Pre- and post-fire anaylisis of soils
Last updated: 2022-02-17
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Knit directory: soil_alcontar/
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Prepare data
raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"),
sheet = "SEGUIMIENTO_SUELOS_sin_ouliers") %>% janitor::clean_names() %>% mutate(treatment_name = case_when(str_detect(geo_parcela_nombre,
"NP_") ~ "Autumn Burning / No Browsing", str_detect(geo_parcela_nombre, "PR_") ~
"Spring Burning / Browsing", str_detect(geo_parcela_nombre, "P_") ~ "Autumn Burning / Browsing"),
zona = case_when(str_detect(geo_parcela_nombre, "NP_") ~ "QOt_NP", str_detect(geo_parcela_nombre,
"PR_") ~ "QPr_P", str_detect(geo_parcela_nombre, "P_") ~ "QOt_P"), fecha = lubridate::ymd(fecha),
pre_post_quema = case_when(pre_post_quema == "Prequema" ~ "0 preQuema", pre_post_quema ==
"Postquema" ~ "1 postQuema"))- Select data pre- and intermediately post-fire (first post-fire sampling: “2018-12-20” and “2019-05-09” for autumn and spring fires respectively)
soil <- raw_soil %>% filter(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 QOt_NP QOt_P QPr_P
0 preQuema 24 24 24
1 postQuema 24 24 24Modelize
- 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
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 Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
pre_post_quema 236.415514 236.4155136 1 129 26.10495006 1.135929e-06
zona 1.455293 0.7276463 2 9 0.08034655 9.234509e-01
pre_post_quema:zona 462.217132 231.1085659 2 129 25.51895804 4.592612e-10
variable factor
pre_post_quema humedad pre_post_quema
zona humedad zona
pre_post_quema:zona humedad pre_post_quema:zonaPost-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 13.4 0.676 12.1 11.88 14.8
1 postQuema 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 preQuema - 1 postQuema 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
QOt_NP 12.4 1.09 9 9.97 14.9
QOt_P 11.9 1.09 9 9.46 14.4
QPr_P 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
QOt_NP - QOt_P 0.5060 1.54 9 0.329 0.9424
QOt_NP - QPr_P 0.5579 1.54 9 0.363 0.9306
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 11.99 1.17 12.1 9.44 14.5
1 postQuema 12.86 1.17 12.1 10.31 15.4
zona = QOt_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 12.45 1.17 12.1 9.90 15.0
1 postQuema 11.39 1.17 12.1 8.84 13.9
zona = QPr_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 15.62 1.17 12.1 13.07 18.2
1 postQuema 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 = QOt_NP:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.872 0.869 129 -1.004 0.3171
zona = QOt_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 1.054 0.869 129 1.213 0.2273
zona = QPr_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 7.507 0.869 129 8.641 <.0001
Degrees-of-freedom method: kenward-roger CIC
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 Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
pre_post_quema 31.17361 31.173609 1 129 8.756838 0.003670913
zona 40.29686 20.148428 2 9 5.659804 0.025613698
pre_post_quema:zona 19.68056 9.840278 2 129 2.764188 0.066763596
variable factor
pre_post_quema cic pre_post_quema
zona cic zona
pre_post_quema:zona cic pre_post_quema:zonaPost-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 15.5 0.462 11.5 14.4 16.5
1 postQuema 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 preQuema - 1 postQuema 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
QOt_NP 15.4 0.753 9 13.7 17.1
QOt_P 16.6 0.753 9 14.9 18.3
QPr_P 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
QOt_NP - QOt_P -1.19 1.06 9 -1.115 0.5291
QOt_NP - QPr_P 2.33 1.06 9 2.191 0.1262
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 15.7 0.801 11.5 13.9 17.4
1 postQuema 15.1 0.801 11.5 13.3 16.8
zona = QOt_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 17.5 0.801 11.5 15.8 19.3
1 postQuema 15.6 0.801 11.5 13.8 17.3
zona = QPr_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 13.2 0.801 11.5 11.4 14.9
1 postQuema 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 = QOt_NP:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.583 0.545 129 1.071 0.2862
zona = QOt_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 1.958 0.545 129 3.595 0.0005
zona = QPr_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.250 0.545 129 0.459 0.6470
Degrees-of-freedom method: kenward-roger C
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 Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
pre_post_quema 17.1120115 17.112011 1 129 7.8770265 0.00578378
zona 5.3707569 2.685378 2 9 1.2361374 0.33548133
pre_post_quema:zona 0.5673181 0.283659 2 129 0.1305743 0.87770709
variable factor
pre_post_quema c_percent pre_post_quema
zona c_percent zona
pre_post_quema:zona c_percent pre_post_quema:zonaPost-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 7.16 0.405 10.9 6.27 8.06
1 postQuema 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 preQuema - 1 postQuema -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
QOt_NP 6.89 0.669 9 5.37 8.40
QOt_P 8.33 0.669 9 6.82 9.84
