Pre- and post-fire anaylisis of soils by Fire date
Last updated: 2021-09-14
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Knit directory: soil_alcontar/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 56e0949 | ajpelu | 2021-09-14 | change fechaQuema por estacion; momento por |
| html | 389b963 | ajpelu | 2021-09-14 | Build site. |
| Rmd | 9ba7a9d | ajpelu | 2021-09-14 | add analysis by date Fire |
Prepare data
raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"),
sheet = "SEGUIMIENTO_SUELOS_sin_ouliers") %>% janitor::clean_names() %>% mutate(treatment_name = case_when(str_detect(geo_parcela_nombre,
"NP_") ~ "Autumn Burning / No Browsing", str_detect(geo_parcela_nombre, "PR_") ~
"Spring Burning / Browsing", str_detect(geo_parcela_nombre, "P_") ~ "Autumn Burning / Browsing"),
zona = case_when(str_detect(geo_parcela_nombre, "NP_") ~ "QOt_NP", str_detect(geo_parcela_nombre,
"PR_") ~ "QPr_P", str_detect(geo_parcela_nombre, "P_") ~ "QOt_P"), estacion = case_when(str_detect(geo_parcela_nombre,
"NP_") ~ "Ot", str_detect(geo_parcela_nombre, "PR_") ~ "Pr", str_detect(geo_parcela_nombre,
"P_") ~ "Ot"), date = lubridate::ymd(fecha), fecha = case_when(pre_post_quema ==
"Prequema" ~ "0 preQuema", pre_post_quema == "Postquema" ~ "1 postQuema"))- Select data pre- and intermediately post-fire (first post-fire sampling: “2018-12-20” and “2019-05-09” for autumn and spring fires respectively)
soil <- raw_soil %>% filter(date %in% lubridate::ymd(c("2018-12-11", "2018-12-20",
"2019-04-24", "2019-05-09"))) %>% mutate(zona = as.factor(zona), fecha = as.factor(fecha))- Structure of the data
estacion
fecha Ot Pr
0 preQuema 48 24
1 postQuema 48 24Modelize
- For each response variable, the approach modelling is
\(Y \sim estacion (Ot|Pr) + fecha(pre|post) + estacion \times fecha\)
using the “(1|estacion:geo_parcela_nombre)” as nested random effects
Then explore error distribution of the variable response and model diagnostics
Select the appropiate error distribution and use LMM or GLMM
Explore Post-hoc
Plot interactions
Humedad
Model
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
fecha 461.74 461.74 1 130 50.4197 7.257e-11 ***
estacion 0.53 0.53 1 10 0.0575 0.8153
fecha:estacion 439.95 439.95 1 130 48.0407 1.748e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
fecha 461.7404592 461.7404592 1 130 50.41969020 7.256505e-11
estacion 0.5268064 0.5268064 1 10 0.05752456 8.152969e-01
fecha:estacion 439.9539281 439.9539281 1 130 48.04071273 1.748462e-10
variable factor
fecha humedad fecha
estacion humedad estacion
fecha:estacion humedad fecha:estacionPost-hoc
$`emmeans of fecha`
fecha emmean SE df lower.CL upper.CL
0 preQuema 13.9 0.689 13.8 12.44 15.4
1 postQuema 10.1 0.689 13.8 8.64 11.6
Results are averaged over the levels of: estacion
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 3.8 0.535 130 7.101 <.0001
Results are averaged over the levels of: estacion
Degrees-of-freedom method: kenward-roger $`emmeans of estacion`
estacion emmean SE df lower.CL upper.CL
Ot 12.2 0.734 10 10.54 13.8
Pr 11.9 1.038 10 9.56 14.2
Results are averaged over the levels of: fecha
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df t.ratio p.value
Ot - Pr 0.305 1.27 10 0.240 0.8153
Results are averaged over the levels of: fecha
Degrees-of-freedom method: kenward-roger $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df lower.CL upper.CL
0 preQuema 12.22 0.796 13.8 10.5 13.9
1 postQuema 12.13 0.796 13.8 10.4 13.8
estacion = Pr:
fecha emmean SE df lower.CL upper.CL
0 preQuema 15.62 1.126 13.8 13.2 18.0
1 postQuema 8.11 1.126 13.8 5.7 10.5
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0907 0.618 130 0.147 0.8835
