -1.979  0.0478 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean      SE  df asymp.LCL asymp.UCL
 NP    0.526 0.00599 Inf     0.514     0.538
 P     0.607 0.00848 Inf     0.590     0.623
 PR    0.531 0.00846 Inf     0.514     0.547

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate      SE  df z.ratio p.value
 NP - P  -0.08067 0.00609 Inf -13.241 <.0001 
 NP - PR -0.00486 0.00608 Inf  -0.799 0.7036 
 P - PR   0.07581 0.00860 Inf   8.819 <.0001 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre           0.515 0.00543 Inf     0.504     0.526
 1 Post          0.537 0.00747 Inf     0.522     0.552

zona = P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre           0.601 0.00768 Inf     0.586     0.616
 1 Post          0.612 0.01058 Inf     0.591     0.633

zona = PR:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre           0.530 0.00767 Inf     0.515     0.545
 1 Post          0.531 0.01054 Inf     0.511     0.552

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post -0.02193 0.00519 Inf -4.225  <.0001 

zona = P:
 2              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post -0.01062 0.00737 Inf -1.442  0.1493 

zona = PR:
 2              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post -0.00106 0.00732 Inf -0.144  0.8853 

Note: contrasts are still on the inverse scale

% K

Check distribution

summary statistics
------
min:  0.056   max:  1.74 
median:  0.4965 
mean:  0.5435972 
estimated sd:  0.2791576 
estimated skewness:  1.262402 
estimated kurtosis:  5.88111

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.339).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df      F p.value
1      pre_post_quema 1, 129   2.19    .141
2                zona   2, 9 6.73 *    .016
3 pre_post_quema:zona 2, 129   0.43    .651
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            2.13 0.168 Inf      1.80      2.46
 1 Post           2.37 0.173 Inf      2.03      2.71

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post   -0.243 0.107 Inf -2.275  0.0229 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean    SE  df asymp.LCL asymp.UCL
 NP     2.01 0.273 Inf     1.472      2.54
 P      3.25 0.257 Inf     2.746      3.75
 PR     1.49 0.303 Inf     0.896      2.08

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate    SE  df z.ratio p.value
 NP - P    -1.242 0.372 Inf -3.339  0.0024 
 NP - PR    0.517 0.406 Inf  1.273  0.4106 
 P - PR     1.759 0.396 Inf  4.445  <.0001 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            1.99 0.283 Inf     1.438      2.55
 1 Post           2.02 0.283 Inf     1.466      2.58

zona = P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            2.93 0.276 Inf     2.386      3.47
 1 Post           3.57 0.301 Inf     2.982      4.16

zona = PR:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            1.46 0.307 Inf     0.860      2.06
 1 Post           1.52 0.308 Inf     0.914      2.12

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post  -0.0297 0.150 Inf -0.198  0.8433 

zona = P:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post  -0.6437 0.263 Inf -2.444  0.0145 

zona = PR:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post  -0.0556 0.104 Inf -0.536  0.5921 

Note: contrasts are still on the inverse scale

% Mg

Check distribution

summary statistics
------
min:  0.252   max:  4.59 
median:  1.4 
mean:  1.579299 
estimated sd:  0.8084862 
estimated skewness:  1.064159 
estimated kurtosis:  4.167461

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.525).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df      F p.value
1      pre_post_quema 1, 129   1.37    .245
2                zona   2, 9 3.05 +    .097
3 pre_post_quema:zona 2, 129   0.84    .434
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           0.784 0.0807 Inf     0.625     0.942
 1 Post          0.749 0.0803 Inf     0.592     0.907

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post   0.0345 0.0371 Inf 0.929   0.3528 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean    SE  df asymp.LCL asymp.UCL
 NP    0.759 0.133 Inf     0.498      1.02
 P     0.992 0.125 Inf     0.747      1.24
 PR    0.548 0.144 Inf     0.265      0.83