QPr_P 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
QOt_NP - QOt_P -1.446 0.945 9 -1.529 0.3233
QOt_NP - QPr_P -0.424 0.945 9 -0.448 0.8965
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 6.50 0.702 10.9 4.96 8.05
1 postQuema 7.27 0.702 10.9 5.72 8.81
zona = QOt_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 7.94 0.702 10.9 6.39 9.48
1 postQuema 8.73 0.702 10.9 7.18 10.27
zona = QPr_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema 7.05 0.702 10.9 5.51 8.60
1 postQuema 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 = QOt_NP:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.765 0.425 129 -1.799 0.0744
zona = QOt_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.790 0.425 129 -1.858 0.0655
zona = QPr_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.512 0.425 129 -1.205 0.2306
Degrees-of-freedom method: kenward-roger Fe
Model
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 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 129 0.5186 0.4727
zona 2 9 0.6903 0.5261
pre_post_quema:zona 2 129 0.1688 0.8449Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.549 0.00599 Inf 0.537 0.561
1 postQuema 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 preQuema - 1 postQuema -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
QOt_NP 0.526 0.00599 Inf 0.514 0.538
QOt_P 0.607 0.00848 Inf 0.590 0.623
QPr_P 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
QOt_NP - QOt_P -0.08067 0.00609 Inf -13.241 <.0001
QOt_NP - QPr_P -0.00486 0.00608 Inf -0.799 0.7036
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.515 0.00543 Inf 0.504 0.526
1 postQuema 0.537 0.00747 Inf 0.522 0.552
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.601 0.00768 Inf 0.586 0.616
1 postQuema 0.612 0.01058 Inf 0.591 0.633
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.530 0.00767 Inf 0.515 0.545
1 postQuema 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 = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.02193 0.00519 Inf -4.225 <.0001
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.01062 0.00737 Inf -1.442 0.1493
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00106 0.00732 Inf -0.144 0.8853
Note: contrasts are still on the inverse scale MO
Model
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 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 129 1.3496 0.2474987
zona 2 9 20.3726 0.0004557 ***
pre_post_quema:zona 2 129 1.0383 0.3569810
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.244 0.0152 Inf 0.215 0.274
1 postQuema 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 preQuema - 1 postQuema 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
QOt_NP 0.194 0.0186 Inf 0.157 0.230
QOt_P 0.168 0.0176 Inf 0.133 0.202
QPr_P 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
QOt_NP - QOt_P 0.0261 0.0253 Inf 1.034 0.5556
QOt_NP - QPr_P -0.1608 0.0312 Inf -5.156 <.0001
QOt_P - QPr_P -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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.213 0.0231 Inf 0.168 0.258
1 postQuema 0.174 0.0206 Inf 0.134 0.215
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.170 0.0204 Inf 0.130 0.210
1 postQuema 0.165 0.0201 Inf 0.126 0.205
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.350 0.0330 Inf 0.286 0.415
1 postQuema 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 = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.03837 0.0233 Inf 1.648 0.0993
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00480 0.0201 Inf 0.239 0.8111
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00816 0.0432 Inf -0.189 0.8502
Note: contrasts are still on the inverse scale K
Model
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 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 129 2.1884 0.1415
zona 2 9 6.7329 0.0163 *
pre_post_quema:zona 2 129 0.4302 0.6513
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 2.13 0.168 Inf 1.80 2.46
1 postQuema 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 preQuema - 1 postQuema -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
QOt_NP 2.01 0.273 Inf 1.472 2.54
QOt_P 3.25 0.257 Inf 2.746 3.75
QPr_P 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
QOt_NP - QOt_P -1.242 0.372 Inf -3.339 0.0024
QOt_NP - QPr_P 0.517 0.406 Inf 1.273 0.4106
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.99 0.283 Inf 1.438 2.55
1 postQuema 2.02 0.283 Inf 1.466 2.58
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 2.93 0.276 Inf 2.386 3.47
1 postQuema 3.57 0.301 Inf 2.982 4.16
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.46 0.307 Inf 0.860 2.06
1 postQuema 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 = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.0297 0.150 Inf -0.198 0.8433
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.6437 0.263 Inf -2.444 0.0145
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.0556 0.104 Inf -0.536 0.5921
Note: contrasts are still on the inverse scale Mg
Model
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 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 129 1.3653 0.24477
zona 2 9 3.0516 0.09734 .