estacion = Pr:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 7.5065 0.874 130 8.593 <.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)
fecha 18.503 18.503 1 130 5.1118 0.02543 *
estacion 35.605 35.605 1 10 9.8361 0.01058 *
fecha:estacion 8.337 8.337 1 130 2.3031 0.13154
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
fecha 18.503472 18.503472 1 130.000001 5.111751 0.02542616
estacion 35.604511 35.604511 1 9.999998 9.836067 0.01057742
fecha:estacion 8.336806 8.336806 1 130.000001 2.303118 0.13154272
variable factor
fecha cic fecha
estacion cic estacion
fecha:estacion cic fecha:estacionPost-hoc
$`emmeans of fecha`
fecha emmean SE df lower.CL upper.CL
0 preQuema 14.9 0.496 12.8 13.8 16.0
1 postQuema 14.1 0.496 12.8 13.1 15.2
Results are averaged over the levels of: estacion
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.76 0.336 130 2.261 0.0254
Results are averaged over the levels of: estacion
Degrees-of-freedom method: kenward-roger $`emmeans of estacion`
estacion emmean SE df lower.CL upper.CL
Ot 16 0.539 10 14.8 17.2
Pr 13 0.762 10 11.3 14.7
Results are averaged over the levels of: fecha
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df t.ratio p.value
Ot - Pr 2.93 0.933 10 3.136 0.0106
Results are averaged over the levels of: fecha
Degrees-of-freedom method: kenward-roger $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df lower.CL upper.CL
0 preQuema 16.6 0.573 12.8 15.4 17.8
1 postQuema 15.3 0.573 12.8 14.1 16.6
estacion = Pr:
fecha emmean SE df lower.CL upper.CL
0 preQuema 13.2 0.810 12.8 11.4 14.9
1 postQuema 12.9 0.810 12.8 11.2 14.7
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 1.27 0.388 130 3.272 0.0014
estacion = Pr:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.25 0.549 130 0.455 0.6497
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)
fecha 13.3214 13.3214 1 130 6.1796 0.01419 *
estacion 0.2542 0.2542 1 10 0.1179 0.73843
fecha:estacion 0.5636 0.5636 1 130 0.2614 0.61001
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
fecha 13.3214014 13.3214014 1 130.000000 6.1795805 0.01419189
estacion 0.2541669 0.2541669 1 9.999999 0.1179039 0.73842869
fecha:estacion 0.5635681 0.5635681 1 130.000000 0.2614300 0.61000674
variable factor
fecha c_percent fecha
estacion c_percent estacion
fecha:estacion c_percent fecha:estacionPost-hoc
$`emmeans of fecha`
fecha emmean SE df lower.CL upper.CL
0 preQuema 7.14 0.455 11.8 6.14 8.13
1 postQuema 7.78 0.455 11.8 6.79 8.77
Results are averaged over the levels of: estacion
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.645 0.26 130 -2.486 0.0142
Results are averaged over the levels of: estacion
Degrees-of-freedom method: kenward-roger $`emmeans of estacion`
estacion emmean SE df lower.CL upper.CL
Ot 7.61 0.503 10 6.49 8.73
Pr 7.31 0.712 10 5.72 8.90
Results are averaged over the levels of: fecha
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df t.ratio p.value
Ot - Pr 0.299 0.872 10 0.343 0.7384
Results are averaged over the levels of: fecha
Degrees-of-freedom method: kenward-roger $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df lower.CL upper.CL
0 preQuema 7.22 0.525 11.8 6.07 8.37
1 postQuema 8.00 0.525 11.8 6.85 9.14
estacion = Pr:
fecha emmean SE df lower.CL upper.CL
0 preQuema 7.05 0.743 11.8 5.43 8.67
1 postQuema 7.57 0.743 11.8 5.95 9.19
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.778 0.300 130 -2.596 0.0105
estacion = Pr:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.512 0.424 130 -1.209 0.2288
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 0.28 .598
2 estacion 1, 10 0.46 .512
3 fecha:estacion 1, 130 0.21 .648
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: fe_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 0.2796 0.5979