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate    SE  df z.ratio p.value
 NP - P    -0.233 0.181 Inf -1.286  0.4032 
 NP - PR    0.212 0.196 Inf  1.080  0.5264 
 P - PR     0.445 0.190 Inf  2.341  0.0504 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre           0.807 0.137 Inf     0.538     1.076
 1 Post          0.711 0.135 Inf     0.446     0.977

zona = P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre           0.988 0.132 Inf     0.730     1.247
 1 Post          0.996 0.132 Inf     0.737     1.255

zona = PR:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre           0.555 0.146 Inf     0.269     0.841
 1 Post          0.540 0.146 Inf     0.255     0.826

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post  0.09632 0.0566 Inf  1.702  0.0887 

zona = P:
 2              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post -0.00743 0.0841 Inf -0.088  0.9296 

zona = PR:
 2              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post  0.01456 0.0461 Inf  0.316  0.7523 

Note: contrasts are still on the inverse scale

C/N

Check distribution

summary statistics
------
min:  14.20907   max:  116.0373 
median:  34.20672 
mean:  39.50944 
estimated sd:  19.74075 
estimated skewness:  1.645355 
estimated kurtosis:  6.246139

Normality & Homocedasticity

[1] "Variances homogeneity?"

OK: Variances in each of the groups are the same (Bartlett Test, p = 0.136).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.614).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df    F p.value
1      pre_post_quema 1, 129 0.32    .574
2                zona   2, 9 0.43    .663
3 pre_post_quema:zona 2, 129 0.42    .659
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre          0.0269 0.00248 Inf    0.0221    0.0318
 1 Post         0.0281 0.00250 Inf    0.0232    0.0330

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post -0.00119 0.0016 Inf -0.743  0.4574 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean      SE  df asymp.LCL asymp.UCL
 NP   0.0301 0.00398 Inf    0.0223    0.0379
 P    0.0275 0.00403 Inf    0.0196    0.0354
 PR   0.0249 0.00402 Inf    0.0171    0.0328

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate      SE  df z.ratio p.value
 NP - P   0.00257 0.00559 Inf 0.459   0.8904 
 NP - PR  0.00514 0.00562 Inf 0.915   0.6311 
 P - PR   0.00257 0.00566 Inf 0.455   0.8923 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre          0.0290 0.00422 Inf    0.0207    0.0373
 1 Post         0.0312 0.00431 Inf    0.0227    0.0396

zona = P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre          0.0263 0.00421 Inf    0.0180    0.0345
 1 Post         0.0288 0.00429 Inf    0.0204    0.0372

zona = PR:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 Pre          0.0255 0.00424 Inf    0.0172    0.0338
 1 Post         0.0244 0.00420 Inf    0.0162    0.0326

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post -0.00215 0.00305 Inf -0.706  0.4803 

zona = P:
 2              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post -0.00252 0.00269 Inf -0.939  0.3479 

zona = PR:
 2              estimate      SE  df z.ratio p.value
 0 Pre - 1 Post  0.00111 0.00255 Inf  0.435  0.6635 

Note: contrasts are still on the inverse scale

P

Check distribution

summary statistics
------
min:  1   max:  17 
median:  4.5 
mean:  5.116667 
estimated sd:  2.43368 
estimated skewness:  1.886353 
estimated kurtosis:  8.270509

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) Warning: Non-normality of random effects detected (p = 0.007).

Model

Negative Binomial

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df       F p.value
1      pre_post_quema 1, 129  3.20 +    .076
2                zona   2, 9  3.81 +    .063
3 pre_post_quema:zona 2, 129 4.88 **    .009
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre            1.53 0.0628 Inf      1.40      1.65
 1 Post           1.68 0.0588 Inf      1.57      1.80

Results are averaged over the levels of: zona 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post   -0.156 0.0744 Inf -2.103  0.0355 

Results are averaged over the levels of: zona 
Results are given on the log (not the response) scale.