pre_post_quema:zona 2 129 0.8402 0.43395
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.784 0.0807 Inf 0.625 0.942
1 postQuema 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 preQuema - 1 postQuema 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
QOt_NP 0.759 0.133 Inf 0.498 1.02
QOt_P 0.992 0.125 Inf 0.747 1.24
QPr_P 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
QOt_NP - QOt_P -0.233 0.181 Inf -1.286 0.4032
QOt_NP - QPr_P 0.212 0.196 Inf 1.080 0.5264
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.807 0.137 Inf 0.538 1.076
1 postQuema 0.711 0.135 Inf 0.446 0.977
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.988 0.132 Inf 0.730 1.247
1 postQuema 0.996 0.132 Inf 0.737 1.255
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.555 0.146 Inf 0.269 0.841
1 postQuema 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 = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.09632 0.0566 Inf 1.702 0.0887
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00743 0.0841 Inf -0.088 0.9296
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.01456 0.0461 Inf 0.316 0.7523
Note: contrasts are still on the inverse scale C/N
Model
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 7.82 ** .006
2 zona 2, 9 15.56 ** .001
3 pre_post_quema:zona 2, 129 2.37 + .098
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 129 7.8222 0.005951 **
zona 2 9 15.5645 0.001198 **
pre_post_quema:zona 2 129 2.3682 0.097712 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.0829 0.00412 Inf 0.0748 0.0909
1 postQuema 0.0913 0.00425 Inf 0.0830 0.0996
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 preQuema - 1 postQuema -0.00845 0.00383 Inf -2.206 0.0274
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
QOt_NP 0.0796 0.00627 Inf 0.0673 0.0919
QOt_P 0.0676 0.00631 Inf 0.0553 0.0800
QPr_P 0.1140 0.00657 Inf 0.1011 0.1268
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
QOt_NP - QOt_P 0.0120 0.00884 Inf 1.355 0.3648
QOt_NP - QPr_P -0.0343 0.00903 Inf -3.804 0.0004
QOt_P - QPr_P -0.0463 0.00908 Inf -5.101 <.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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.0735 0.00672 Inf 0.0603 0.0866
1 postQuema 0.0858 0.00711 Inf 0.0719 0.0997
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.0607 0.00659 Inf 0.0478 0.0737
1 postQuema 0.0746 0.00698 Inf 0.0609 0.0882
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.1144 0.00785 Inf 0.0990 0.1298
1 postQuema 0.1136 0.00781 Inf 0.0982 0.1289
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.01236 0.00585 Inf -2.111 0.0348
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.01382 0.00503 Inf -2.750 0.0060
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00083 0.00851 Inf 0.097 0.9224
Note: contrasts are still on the inverse scale P
Model
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 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 129 3.2017 0.075909 .
zona 2 9 3.8067 0.063389 .