estacion 1 10 0.4631 0.5116
fecha:estacion 1 130 0.2093 0.6481Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.545 0.036 Inf 0.475 0.616
1 postQuema 0.554 0.036 Inf 0.484 0.625
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00898 0.0167 Inf -0.539 0.5898
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 0.568 0.0398 Inf 0.490 0.646
Pr 0.532 0.0573 Inf 0.419 0.644
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr 0.0364 0.0695 Inf 0.524 0.6006
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.559 0.0410 Inf 0.479 0.640
1 postQuema 0.576 0.0411 Inf 0.496 0.657
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.531 0.0588 Inf 0.416 0.646
1 postQuema 0.532 0.0588 Inf 0.417 0.647
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.01688 0.0200 Inf -0.843 0.3991
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00109 0.0266 Inf -0.041 0.9675
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 0.60 .439
2 estacion 1, 10 35.18 *** <.001
3 fecha:estacion 1, 130 0.91 .341
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: mo ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 0.6020 0.4392419
estacion 1 10 35.1850 0.0001448 ***
fecha:estacion 1 130 0.9143 0.3407472
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.271 0.0190 Inf 0.233 0.308
1 postQuema 0.265 0.0191 Inf 0.228 0.302
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00557 0.0229 Inf 0.243 0.8082
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 0.181 0.0143 Inf 0.153 0.209
Pr 0.355 0.0265 Inf 0.303 0.407
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr -0.175 0.0298 Inf -5.859 <.0001
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.190 0.0166 Inf 0.158 0.223
1 postQuema 0.171 0.0158 Inf 0.140 0.202
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.351 0.0339 Inf 0.285 0.417
1 postQuema 0.359 0.0345 Inf 0.292 0.427
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.01929 0.0152 Inf 1.269 0.2044
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00815 0.0433 Inf -0.188 0.8507
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 1.92 .168
2 estacion 1, 10 8.33 * .016
3 fecha:estacion 1, 130 0.00 .976
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: k_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 1.9189 0.16835
estacion 1 10 8.3254 0.01624 *
fecha:estacion 1 130 0.0009 0.97555
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 2.06 0.286 Inf 1.50 2.62
1 postQuema 2.18 0.287 Inf 1.62 2.74
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.119 0.0844 Inf -1.414 0.1574
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 2.73 0.287 Inf 2.167 3.29
Pr 1.51 0.487 Inf 0.554 2.46
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr 1.22 0.565 Inf 2.161 0.0307
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 2.64 0.294 Inf 2.063 3.21
1 postQuema 2.82 0.296 Inf 2.242 3.40
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.48 0.490 Inf 0.522 2.44
1 postQuema 1.54 0.490 Inf 0.576 2.50
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.1833 0.132 Inf -1.386 0.1657
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.0554 0.105 Inf -0.528 0.5975
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 0.96 .329
2 estacion 1, 10 4.01 + .073
3 fecha:estacion 1, 130 0.13 .719
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: mg_percent ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 0.9587 0.32933
estacion 1 10 4.0114 0.07304 .
fecha:estacion 1 130 0.1300 0.71903
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.738 0.0953 Inf 0.551 0.925
1 postQuema 0.699 0.0950 Inf 0.513 0.885
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.0394 0.0329 Inf 1.197 0.2313