$`emmeans of zona`
 zona emmean     SE  df asymp.LCL asymp.UCL
 NP     1.52 0.0844 Inf      1.35      1.69
 P      1.46 0.0870 Inf      1.29      1.64
 PR     1.83 0.0776 Inf      1.68      1.98

Results are averaged over the levels of: pre_post_quema 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate    SE  df z.ratio p.value
 NP - P    0.0548 0.121 Inf  0.451  0.8938 
 NP - PR  -0.3116 0.114 Inf -2.734  0.0172 
 P - PR   -0.3664 0.116 Inf -3.147  0.0047 

Results are averaged over the levels of: pre_post_quema 
Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre            1.34 0.1122 Inf      1.12      1.56
 1 Post           1.70 0.0998 Inf      1.50      1.90

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre            1.34 0.1165 Inf      1.11      1.57
 1 Post           1.59 0.1053 Inf      1.38      1.79

zona = PR:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre            1.90 0.0936 Inf      1.72      2.08
 1 Post           1.76 0.0989 Inf      1.57      1.96

Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post   -0.361 0.129 Inf -2.808  0.0050 

zona = P:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post   -0.247 0.138 Inf -1.790  0.0734 

zona = PR:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post    0.139 0.114 Inf  1.217  0.2235 

Results are given on the log (not the response) scale.

MO

Check distribution

summary statistics
------
min:  0.44   max:  12.91 
median:  4.4 
mean:  4.737431 
estimated sd:  2.507076 
estimated skewness:  0.6914957 
estimated kurtosis:  2.846614

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p = 0.036).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.837).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df         F p.value
1      pre_post_quema 1, 129      1.35    .247
2                zona   2, 9 20.37 ***   <.001
3 pre_post_quema:zona 2, 129      1.04    .357
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           0.244 0.0152 Inf     0.215     0.274
 1 Post          0.233 0.0149 Inf     0.203     0.262

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post   0.0117 0.0177 Inf 0.660   0.5094 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean     SE  df asymp.LCL asymp.UCL
 NP    0.194 0.0186 Inf     0.157     0.230
 P     0.168 0.0176 Inf     0.133     0.202
 PR    0.354 0.0254 Inf     0.305     0.404

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate     SE  df z.ratio p.value
 NP - P    0.0261 0.0253 Inf  1.034  0.5556 
 NP - PR  -0.1608 0.0312 Inf -5.156  <.0001 
 P - PR   -0.1870 0.0307 Inf -6.084  <.0001 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           0.213 0.0231 Inf     0.168     0.258
 1 Post          0.174 0.0206 Inf     0.134     0.215

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           0.170 0.0204 Inf     0.130     0.210
 1 Post          0.165 0.0201 Inf     0.126     0.205

zona = PR:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           0.350 0.0330 Inf     0.286     0.415
 1 Post          0.359 0.0337 Inf     0.293     0.425

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post  0.03837 0.0233 Inf  1.648  0.0993 

zona = P:
 2              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post  0.00480 0.0201 Inf  0.239  0.8111 

zona = PR:
 2              estimate     SE  df z.ratio p.value
 0 Pre - 1 Post -0.00816 0.0432 Inf -0.189  0.8502 

Note: contrasts are still on the inverse scale

% N

Check distribution

summary statistics
------
min:  0.0427   max:  0.634455 
median:  0.2104425 
mean:  0.2299281 
estimated sd:  0.1176054 
estimated skewness:  1.283039 
estimated kurtosis:  4.846892

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

Model

Beta (glmmADBM)
Beta glmmTMB

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df lower.CL upper.CL
 0 Pre           -1.31 0.0710 136    -1.45   -1.171
 1 Post          -1.10 0.0678 136    -1.24   -0.968

Results are averaged over the levels of: zona 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate     SE  df t.ratio p.value
 0 Pre - 1 Post    -0.21 0.0968 136 -2.170  0.0318 

Results are averaged over the levels of: zona 
Results are given on the log odds ratio (not the response) scale.