pre_post_quema:zona 2 129 4.8757 0.009093 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.53 0.0628 Inf 1.40 1.65
1 postQuema 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 preQuema - 1 postQuema -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
QOt_NP 1.52 0.0844 Inf 1.35 1.69
QOt_P 1.46 0.0870 Inf 1.29 1.64
QPr_P 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
QOt_NP - QOt_P 0.0548 0.121 Inf 0.451 0.8938
QOt_NP - QPr_P -0.3116 0.114 Inf -2.734 0.0172
QOt_P - QPr_P -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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.34 0.1122 Inf 1.12 1.56
1 postQuema 1.70 0.0998 Inf 1.50 1.90
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.34 0.1165 Inf 1.11 1.57
1 postQuema 1.59 0.1053 Inf 1.38 1.79
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.90 0.0936 Inf 1.72 2.08
1 postQuema 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 = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.361 0.129 Inf -2.808 0.0050
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.247 0.138 Inf -1.790 0.0734
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.139 0.114 Inf 1.217 0.2235
Results are given on the log (not the response) scale. N
Model
Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -1.31 0.0710 136 -1.45 -1.171
1 postQuema -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 preQuema - 1 postQuema -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
QOt_NP -1.11 0.0827 136 -1.27 -0.947
QOt_P -1.12 0.0828 136 -1.28 -0.953
QPr_P -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
QOt_NP - QOt_P 0.00552 0.116 136 0.048 0.9988
QOt_NP - QPr_P 0.28266 0.120 136 2.362 0.0510
QOt_P - QPr_P 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 = QOt_NP:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -1.282 0.121 136 -1.52 -1.043
1 postQuema -0.940 0.112 136 -1.16 -0.718
zona = QOt_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -1.269 0.120 136 -1.51 -1.031
1 postQuema -0.964 0.113 136 -1.19 -0.741
zona = QPr_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -1.384 0.124 136 -1.63 -1.140
1 postQuema -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 = QOt_NP:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.3425 0.164 136 -2.086 0.0388
zona = QOt_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.3055 0.164 136 -1.860 0.0650
zona = QPr_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0182 0.174 136 0.104 0.9171
Results are given on the log odds ratio (not the response) scale. Na
Model
Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -3.00 0.0916 136 -3.18 -2.82
1 postQuema -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 preQuema - 1 postQuema 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
QOt_NP -2.88 0.134 136 -3.15 -2.61
QOt_P -3.43 0.146 136 -3.72 -3.15
QPr_P -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
QOt_NP - QOt_P 0.5523 0.196 136 2.816 0.0153
QOt_NP - QPr_P -0.0772 0.188 136 -0.411 0.9113
QOt_P - QPr_P -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 = QOt_NP:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -2.86 0.152 136 -3.16 -2.56
1 postQuema -2.91 0.154 136 -3.21 -2.60
zona = QOt_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -3.36 0.168 136 -3.69 -3.03
1 postQuema -3.51 0.173 136 -3.85 -3.17
zona = QPr_P:
pre_post_quema emmean SE df lower.CL upper.CL
0 preQuema -2.78 0.150 136 -3.08 -2.48
1 postQuema -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 = QOt_NP:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0499 0.144 136 0.345 0.7303
zona = QOt_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.1486 0.177 136 0.837 0.4039
zona = QPr_P:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0475 0.139 136 0.343 0.7321