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 0.888 0.100 Inf 0.691 1.084
Pr 0.549 0.158 Inf 0.240 0.858
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr 0.338 0.186 Inf 1.816 0.0693
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.920 0.103 Inf 0.717 1.122
1 postQuema 0.856 0.102 Inf 0.655 1.056
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.557 0.159 Inf 0.244 0.869
1 postQuema 0.542 0.159 Inf 0.230 0.854
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.0643 0.0469 Inf 1.371 0.1702
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.0146 0.0463 Inf 0.315 0.7530
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 0.05 .815
2 estacion 1, 10 0.56 .471
3 fecha:estacion 1, 130 0.80 .372
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: c_n ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 0.0550 0.8150
estacion 1 10 0.5601 0.4715
fecha:estacion 1 130 0.8031 0.3718Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.0266 0.00265 Inf 0.0214 0.0318
1 postQuema 0.0272 0.00265 Inf 0.0220 0.0324
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.000626 0.00163 Inf -0.385 0.7000
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 0.0288 0.00291 Inf 0.0231 0.0345
Pr 0.0250 0.00408 Inf 0.0170 0.0330
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr 0.00388 0.00496 Inf 0.781 0.4346
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.0277 0.00305 Inf 0.0217 0.0336
1 postQuema 0.0300 0.00311 Inf 0.0239 0.0361
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.0255 0.00429 Inf 0.0171 0.0339
1 postQuema 0.0244 0.00425 Inf 0.0161 0.0327
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.00236 0.00202 Inf -1.171 0.2415
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00111 0.00255 Inf 0.435 0.6636
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 0.45 .502
2 estacion 1, 10 8.17 * .017
3 fecha:estacion 1, 130 9.31 ** .003
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 0.4536 0.501823
estacion 1 10 8.1689 0.017011 *
fecha:estacion 1 130 9.3060 0.002769 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.62 0.0628 Inf 1.50 1.74
1 postQuema 1.70 0.0619 Inf 1.58 1.82
Results are averaged over the levels of: estacion
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.0834 0.0746 Inf -1.118 0.2635
Results are averaged over the levels of: estacion
Results are given on the log (not the response) scale. $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 1.49 0.0614 Inf 1.37 1.61
Pr 1.83 0.0788 Inf 1.68 1.99
Results are averaged over the levels of: fecha
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr -0.338 0.1 Inf -3.379 0.0007
Results are averaged over the levels of: fecha
Results are given on the log (not the response) scale. $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.34 0.0819 Inf 1.18 1.50
1 postQuema 1.65 0.0730 Inf 1.50 1.79
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 1.90 0.0951 Inf 1.71 2.09
1 postQuema 1.76 0.0994 Inf 1.57 1.96
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.306 0.0947 Inf -3.227 0.0012
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.139 0.1140 Inf 1.217 0.2235
Results are given on the log (not the response) scale. N
Model
Post-hoc
$`emmeans of fecha`
fecha emmean SE df lower.CL upper.CL
0 preQuema -1.33 0.0758 138 -1.48 -1.18
1 postQuema -1.18 0.0742 138 -1.32 -1.03
Results are averaged over the levels of: estacion
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.153 0.105 138 -1.461 0.1463
Results are averaged over the levels of: estacion
Results are given on the log odds ratio (not the response) scale. $`emmeans of estacion`
estacion emmean SE df lower.CL upper.CL
Ot -1.11 0.0590 138 -1.23 -0.997
Pr -1.39 0.0883 138 -1.57 -1.219
