Last updated: 2021-09-07

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File	Version	Author	Date	Message
Rmd	dc87911	ajpelu	2021-09-07	include pre post analysis

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))

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))

Design structure of the data

              zona
pre_post_quema NP  P PR
     Postquema 24 24 24
     Prequema  24 24 24

Models

Analysis
mira esto https://doi.org/10.1186/s42408-018-0022-8
Explore distribution of the data fore each variable
Modelize
Post hocs
Plots

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        10.8 0.676 12.1     9.32     12.3
 Prequema         13.4 0.676 12.1    11.88     14.8

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
 Postquema - Prequema    -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
 Postquema       12.86 1.17 12.1    10.31     15.4
 Prequema        11.99 1.17 12.1     9.44     14.5

zona = P:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 Postquema       11.39 1.17 12.1     8.84     13.9
 Prequema        12.45 1.17 12.1     9.90     15.0

zona = PR:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 Postquema        8.11 1.17 12.1     5.57     10.7
 Prequema        15.62 1.17 12.1    13.07     18.2

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
 Postquema - Prequema    0.872 0.869 129  1.004  0.3171 

zona = P:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema   -1.054 0.869 129 -1.213  0.2273 

zona = PR:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema   -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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        14.5 0.462 11.5     13.5     15.5
 Prequema         15.5 0.462 11.5     14.4     16.5

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
 Postquema - Prequema   -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
 Postquema        15.1 0.801 11.5     13.3     16.8
 Prequema         15.7 0.801 11.5     13.9     17.4

zona = P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        15.6 0.801 11.5     13.8     17.3
 Prequema         17.5 0.801 11.5     15.8     19.3

zona = PR:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        12.9 0.801 11.5     11.2     14.7
 Prequema         13.2 0.801 11.5     11.4     14.9

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
 Postquema - Prequema   -0.583 0.545 129 -1.071  0.2862 

zona = P:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema   -1.958 0.545 129 -3.595  0.0005 

zona = PR:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema   -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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        7.85 0.405 10.9     6.96     8.75
 Prequema         7.16 0.405 10.9     6.27     8.06

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
 Postquema - Prequema    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
 Postquema        7.27 0.702 10.9     5.72     8.81
 Prequema         6.50 0.702 10.9     4.96     8.05

zona = P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        8.73 0.702 10.9     7.18    10.27
 Prequema         7.94 0.702 10.9     6.39     9.48

zona = PR:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 Postquema        7.57 0.702 10.9     6.02     9.11
 Prequema         7.05 0.702 10.9     5.51     8.60

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
 Postquema - Prequema    0.765 0.425 129 1.799   0.0744 

zona = P:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema    0.790 0.425 129 1.858   0.0655 

zona = PR:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema    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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.560 0.0317 Inf     0.498     0.622
 Prequema        0.548 0.0316 Inf     0.486     0.610

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
 Postquema - Prequema   0.0113 0.0161 Inf 0.701   0.4831 

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 0.0532 Inf     0.422     0.630
 P     0.606 0.0516 Inf     0.505     0.707
 PR    0.531 0.0534 Inf     0.426     0.635

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  -0.07983 0.0739 Inf -1.081  0.5260 
 NP - PR -0.00489 0.0753 Inf -0.065  0.9977 
 P - PR   0.07494 0.0738 Inf  1.015  0.5674 

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
 Postquema       0.537 0.0550 Inf     0.429     0.645
 Prequema        0.515 0.0547 Inf     0.408     0.622

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.611 0.0537 Inf     0.506     0.716
 Prequema        0.600 0.0536 Inf     0.495     0.705

zona = PR:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.531 0.0550 Inf     0.423     0.639
 Prequema        0.530 0.0550 Inf     0.422     0.638

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
 Postquema - Prequema  0.02200 0.0271 Inf 0.812   0.4166 

zona = P:
 2                    estimate     SE  df z.ratio p.value
 Postquema - Prequema  0.01071 0.0297 Inf 0.361   0.7180 

zona = PR:
 2                    estimate     SE  df z.ratio p.value
 Postquema - Prequema  0.00108 0.0266 Inf 0.041   0.9675 

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema        2.37 0.173 Inf      2.03      2.71
 Prequema         2.13 0.168 Inf      1.80      2.46