Results are given on the log odds ratio (not the response) scale. pH agua
Model
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_agua_eez ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 pre_post_quema 1, 129 0.01 .928
2 zona 2, 9 4.64 * .041
3 pre_post_quema:zona 2, 129 5.20 ** .007
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_agua_eez ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pre_post_quema 1 129 0.0083 0.927741
zona 2 9 4.6444 0.041140 *
pre_post_quema:zona 2 129 5.2024 0.006716 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.1255 0.0002922 Inf 0.1249 0.1261
1 postQuema 0.1255 0.0002926 Inf 0.1250 0.1261
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 preQuema - 1 postQuema -4.66e-05 0.000375 Inf -0.124 0.9012
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
QOt_NP 0.126 0.000389 Inf 0.125 0.127
QOt_P 0.126 0.000389 Inf 0.125 0.127
QPr_P 0.125 0.000387 Inf 0.124 0.125
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
QOt_NP - QOt_P 0.000164 0.000550 Inf 0.299 0.9519
QOt_NP - QPr_P 0.001311 0.000548 Inf 2.393 0.0441
QOt_P - QPr_P 0.001147 0.000549 Inf 2.090 0.0920
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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.126 0.000503 Inf 0.125 0.127
1 postQuema 0.126 0.000509 Inf 0.125 0.127
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.125 0.000506 Inf 0.124 0.126
1 postQuema 0.126 0.000509 Inf 0.125 0.127
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.126 0.000507 Inf 0.125 0.127
1 postQuema 0.124 0.000501 Inf 0.123 0.125
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.000834 0.000649 Inf -1.285 0.1989
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.001003 0.000652 Inf -1.539 0.1238
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.001697 0.000646 Inf 2.628 0.0086
Note: contrasts are still on the inverse scale pH KCl
Model
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_k_cl ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 pre_post_quema 1, 129 37.25 *** <.001
2 zona 2, 9 2.57 .131
3 pre_post_quema:zona 2, 129 6.18 ** .003
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_k_cl ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pre_post_quema 1 129 37.246 1.133e-08 ***
zona 2 9 2.573 0.130687
pre_post_quema:zona 2 129 6.183 0.002728 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.134 0.000592 Inf 0.133 0.135
1 postQuema 0.131 0.000591 Inf 0.130 0.133
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 preQuema - 1 postQuema 0.00224 0.000359 Inf 6.253 <.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
QOt_NP 0.133 0.000973 Inf 0.131 0.135
QOt_P 0.133 0.000974 Inf 0.131 0.135
QPr_P 0.131 0.000980 Inf 0.129 0.133
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
QOt_NP - QOt_P 9.55e-05 0.00138 Inf 0.069 0.9973
QOt_NP - QPr_P 2.21e-03 0.00138 Inf 1.603 0.2444
QOt_P - QPr_P 2.12e-03 0.00138 Inf 1.533 0.2755
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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.134 0.00102 Inf 0.132 0.136
1 postQuema 0.133 0.00102 Inf 0.131 0.135
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.134 0.00102 Inf 0.132 0.136
1 postQuema 0.132 0.00102 Inf 0.130 0.134
zona = QPr_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.133 0.00103 Inf 0.131 0.135
1 postQuema 0.129 0.00102 Inf 0.127 0.131
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.000697 0.000624 Inf 1.116 0.2644
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.002241 0.000624 Inf 3.588 0.0003
zona = QPr_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.003792 0.000614 Inf 6.173 <.0001
Note: contrasts are still on the inverse scale NH4
- prepara datos
zona