Results are averaged over the levels of: fecha
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df t.ratio p.value
Ot - Pr 0.28 0.105 138 2.673 0.0084
Results are averaged over the levels of: fecha
Results are given on the log odds ratio (not the response) scale. $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df lower.CL upper.CL
0 preQuema -1.276 0.0857 138 -1.45 -1.106
1 postQuema -0.952 0.0798 138 -1.11 -0.794
estacion = Pr:
fecha emmean SE df lower.CL upper.CL
0 preQuema -1.384 0.1237 138 -1.63 -1.140
1 postQuema -1.403 0.1243 138 -1.65 -1.157
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema -0.3240 0.116 138 -2.789 0.0060
estacion = Pr:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0182 0.174 138 0.104 0.9171
Results are given on the log odds ratio (not the response) scale. Na
Model
Post-hoc
$`emmeans of fecha`
fecha emmean SE df lower.CL upper.CL
0 preQuema -2.95 0.116 138 -3.18 -2.72
1 postQuema -3.02 0.117 138 -3.25 -2.78
Results are averaged over the levels of: estacion
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0699 0.0893 138 0.784 0.4346
Results are averaged over the levels of: estacion
Results are given on the log odds ratio (not the response) scale. $`emmeans of estacion`
estacion emmean SE df lower.CL upper.CL
Ot -3.15 0.126 138 -3.40 -2.90
Pr -2.81 0.172 138 -3.15 -2.47
Results are averaged over the levels of: fecha
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df t.ratio p.value
Ot - Pr -0.341 0.212 138 -1.606 0.1106
Results are averaged over the levels of: fecha
Results are given on the log odds ratio (not the response) scale. $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df lower.CL upper.CL
0 preQuema -3.10 0.137 138 -3.38 -2.83
1 postQuema -3.20 0.139 138 -3.47 -2.92
estacion = Pr:
fecha emmean SE df lower.CL upper.CL
0 preQuema -2.79 0.186 138 -3.15 -2.42
1 postQuema -2.83 0.186 138 -3.20 -2.47
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0936 0.112 138 0.833 0.4065
estacion = Pr:
2 estimate SE df t.ratio p.value
0 preQuema - 1 postQuema 0.0463 0.139 138 0.334 0.7389
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 0.99 .322
2 estacion 1, 10 9.29 * .012
3 fecha:estacion 1, 130 10.51 ** .002
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_agua_eez ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 0.9889 0.321856
estacion 1 10 9.2903 0.012294 *
fecha:estacion 1 130 10.5108 0.001508 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.1255 0.0003116 Inf 0.1249 0.1261
1 postQuema 0.1251 0.0003101 Inf 0.1245 0.1257
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00039 0.000397 Inf 0.982 0.3261
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 0.1259 0.0002773 Inf 0.1254 0.1265
Pr 0.1247 0.0003898 Inf 0.1239 0.1255
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr 0.00123 0.000478 Inf 2.570 0.0102
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.125 0.000359 Inf 0.125 0.126
1 postQuema 0.126 0.000362 Inf 0.126 0.127
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.126 0.000509 Inf 0.125 0.127
1 postQuema 0.124 0.000503 Inf 0.123 0.125
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema -0.000918 0.000460 Inf -1.994 0.0461
estacion = Pr:
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 ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
Effect df F p.value
1 fecha 1, 130 45.36 *** <.001
2 estacion 1, 10 5.70 * .038
3 fecha:estacion 1, 130 9.39 ** .003
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_k_cl ~ fecha * estacion + (1 | estacion:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
fecha 1 130 45.3634 4.776e-10 ***
estacion 1 10 5.7006 0.038115 *