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
 Postquema - Prequema    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  0.4105 
 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
 Postquema        2.02 0.283 Inf     1.466      2.58
 Prequema         1.99 0.283 Inf     1.438      2.55

zona = P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema        3.57 0.301 Inf     2.982      4.16
 Prequema         2.93 0.276 Inf     2.386      3.47

zona = PR:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema        1.52 0.308 Inf     0.914      2.12
 Prequema         1.46 0.307 Inf     0.860      2.06

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
 Postquema - Prequema   0.0297 0.150 Inf 0.198   0.8433 

zona = P:
 2                    estimate    SE  df z.ratio p.value
 Postquema - Prequema   0.6437 0.263 Inf 2.444   0.0145 

zona = PR:
 2                    estimate    SE  df z.ratio p.value
 Postquema - Prequema   0.0556 0.104 Inf 0.536   0.5920 

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.749 0.0803 Inf     0.592     0.907
 Prequema        0.784 0.0807 Inf     0.625     0.942

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
 Postquema - Prequema  -0.0345 0.0371 Inf -0.929  0.3527 

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  0.4031 
 NP - PR    0.212 0.196 Inf  1.080  0.5266 
 P - PR     0.445 0.190 Inf  2.340  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
 Postquema       0.711 0.135 Inf     0.445     0.977
 Prequema        0.807 0.137 Inf     0.538     1.076

zona = P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema       0.996 0.132 Inf     0.737     1.255
 Prequema        0.988 0.132 Inf     0.730     1.247

zona = PR:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema       0.540 0.146 Inf     0.255     0.826
 Prequema        0.555 0.146 Inf     0.269     0.841

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
 Postquema - Prequema -0.09632 0.0566 Inf -1.702  0.0887 

zona = P:
 2                    estimate     SE  df z.ratio p.value
 Postquema - Prequema  0.00742 0.0841 Inf  0.088  0.9297 

zona = PR:
 2                    estimate     SE  df z.ratio p.value
 Postquema - Prequema -0.01457 0.0461 Inf -0.316  0.7521 

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 Postquema      0.0281 0.00250 Inf    0.0232    0.0330
 Prequema       0.0269 0.00248 Inf    0.0221    0.0318

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
 Postquema - Prequema  0.00119 0.0016 Inf 0.743   0.4573 

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   0.8903 
 NP - PR  0.00514 0.00562 Inf 0.915   0.6310 
 P - PR   0.00257 0.00566 Inf 0.455   0.8924 

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
 Postquema      0.0312 0.00431 Inf    0.0227    0.0396
 Prequema       0.0290 0.00422 Inf    0.0207    0.0373

zona = P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 Postquema      0.0288 0.00429 Inf    0.0204    0.0372
 Prequema       0.0263 0.00421 Inf    0.0180    0.0345

zona = PR:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 Postquema      0.0244 0.00420 Inf    0.0162    0.0326
 Prequema       0.0255 0.00424 Inf    0.0172    0.0338

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
 Postquema - Prequema  0.00215 0.00305 Inf  0.706  0.4802 

zona = P:
 2                    estimate      SE  df z.ratio p.value
 Postquema - Prequema  0.00252 0.00269 Inf  0.939  0.3479 

zona = PR:
 2                    estimate      SE  df z.ratio p.value
 Postquema - Prequema -0.00111 0.00255 Inf -0.435  0.6637 

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema        1.68 0.0586 Inf      1.57      1.80
 Prequema         1.53 0.0631 Inf      1.40      1.65

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
 Postquema - Prequema    0.156 0.0745 Inf 2.100   0.0357 

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 0.0865 Inf      1.30      1.63
 PR     1.83 0.0777 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  0.455  0.8923 
 NP - PR  -0.3116 0.114 Inf -2.726  0.0176 
 P - PR   -0.3664 0.116 Inf -3.155  0.0046 

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
 Postquema        1.70 0.0984 Inf      1.51      1.89
 Prequema         1.34 0.1146 Inf      1.11      1.56

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema        1.59 0.1048 Inf      1.38      1.79
 Prequema         1.34 0.1159 Inf      1.11      1.57

zona = PR:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema        1.76 0.0984 Inf      1.57      1.95
 Prequema         1.90 0.0943 Inf      1.72      2.09

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
 Postquema - Prequema    0.361 0.131 Inf  2.761  0.0058 

zona = P:
 2                    estimate    SE  df z.ratio p.value
 Postquema - Prequema    0.247 0.137 Inf  1.797  0.0724 

zona = PR:
 2                    estimate    SE  df z.ratio p.value
 Postquema - Prequema   -0.139 0.114 Inf -1.218  0.2232 