pre_post_quema QOt_NP QOt_P
0 preQuema 24 24
1 postQuema 21 22Model
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, 81.31 42.84 *** <.001
2 zona 1, 6.01 2.19 .189
3 pre_post_quema:zona 1, 81.31 2.53 .115
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 81.3060 42.8372 4.956e-09 ***
zona 1 6.0102 2.1923 0.1891
pre_post_quema:zona 1 81.3060 2.5343 0.1153
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.636 0.1547 Inf 1.332 1.939
1 postQuema 0.383 0.0587 Inf 0.268 0.498
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 preQuema - 1 postQuema 1.25 0.153 Inf 8.158 <.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
QOt_NP 0.996 0.125 Inf 0.752 1.24
QOt_P 1.023 0.123 Inf 0.783 1.26
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
QOt_NP - QOt_P -0.0273 0.173 Inf -0.158 0.8748
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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.682 0.2245 Inf 1.242 2.122
1 postQuema 0.310 0.0708 Inf 0.171 0.449
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.589 0.2121 Inf 1.174 2.005
1 postQuema 0.457 0.0878 Inf 0.285 0.629
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 1.37 0.221 Inf 6.214 <.0001
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 1.13 0.213 Inf 5.324 <.0001
Note: contrasts are still on the inverse scale NO3
Model
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, 81.11 0.93 .339
2 zona 1, 6.01 0.22 .658
3 pre_post_quema:zona 1, 81.11 0.25 .619
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting 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
num Df den Df F Pr(>F)
pre_post_quema 1 81.1146 0.9263 0.3387
zona 1 6.0094 0.2164 0.6581
pre_post_quema:zona 1 81.1146 0.2492 0.6190Post-hoc
$`emmeans of pre_post_quema`
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.17 0.137 Inf 0.899 1.44
1 postQuema 1.28 0.144 Inf 1.001 1.57
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 preQuema - 1 postQuema -0.116 0.116 Inf -1.004 0.3155
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
QOt_NP 1.29 0.181 Inf 0.934 1.64
QOt_P 1.16 0.175 Inf 0.819 1.50
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
QOt_NP - QOt_P 0.127 0.247 Inf 0.513 0.6078
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 = QOt_NP:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.26 0.196 Inf 0.874 1.64
1 postQuema 1.32 0.204 Inf 0.920 1.72
zona = QOt_P:
pre_post_quema emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.08 0.185 Inf 0.713 1.44
1 postQuema 1.25 0.198 Inf 0.859 1.64
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.0616 0.170 Inf -0.362 0.7170
zona = QOt_P:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.1710 0.157 Inf -1.086 0.2775
Note: contrasts are still on the inverse scale General Overview
Mean + SE table
| Characteristic | 0 preQuema | 1 postQuema | ||||
|---|---|---|---|---|---|---|
| QOt_NP, N = 241 | QOt_P, N = 241 | QPr_P, N = 241 | QOt_NP, N = 241 | QOt_P, N = 241 | QPr_P, N = 241 | |
| humedad | 11.99 (0.76) | 12.45 (0.66) | 15.62 (0.63) | 12.86 (0.86) | 11.39 (0.79) | 8.11 (0.53) |
| n_nh4 | 0.60 (0.05) | 0.63 (0.05) | NA (NA) | 3.42 (0.57) | 2.44 (0.45) | NA (NA) |
| n_no3 | 0.85 (0.11) | 0.98 (0.08) | NA (NA) | 0.81 (0.12) | 0.82 (0.08) | NA (NA) |
| fe_percent | 1.97 (0.08) | 1.76 (0.12) | 1.95 (0.08) | 1.89 (0.04) | 1.72 (0.07) | 1.95 (0.09) |
| k_percent | 0.55 (0.04) | 0.35 (0.03) | 0.79 (0.06) | 0.54 (0.03) | 0.29 (0.02) | 0.75 (0.06) |
| mg_percent | 1.46 (0.15) | 1.11 (0.09) | 2.00 (0.14) | 1.74 (0.21) | 1.10 (0.07) | 2.06 (0.18) |
| na_percent | 0.05 (0.01) | 0.03 (0.00) | 0.06 (0.01) | 0.05 (0.00) | 0.03 (0.00) | 0.06 (0.01) |
| n_percent | 0.22 (0.02) | 0.21 (0.02) | 0.19 (0.02) | 0.30 (0.04) | 0.28 (0.03) | 0.18 (0.01) |