fecha:estacion 1 130 9.3932 0.002649 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of fecha`
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.134 0.000631 Inf 0.132 0.135
1 postQuema 0.131 0.000628 Inf 0.130 0.132
Results are averaged over the levels of: estacion
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha`
1 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00263 0.000383 Inf 6.876 <.0001
Results are averaged over the levels of: estacion
Note: contrasts are still on the inverse scale $`emmeans of estacion`
estacion emmean SE df asymp.LCL asymp.UCL
Ot 0.133 0.000689 Inf 0.132 0.135
Pr 0.131 0.000981 Inf 0.129 0.133
Results are averaged over the levels of: fecha
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of estacion`
1 estimate SE df z.ratio p.value
Ot - Pr 0.00216 0.0012 Inf 1.806 0.0710
Results are averaged over the levels of: fecha
Note: contrasts are still on the inverse scale $`emmeans of fecha | estacion`
estacion = Ot:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.134 0.000725 Inf 0.133 0.135
1 postQuema 0.133 0.000724 Inf 0.131 0.134
estacion = Pr:
fecha emmean SE df asymp.LCL asymp.UCL
0 preQuema 0.133 0.001032 Inf 0.131 0.135
1 postQuema 0.129 0.001026 Inf 0.127 0.131
Results are given on the inverse (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of fecha | estacion`
estacion = Ot:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00147 0.000446 Inf 3.291 0.0010
estacion = Pr:
2 estimate SE df z.ratio p.value
0 preQuema - 1 postQuema 0.00379 0.000621 Inf 6.105 <.0001
Note: contrasts are still on the inverse scale NH4
- Prepare data
- We have only data for Autumn fire
- The approach will be the following: Apply non-parametric Wilcoxon test to compare pre and postFire
fecha
estacion 0 preQuema 1 postQuema
Ot 48 43Model
NO3
Model
General Overview
Mean + SE table
| Characteristic | 0 preQuema | 1 postQuema | ||
|---|---|---|---|---|
| Ot, N = 481 | Pr, N = 241 | Ot, N = 481 | Pr, N = 241 | |
| humedad | 12.22 (0.50) | 15.62 (0.63) | 12.13 (0.59) | 8.11 (0.53) |
| n_nh4 | 0.62 (0.03) | NA (NA) | 2.91 (0.36) | NA (NA) |
| n_no3 | 0.92 (0.07) | NA (NA) | 0.81 (0.07) | NA (NA) |
| fe_percent | 1.86 (0.07) | 1.95 (0.08) | 1.80 (0.04) | 1.95 (0.09) |
| k_percent | 0.45 (0.03) | 0.79 (0.06) | 0.41 (0.03) | 0.75 (0.06) |
| mg_percent | 1.29 (0.09) | 2.00 (0.14) | 1.42 (0.12) | 2.06 (0.18) |
| na_percent | 0.04 (0.00) | 0.06 (0.01) | 0.04 (0.00) | 0.06 (0.01) |
| n_percent | 0.22 (0.01) | 0.19 (0.02) | 0.29 (0.02) | 0.18 (0.01) |
| c_percent | 7.22 (0.30) | 7.05 (0.31) | 8.00 (0.29) | 7.57 (0.38) |
| c_n | 39.72 (2.77) | 41.62 (3.62) | 36.14 (3.32) | 43.72 (3.06) |
| cic | 16.60 (0.34) | 13.17 (0.49) | 15.33 (0.29) | 12.92 (0.55) |
| p | 3.83 (0.17) | 6.75 (0.92) | 5.20 (0.24) | 5.88 (0.25) |
| mo | 5.37 (0.30) | 2.87 (0.32) | 6.00 (0.39) | 2.80 (0.16) |
| p_h_k_cl | 7.46 (0.02) | 7.52 (0.04) | 7.54 (0.02) | 7.74 (0.03) |
| p_h_agua_eez | 7.97 (0.02) | 7.97 (0.04) | 7.91 (0.02) | 8.07 (0.02) |
|
1
Mean (std.error)
|
||||
| Characteristic | Ot | Pr | ||
|---|---|---|---|---|
| 0 preQuema, N = 481 | 1 postQuema, N = 481 | 0 preQuema, N = 241 | 1 postQuema, N = 241 | |
| humedad | 12.22 (0.50) | 12.13 (0.59) | 15.62 (0.63) | 8.11 (0.53) |
| n_nh4 | 0.62 (0.03) | 2.91 (0.36) | NA (NA) | NA (NA) |
| n_no3 | 0.92 (0.07) | 0.81 (0.07) | NA (NA) | NA (NA) |
| fe_percent | 1.86 (0.07) | 1.80 (0.04) | 1.95 (0.08) | 1.95 (0.09) |
| k_percent | 0.45 (0.03) | 0.41 (0.03) | 0.79 (0.06) | 0.75 (0.06) |
| mg_percent | 1.29 (0.09) | 1.42 (0.12) | 2.00 (0.14) | 2.06 (0.18) |
| na_percent | 0.04 (0.00) | 0.04 (0.00) | 0.06 (0.01) | 0.06 (0.01) |
| n_percent | 0.22 (0.01) | 0.29 (0.02) | 0.19 (0.02) | 0.18 (0.01) |
| c_percent | 7.22 (0.30) | 8.00 (0.29) | 7.05 (0.31) | 7.57 (0.38) |