$`emmeans of zona`
 zona emmean     SE  df lower.CL upper.CL
 NP    -1.11 0.0827 136    -1.27   -0.947
 P     -1.12 0.0828 136    -1.28   -0.953
 PR    -1.39 0.0882 136    -1.57   -1.219

Results are averaged over the levels of: pre_post_quema 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate    SE  df t.ratio p.value
 NP - P   0.00552 0.116 136 0.048   0.9988 
 NP - PR  0.28266 0.120 136 2.362   0.0510 
 P - PR   0.27714 0.120 136 2.315   0.0571 

Results are averaged over the levels of: pre_post_quema 
Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 Pre          -1.282 0.121 136    -1.52   -1.043
 1 Post         -0.940 0.112 136    -1.16   -0.718

zona = P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 Pre          -1.269 0.120 136    -1.51   -1.031
 1 Post         -0.964 0.113 136    -1.19   -0.741

zona = PR:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 Pre          -1.384 0.124 136    -1.63   -1.140
 1 Post         -1.403 0.124 136    -1.65   -1.157

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post  -0.3425 0.164 136 -2.086  0.0388 

zona = P:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post  -0.3055 0.164 136 -1.860  0.0650 

zona = PR:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   0.0182 0.174 136  0.104  0.9171 

Results are given on the log odds ratio (not the response) scale.

% Na

Check distribution

summary statistics
------
min:  0.005   max:  0.25 
median:  0.0355 
mean:  0.04798611 
estimated sd:  0.0382072 
estimated skewness:  2.52624 
estimated kurtosis:  10.93139

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

[1] "Normality, Random effects"

Group: zona:geo_parcela_nombre
(Intercept) OK: random effects appear as normally distributed (p = 0.952).

Model

Beta (glmmADBM)

Analysis of Deviance Table (Type II tests)

Response: na_percent
                    Df  Chisq Pr(>Chisq)  
pre_post_quema       1 0.0675    0.79498  
zona                 2 5.0992    0.07811 .
pre_post_quema:zona  2 0.1864    0.91101  
---
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: na_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df    F p.value
1      pre_post_quema 1, 129 0.42    .518
2                zona   2, 9 2.82    .112
3 pre_post_quema:zona 2, 129 0.18    .836
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Beta glmmTMB

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: na_percent
                      Chisq Df Pr(>Chisq)   
pre_post_quema       0.6972  1   0.403726   
zona                11.9551  2   0.002535 **
pre_post_quema:zona  0.2408  2   0.886558   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df lower.CL upper.CL
 0 Pre           -3.00 0.0916 136    -3.18    -2.82
 1 Post          -3.08 0.0934 136    -3.26    -2.90

Results are averaged over the levels of: zona 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate     SE  df t.ratio p.value
 0 Pre - 1 Post    0.082 0.0891 136 0.920   0.3593 

Results are averaged over the levels of: zona 
Results are given on the log odds ratio (not the response) scale.

$`emmeans of zona`
 zona emmean    SE  df lower.CL upper.CL
 NP    -2.88 0.134 136    -3.15    -2.61
 P     -3.43 0.146 136    -3.72    -3.15
 PR    -2.80 0.133 136    -3.07    -2.54

Results are averaged over the levels of: pre_post_quema 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1       estimate    SE  df t.ratio p.value
 NP - P    0.5523 0.196 136  2.816  0.0153 
 NP - PR  -0.0772 0.188 136 -0.411  0.9113 
 P - PR   -0.6295 0.195 136 -3.224  0.0045 

Results are averaged over the levels of: pre_post_quema 
Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 3 estimates

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 Pre           -2.86 0.152 136    -3.16    -2.56
 1 Post          -2.91 0.154 136    -3.21    -2.60

zona = P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 Pre           -3.36 0.168 136    -3.69    -3.03
 1 Post          -3.51 0.173 136    -3.85    -3.17

zona = PR:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 Pre           -2.78 0.150 136    -3.08    -2.48
 1 Post          -2.83 0.151 136    -3.13    -2.53

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   0.0499 0.144 136 0.345   0.7303 

zona = P:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   0.1486 0.177 136 0.837   0.4039 

zona = PR:
 2              estimate    SE  df t.ratio p.value
 0 Pre - 1 Post   0.0475 0.139 136 0.343   0.7321 

Results are given on the log odds ratio (not the response) scale.