| c_percent | 6.50 (0.33) | 7.94 (0.46) | 7.05 (0.31) | 7.27 (0.42) | 8.73 (0.35) | 7.57 (0.38) |
| c_n | 13.96 (0.89) | 16.77 (0.87) | 8.81 (1.03) | 11.88 (0.72) | 13.59 (0.74) | 8.87 (0.22) |
| cic | 15.67 (0.38) | 17.54 (0.49) | 13.17 (0.49) | 15.08 (0.36) | 15.58 (0.47) | 12.92 (0.55) |
| p | 3.83 (0.23) | 3.84 (0.24) | 6.75 (0.92) | 5.50 (0.33) | 4.91 (0.35) | 5.88 (0.25) |
| mo | 4.77 (0.39) | 5.97 (0.44) | 2.87 (0.32) | 5.86 (0.65) | 6.15 (0.46) | 2.80 (0.16) |
| p_h_k_cl | 7.48 (0.02) | 7.44 (0.03) | 7.52 (0.04) | 7.52 (0.03) | 7.57 (0.03) | 7.74 (0.03) |
| p_h_agua_eez | 7.96 (0.03) | 7.98 (0.03) | 7.97 (0.04) | 7.91 (0.02) | 7.92 (0.03) | 8.07 (0.02) |
|
1
Mean (std.error)
|
||||||
Anovas table
| Variables | F | p | F | p | F | p |
|---|---|---|---|---|---|---|
| c_n | 15.564 | 0.001 | 7.822 | 0.006 | 2.368 | 0.098 |
| cic | 5.660 | 0.026 | 8.757 | 0.004 | 2.764 | 0.067 |
| k_percent | 6.733 | 0.016 | 2.188 | 0.141 | 0.430 | 0.651 |
| mg_percent | 3.052 | 0.097 | 1.365 | 0.245 | 0.840 | 0.434 |
| mo | 20.373 | 0.000 | 1.350 | 0.247 | 1.038 | 0.357 |
| n_nh4 | 2.192 | 0.189 | 42.837 | 0.000 | 2.534 | 0.115 |
| n_no3 | 0.216 | 0.658 | 0.926 | 0.339 | 0.249 | 0.619 |
| p | 3.807 | 0.063 | 3.202 | 0.076 | 4.876 | 0.009 |
| p_h_agua_eez | 4.644 | 0.041 | 0.008 | 0.928 | 5.202 | 0.007 |
| p_h_k_cl | 2.573 | 0.131 | 37.246 | 0.000 | 6.183 | 0.003 |
| humedad | 0.080 | 0.923 | 26.105 | 0.000 | 25.519 | 0.000 |
| n_percent | 7.301 | 0.026 | 5.122 | 0.024 | 2.697 | 0.260 |
| c_percent | 1.236 | 0.335 | 7.877 | 0.006 | 0.131 | 0.878 |
| na_percent | 11.955 | 0.003 | 0.697 | 0.404 | 0.241 | 0.887 |
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] kableExtra_1.3.1 gtsummary_1.4.2 plotrix_3.8-1 glmmTMB_1.0.2.1
[5] afex_0.28-1 performance_0.8.0 multcomp_1.4-16 TH.data_1.0-10
[9] mvtnorm_1.1-1 emmeans_1.5.4 lmerTest_3.1-3 lme4_1.1-27.1
[13] Matrix_1.3-2 fitdistrplus_1.1-3 survival_3.2-7 MASS_7.3-53
[17] ggpubr_0.4.0 janitor_2.1.0 here_1.0.1 forcats_0.5.1
[21] stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0
[25] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
[29] rmdformats_1.0.1 knitr_1.31 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_2.0-2 ggsignif_0.6.0
[4] ellipsis_0.3.2 rio_0.5.16 rprojroot_2.0.2
[7] estimability_1.3 snakecase_0.11.0 fs_1.5.0
[10] rstudioapi_0.13 farver_2.1.0 fansi_0.4.2
[13] lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18
[16] splines_4.0.2 jsonlite_1.7.2 nloptr_1.2.2.2
[19] gt_0.3.0 pbkrtest_0.5-0.1 broom_0.7.9
[22] dbplyr_2.1.1 compiler_4.0.2 httr_1.4.2
[25] backports_1.2.1 assertthat_0.2.1 fastmap_1.1.0
[28] cli_2.5.0 formatR_1.8 later_1.1.0.1
[31] htmltools_0.5.2 tools_4.0.2 coda_0.19-4
[34] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[37] Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
[40] jquerylib_0.1.3 vctrs_0.3.8 nlme_3.1-152
[43] broom.helpers_1.4.0 insight_0.14.4 xfun_0.23
[46] ps_1.5.0 openxlsx_4.2.3 rvest_1.0.0
[49] lifecycle_1.0.1 rstatix_0.6.0 zoo_1.8-8
[52] getPass_0.2-2 scales_1.1.1.9000 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] sass_0.3.1 stringi_1.7.4 highr_0.8
[64] checkmate_2.0.0 boot_1.3-26 zip_2.1.1
[67] commonmark_1.7 rlang_0.4.12 pkgconfig_2.0.3
[70] evaluate_0.14 lattice_0.20-41 labeling_0.4.2
[73] processx_3.5.1 tidyselect_1.1.1 plyr_1.8.6
[76] magrittr_2.0.1 bookdown_0.21.6 R6_2.5.1
[79] generics_0.1.0 DBI_1.1.1 pillar_1.6.1
[82] haven_2.3.1 whisker_0.4 foreign_0.8-81
[85] withr_2.4.1 abind_1.4-5 modelr_0.1.8
[88] crayon_1.4.1 car_3.0-10 utf8_1.1.4
[91] rmarkdown_2.8 grid_4.0.2 readxl_1.3.1
[94] data.table_1.14.0 callr_3.7.0 git2r_0.28.0
[97] webshot_0.5.2 reprex_2.0.0 digest_0.6.27
[100] xtable_1.8-4
[ reached getOption("max.print") -- omitted 5 entries ]