| c_n | 39.72 (2.77) | 36.14 (3.32) | 41.62 (3.62) | 43.72 (3.06) |
| cic | 16.60 (0.34) | 15.33 (0.29) | 13.17 (0.49) | 12.92 (0.55) |
| p | 3.83 (0.17) | 5.20 (0.24) | 6.75 (0.92) | 5.88 (0.25) |
| mo | 5.37 (0.30) | 6.00 (0.39) | 2.87 (0.32) | 2.80 (0.16) |
| p_h_k_cl | 7.46 (0.02) | 7.54 (0.02) | 7.52 (0.04) | 7.74 (0.03) |
| p_h_agua_eez | 7.97 (0.02) | 7.91 (0.02) | 7.97 (0.04) | 8.07 (0.02) |
|
1
Mean (std.error)
|
||||
Figures
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
Anovas table
| Variables | F | p | F | p | F | p |
|---|---|---|---|---|---|---|
| c_n | 0.560 | 0.471 | 0.055 | 0.815 | 0.803 | 0.372 |
| cic | 9.836 | 0.011 | 5.112 | 0.025 | 2.303 | 0.132 |
| k_percent | 8.325 | 0.016 | 1.919 | 0.168 | 0.001 | 0.976 |
| mg_percent | 4.011 | 0.073 | 0.959 | 0.329 | 0.130 | 0.719 |
| mo | 35.185 | 0.000 | 0.602 | 0.439 | 0.914 | 0.341 |
| n_nh4 | NA | NA | 403.000 | 0.000 | NA | NA |
| n_no3 | NA | NA | 1198.500 | 0.187 | NA | NA |
| p | 8.169 | 0.017 | 0.454 | 0.502 | 9.306 | 0.003 |
| p_h_agua_eez | 9.290 | 0.012 | 0.989 | 0.322 | 10.511 | 0.002 |
| p_h_k_cl | 5.701 | 0.038 | 45.363 | 0.000 | 9.393 | 0.003 |
| humedad | 0.058 | 0.815 | 50.420 | 0.000 | 48.041 | 0.000 |
| n_percent | 7.294 | 0.007 | 5.121 | 0.024 | 2.671 | 0.102 |
| c_percent | 0.118 | 0.738 | 6.180 | 0.014 | 0.261 | 0.610 |
| na_percent | 2.571 | 0.109 | 0.735 | 0.391 | 0.070 | 0.791 |
Gráficos feos feísimos
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
| Version | Author | Date |
|---|---|---|
| 389b963 | ajpelu | 2021-09-14 |
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] kableExtra_1.3.1 gtsummary_1.4.2 plotrix_3.8-1 glmmTMB_1.0.2.1
[5] afex_0.28-1 performance_0.7.2 multcomp_1.4-16 TH.data_1.0-10
[9] mvtnorm_1.1-1 emmeans_1.5.4 lmerTest_3.1-3 lme4_1.1-27.1
[13] Matrix_1.3-2 fitdistrplus_1.1-3 survival_3.2-7 MASS_7.3-53
[17] ggpubr_0.4.0 janitor_2.1.0 here_1.0.1 forcats_0.5.1
[21] stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0
[25] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
[29] rmdformats_1.0.1 knitr_1.31 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_2.0-0 ggsignif_0.6.0
[4] ellipsis_0.3.2 rio_0.5.16 rprojroot_2.0.2
[7] estimability_1.3 snakecase_0.11.0 fs_1.5.0
[10] rstudioapi_0.13 farver_2.0.3 fansi_0.4.2
[13] lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18
[16] splines_4.0.2 jsonlite_1.7.2 nloptr_1.2.2.2
[19] gt_0.3.0 pbkrtest_0.5-0.1 broom_0.7.9
[22] dbplyr_2.1.1 compiler_4.0.2 httr_1.4.2
[25] backports_1.2.1 assertthat_0.2.1 fastmap_1.1.0
[28] cli_2.5.0 formatR_1.8 later_1.1.0.1
[31] htmltools_0.5.2 tools_4.0.2 coda_0.19-4
[34] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[37] Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
[40] jquerylib_0.1.3 vctrs_0.3.8 nlme_3.1-152
[43] broom.helpers_1.3.0 insight_0.14.4 xfun_0.23
[46] openxlsx_4.2.3 rvest_1.0.0 lifecycle_1.0.0
[49] rstatix_0.6.0 zoo_1.8-8 scales_1.1.1
[52] hms_1.0.0 promises_1.2.0.1 parallel_4.0.2
[55] sandwich_3.0-0 TMB_1.7.19 yaml_2.2.1
[58] curl_4.3 sass_0.3.1 stringi_1.7.4
[61] highr_0.8 checkmate_2.0.0 boot_1.3-26
[64] zip_2.1.1 commonmark_1.7 rlang_0.4.10
[67] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41
[70] labeling_0.4.2 tidyselect_1.1.1 plyr_1.8.6
[73] magrittr_2.0.1 bookdown_0.21.6 R6_2.5.0
[76] generics_0.1.0 DBI_1.1.1 pillar_1.6.1
[79] haven_2.3.1 whisker_0.4 foreign_0.8-81
[82] withr_2.4.1 abind_1.4-5 modelr_0.1.8
[85] crayon_1.4.1 car_3.0-10 utf8_1.1.4
[88] rmarkdown_2.8 grid_4.0.2 readxl_1.3.1
[91] data.table_1.14.0 git2r_0.28.0 webshot_0.5.2
[94] reprex_2.0.0 digest_0.6.27 xtable_1.8-4
[97] httpuv_1.5.5 numDeriv_2016.8-1.1 munsell_0.5.0
[100] viridisLite_0.3.0 bslib_0.2.4