NH4

prepara datos

              zona
pre_post_quema NP  P
        0 Pre  24 24
        1 Post 24 23

# A tibble: 4 x 4
# Groups:   zona [2]
  zona  pre_post_quema N.nh4 N.no3
  <fct> <fct>          <int> <int>
1 NP    0 Pre             24    24
2 NP    1 Post            24    24
3 P     0 Pre             24    24
4 P     1 Post            23    23

Check distribution

summary statistics
------
min:  0.258   max:  21.312 
median:  0.712 
mean:  2.315116 
estimated sd:  3.686985 
estimated skewness:  3.16529 
estimated kurtosis:  14.49165

Normality & Homocedasticity

[1] "Variances homogeneity?"

Warning: Variances differ between groups (Bartlett Test, p = 0.000).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: n_nh4 ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect       df         F p.value
1      pre_post_quema 1, 85.12 26.17 ***   <.001
2                zona  1, 5.99      1.58    .256
3 pre_post_quema:zona 1, 85.12      1.72    .193
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           1.630 0.1753 Inf     1.286     1.973
 1 Post          0.275 0.0439 Inf     0.189     0.361

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post     1.35 0.176 Inf 7.692   <.0001 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean    SE  df asymp.LCL asymp.UCL
 NP    0.950 0.133 Inf     0.689      1.21
 P     0.955 0.127 Inf     0.706      1.21

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1      estimate    SE  df z.ratio p.value
 NP - P -0.00561 0.183 Inf -0.031  0.9755 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           1.678 0.2550 Inf     1.179     2.178
 1 Post          0.221 0.0517 Inf     0.120     0.322

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 Pre           1.581 0.2404 Inf     1.110     2.052
 1 Post          0.330 0.0643 Inf     0.204     0.456

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post     1.46 0.254 Inf 5.729   <.0001 

zona = P:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post     1.25 0.243 Inf 5.155   <.0001 

Note: contrasts are still on the inverse scale

NO3

Check distribution

summary statistics
------
min:  0.197   max:  2.657 
median:  0.787 
mean:  0.8658526 
estimated sd:  0.4865887 
estimated skewness:  1.354862 
estimated kurtosis:  4.914124

Normality & Homocedasticity

[1] "Variances homogeneity?"

OK: Variances in each of the groups are the same (Bartlett Test, p = 0.066).

[1] "Normality?"

Warning: Non-normality of residuals detected (p < .001).

Model

Gamma

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]

Mixed Model Anova Table (Type 3 tests, KR-method)

Model: n_no3 ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect       df    F p.value
1      pre_post_quema 1, 85.07 1.02    .314
2                zona  1, 6.00 0.27    .620
3 pre_post_quema:zona 1, 85.07 0.28    .601
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            1.14 0.118 Inf     0.908      1.37
 1 Post           1.26 0.124 Inf     1.019      1.50

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post   -0.124 0.116 Inf -1.062  0.2884 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale

$`emmeans of zona`
 zona emmean    SE  df asymp.LCL asymp.UCL
 NP     1.25 0.149 Inf     0.957      1.54
 P      1.15 0.145 Inf     0.867      1.44

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1      estimate    SE  df z.ratio p.value
 NP - P   0.0979 0.204 Inf 0.479   0.6318 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale

$`emmeans of pre_post_quema | zona`
zona = NP:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            1.22 0.169 Inf     0.884      1.55
 1 Post           1.28 0.174 Inf     0.942      1.62

zona = P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 Pre            1.06 0.158 Inf     0.750      1.37
 1 Post           1.24 0.173 Inf     0.903      1.58

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = NP:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post  -0.0671 0.169 Inf -0.397  0.6915 

zona = P:
 2              estimate    SE  df z.ratio p.value
 0 Pre - 1 Post  -0.1801 0.160 Inf -1.125  0.2605 