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.233 0.0149 Inf     0.203     0.262
 Prequema        0.244 0.0152 Inf     0.215     0.274

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
 Postquema - Prequema  -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  1.033  0.5557 
 NP - PR  -0.1608 0.0312 Inf -5.156  <.0001 
 P - PR   -0.1869 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
 Postquema       0.174 0.0206 Inf     0.134     0.215
 Prequema        0.213 0.0231 Inf     0.168     0.258

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.165 0.0201 Inf     0.126     0.205
 Prequema        0.170 0.0204 Inf     0.130     0.210

zona = PR:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.359 0.0337 Inf     0.293     0.425
 Prequema        0.350 0.0330 Inf     0.286     0.415

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
 Postquema - Prequema -0.03836 0.0233 Inf -1.648  0.0993 

zona = P:
 2                    estimate     SE  df z.ratio p.value
 Postquema - Prequema -0.00480 0.0201 Inf -0.239  0.8112 

zona = PR:
 2                    estimate     SE  df z.ratio p.value
 Postquema - Prequema  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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df lower.CL upper.CL
 Postquema       -1.10 0.0678 136    -1.24   -0.968
 Prequema        -1.31 0.0710 136    -1.45   -1.171

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
 Postquema - Prequema     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
 Postquema      -0.940 0.112 136    -1.16   -0.718
 Prequema       -1.282 0.121 136    -1.52   -1.043

zona = P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 Postquema      -0.964 0.113 136    -1.19   -0.741
 Prequema       -1.269 0.120 136    -1.51   -1.031

zona = PR:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 Postquema      -1.403 0.124 136    -1.65   -1.157
 Prequema       -1.384 0.124 136    -1.63   -1.140

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
 Postquema - Prequema   0.3425 0.164 136  2.086  0.0388 

zona = P:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema   0.3055 0.164 136  1.860  0.0650 

zona = PR:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema  -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 6.9677    0.03069 *
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   0.403742   
zona                11.9557  2   0.002534 **
pre_post_quema:zona  0.2409  2   0.886524   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df lower.CL upper.CL
 Postquema       -3.08 0.0934 136    -3.26    -2.90
 Prequema        -3.00 0.0916 136    -3.18    -2.82

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
 Postquema - Prequema   -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
 Postquema       -2.91 0.154 136    -3.21    -2.60
 Prequema        -2.86 0.152 136    -3.16    -2.56

zona = P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 Postquema       -3.51 0.173 136    -3.85    -3.17
 Prequema        -3.36 0.168 136    -3.69    -3.03

zona = PR:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 Postquema       -2.83 0.151 136    -3.13    -2.53
 Prequema        -2.78 0.150 136    -3.08    -2.48

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
 Postquema - Prequema  -0.0499 0.144 136 -0.345  0.7303 

zona = P:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema  -0.1486 0.177 136 -0.837  0.4039 

zona = PR:
 2                    estimate    SE  df t.ratio p.value
 Postquema - Prequema  -0.0475 0.139 136 -0.343  0.7321 

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

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) )

NH4

prepara datos

              zona
pre_post_quema NP  P
     Postquema 24 23
     Prequema  24 24

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

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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.275 0.0439 Inf     0.189     0.361
 Prequema        1.630 0.1753 Inf     1.286     1.973

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
 Postquema - Prequema    -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
 Postquema       0.221 0.0517 Inf     0.120     0.322
 Prequema        1.678 0.2550 Inf     1.179     2.178

zona = P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 Postquema       0.330 0.0643 Inf     0.204     0.456
 Prequema        1.581 0.2404 Inf     1.110     2.052

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
 Postquema - Prequema    -1.46 0.254 Inf -5.729  <.0001 

zona = P:
 2                    estimate    SE  df z.ratio p.value
 Postquema - Prequema    -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-hocs

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema        1.26 0.124 Inf     1.019      1.50
 Prequema         1.14 0.118 Inf     0.908      1.37

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
 Postquema - Prequema    0.124 0.116 Inf 1.062   0.2883 

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
 Postquema        1.28 0.174 Inf     0.942      1.62
 Prequema         1.22 0.169 Inf     0.884      1.55

zona = P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 Postquema        1.24 0.173 Inf     0.903      1.58
 Prequema         1.06 0.158 Inf     0.750      1.37