Note: contrasts are still on the inverse scale

Resumen general

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

loaded via a namespace (and not attached):
  [1] minqa_1.2.4         colorspace_2.0-0    glmmADMB_0.8.3.3   
  [4] ggsignif_0.6.0      ggridges_0.5.3      ellipsis_0.3.2     
  [7] rio_0.5.16          rprojroot_2.0.2     estimability_1.3   
 [10] snakecase_0.11.0    parameters_0.14.0   fs_1.5.0           
 [13] rstudioapi_0.13     farver_2.0.3        fansi_0.4.2        
 [16] lubridate_1.7.10    xml2_1.3.2          codetools_0.2-18   
 [19] splines_4.0.2       jsonlite_1.7.2      nloptr_1.2.2.2     
 [22] gt_0.3.0            pbkrtest_0.5-0.1    broom_0.7.9        
 [25] dbplyr_2.1.1        effectsize_0.4.5    compiler_4.0.2     
 [28] httr_1.4.2          backports_1.2.1     assertthat_0.2.1   
 [31] fastmap_1.1.0       cli_2.5.0           formatR_1.8        
 [34] later_1.1.0.1       htmltools_0.5.2     tools_4.0.2        
 [37] coda_0.19-4         gtable_0.3.0        glue_1.4.2         
 [40] reshape2_1.4.4      Rcpp_1.0.7          carData_3.0-4      
 [43] cellranger_1.1.0    jquerylib_0.1.3     vctrs_0.3.8        
 [46] nlme_3.1-152        broom.helpers_1.3.0 insight_0.14.4     
 [49] xfun_0.23           openxlsx_4.2.3      rvest_1.0.0        
 [52] lifecycle_1.0.0     rstatix_0.6.0       zoo_1.8-8          
 [55] scales_1.1.1        hms_1.0.0           promises_1.2.0.1   
 [58] parallel_4.0.2      sandwich_3.0-0      TMB_1.7.19         
 [61] yaml_2.2.1          curl_4.3            gridExtra_2.3      
 [64] see_0.6.4           sass_0.3.1          stringi_1.7.4      
 [67] bayestestR_0.9.0    highr_0.8           randomForest_4.6-14
 [70] checkmate_2.0.0     boot_1.3-26         zip_2.1.1          
 [73] R2admb_0.7.16.2     commonmark_1.7      rlang_0.4.10       
 [76] pkgconfig_2.0.3     evaluate_0.14       lattice_0.20-41    
 [79] labeling_0.4.2      tidyselect_1.1.1    plyr_1.8.6         
 [82] magrittr_2.0.1      bookdown_0.21.6     R6_2.5.0           
 [85] generics_0.1.0      DBI_1.1.1           pillar_1.6.1       
 [88] haven_2.3.1         whisker_0.4         foreign_0.8-81     
 [91] withr_2.4.1         abind_1.4-5         modelr_0.1.8       
 [94] crayon_1.4.1        car_3.0-10          utf8_1.1.4         
 [97] rmarkdown_2.8       grid_4.0.2          readxl_1.3.1       
[100] data.table_1.14.0  
 [ reached getOption("max.print") -- omitted 8 entries ]

Pre- and post-fire anaylisis of soils

Prepare data

Modellize

Humedad

Check distribution

Normality & Homocedasticity

Model

Post-hoc

CIC

Check distribution

Normality & Homocedasticity

Model

Post-hoc

% C

Check distribution

Normality & Homocedasticity

Model

Post-hoc

% Fe

Check distribution

Normality & Homocedasticity

Model

Post-hoc

% K

Check distribution

Normality & Homocedasticity

Model

Post-hoc

% Mg

Check distribution

Normality & Homocedasticity

Model

Post-hoc

C/N

Check distribution

Normality & Homocedasticity

Model

Post-hoc

P

Check distribution

Normality & Homocedasticity

Model

Post-hoc

MO

Check distribution

Normality & Homocedasticity

Model

Post-hoc

% N

Check distribution

Normality & Homocedasticity

Model

Post-hoc

% Na

Check distribution

Normality & Homocedasticity

Model

Post-hoc

NH4

Check distribution

Normality & Homocedasticity

Model

Post-hoc

NO3

Check distribution

Normality & Homocedasticity

Model

Post-hoc

Resumen general