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
 Postquema - Prequema   0.0671 0.169 Inf 0.397   0.6915 

zona = P:
 2                    estimate    SE  df z.ratio p.value
 Postquema - Prequema   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] glmmTMB_1.0.2.1    afex_0.28-1        performance_0.7.2  multcomp_1.4-16   
 [5] TH.data_1.0-10     mvtnorm_1.1-1      emmeans_1.5.4      lmerTest_3.1-3    
 [9] lme4_1.1-27.1      Matrix_1.3-2       fitdistrplus_1.1-3 survival_3.2-7    
[13] MASS_7.3-53        ggpubr_0.4.0       janitor_2.1.0      here_1.0.1        
[17] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4       
[21] readr_1.4.0        tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.5     
[25] tidyverse_1.3.1    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    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] pbkrtest_0.5-0.1    broom_0.7.9         dbplyr_2.1.1       
 [25] effectsize_0.4.5    compiler_4.0.2      httr_1.4.2         
 [28] backports_1.2.1     assertthat_0.2.1    fastmap_1.1.0      
 [31] cli_2.5.0           formatR_1.8         later_1.1.0.1      
 [34] htmltools_0.5.2     tools_4.0.2         coda_0.19-4        
 [37] gtable_0.3.0        glue_1.4.2          reshape2_1.4.4     
 [40] Rcpp_1.0.7          carData_3.0-4       cellranger_1.1.0   
 [43] jquerylib_0.1.3     vctrs_0.3.8         nlme_3.1-152       
 [46] insight_0.14.4      xfun_0.23           openxlsx_4.2.3     
 [49] rvest_1.0.0         lifecycle_1.0.0     rstatix_0.6.0      
 [52] zoo_1.8-8           scales_1.1.1        hms_1.0.0          
 [55] promises_1.2.0.1    parallel_4.0.2      sandwich_3.0-0     
 [58] TMB_1.7.19          yaml_2.2.1          curl_4.3           
 [61] gridExtra_2.3       see_0.6.4           sass_0.3.1         
 [64] stringi_1.7.4       bayestestR_0.9.0    highr_0.8          
 [67] plotrix_3.8-1       randomForest_4.6-14 boot_1.3-26        
 [70] zip_2.1.1           R2admb_0.7.16.2     rlang_0.4.10       
 [73] pkgconfig_2.0.3     evaluate_0.14       lattice_0.20-41    
 [76] labeling_0.4.2      tidyselect_1.1.1    plyr_1.8.6         
 [79] magrittr_2.0.1      bookdown_0.21.6     R6_2.5.0           
 [82] generics_0.1.0      DBI_1.1.1           pillar_1.6.1       
 [85] haven_2.3.1         foreign_0.8-81      withr_2.4.1        
 [88] abind_1.4-5         modelr_0.1.8        crayon_1.4.1       
 [91] car_3.0-10          utf8_1.1.4          rmarkdown_2.8      
 [94] grid_4.0.2          readxl_1.3.1        data.table_1.14.0  
 [97] git2r_0.28.0        reprex_2.0.0        digest_0.6.27      
[100] xtable_1.8-4       
 [ reached getOption("max.print") -- omitted 4 entries ]

Pre- and post-fire anaylisis of soils

Prepare data

Models

Humedad

Check distribution

Normality & Homocedasticity

Model

Post-hocs

CIC

Check distribution

Normality & Homocedasticity

Model

Post-hocs

% C

Check distribution

Normality & Homocedasticity

Model

Post-hocs

% Fe

Check distribution

Normality & Homocedasticity

Model

Post-hocs

% K

Check distribution

Normality & Homocedasticity

Model

Post-hocs

% Mg

Check distribution

Normality & Homocedasticity

Model

Post-hocs

C/N

Check distribution

Normality & Homocedasticity

Model

Post-hocs

P

Check distribution

Normality & Homocedasticity

Model

Post-hocs

MO

Check distribution

Normality & Homocedasticity

Model

Post-hocs

% N

Check distribution

Normality & Homocedasticity

Model

Post-hocs

% Na

Check distribution

Normality & Homocedasticity

Model

Post-hocs

NH4

Check distribution

Normality & Homocedasticity

Model

Post-hocs

NO3

Check distribution

Normality & Homocedasticity

Model

Post-hocs

Resumen general