Last updated: 2022-02-17
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Introduction
Analysis of temporal evolution of soil parameters along time.
Only for Autumn treatment (i.e. zona == “P”; zona == “NP”)
Interpret zona as “grazing effect”:
zona == “P” corresponds to Browsing
zona == “NP” corresponds to No Browsing
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"))
Compute date as months after fire
autumn_fire <- lubridate::ymd("2018-12-18")
soil <- raw_soil %>% filter(zona != "QPr_P") %>% filter(fecha != "2018-12-11") %>%
mutate(zona = as.factor(zona)) %>% mutate(meses = as.factor(as.character(lubridate::interval(autumn_fire,
lubridate::ymd(fecha))%/%months(1)))) %>% mutate(pastoreo = as.factor(case_when(zona ==
"QOt_P" ~ "Browsing", zona == "QOt_NP" ~ "No Browsing"))) %>% relocate(pastoreo,
fecha, meses) %>% dplyr::select(-pre_post_quema, -tratamiento)
xtabs(~meses + pastoreo, data = soil) pastoreo
meses Browsing No Browsing
0 24 24
22 25 25
29 24 24# sss <- soil %>% dplyr::select(meses, pastoreo, ca_percent)
Modelize
For each response variable, the approach modelling is
\(Y \sim pastoreo (Browsing|NoBrowsing)+ Fecha(0|22|29) + zona \times Fecha\)
using the “(1|pastoreo:geo_parcela_nombre)” as nested random effects
Humedad
humedad ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
pastoreo 4.60 4.597 1 6.005 0.6233 0.4598622
meses 362.22 181.110 2 133.044 24.5534 8.395e-10 ***
pastoreo:meses 132.90 66.452 2 133.044 9.0090 0.0002141 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
t.ratio
p.value
0 - 22
3.6611
0.5490
133.0276
6.6684
0.0000
0 - 29
0.8268
0.5575
133.0164
1.4830
0.3023
22 - 29
-2.8343
0.5524
133.0747
-5.1307
0.0000
meses
pastoreo
contrast
estimate
SE
df
t.ratio
p.value
0
.
No Browsing - Browsing
1.4692
1.4030
9.5543
1.0472
0.9333
22
.
No Browsing - Browsing
-3.1741
1.3946
9.3270
-2.2760
0.2908
29
.
No Browsing - Browsing
-1.2509
1.4080
9.6864
-0.8884
0.9706
.
Browsing
22 - 0
-1.3395
0.7764
133.0287
-1.7251
0.4705
.
Browsing
29 - 22
1.8727
0.7860
133.1214
2.3826
0.1232
.
No Browsing
22 - 0
-5.9827
0.7764
133.0266
-7.7054
0.0000
.
No Browsing
29 - 22
3.7959
0.7764
133.0266
4.8889
0.0000
CIC
cic ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
pastoreo 4.21 4.210 1 5.979 1.3273 0.2933
meses 435.43 217.716 2 133.999 68.6311 <2e-16 ***
pastoreo:meses 2.97 1.485 2 133.999 0.4682 0.6271
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
t.ratio
p.value
0 - 22
0.325
0.3600
134.0292
0.9028
0.6395
0 - 29
-3.500
0.3636
134.0000
-9.6269
0.0000
22 - 29
-3.825
0.3600
134.0292
-10.6238
0.0000
meses
pastoreo
contrast
estimate
SE
df
t.ratio
p.value
0
.
No Browsing - Browsing
-0.5000
0.8904
9.9201
-0.5616
0.9979
22
.
No Browsing - Browsing
-1.1280
0.8846
9.6682
-1.2751
0.8425
29
.
No Browsing - Browsing
-1.0833
0.8904
9.9201
-1.2167
0.8688
.
Browsing
22 - 0
-0.0111
0.5092
134.0292
-0.0217
1.0000
.
Browsing
29 - 22
3.8027
0.5092
134.0292
7.4683
0.0000
.
No Browsing
22 - 0
-0.6390
0.5092
134.0292
-1.2550
0.8107
.
No Browsing
29 - 22
3.8474
0.5092
134.0292
7.5560
0.0000
C
c_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
pastoreo 2.9927 2.9927 1 5.995 1.5033 0.2661352
meses 30.1228 15.0614 2 134.001 7.5658 0.0007706 ***
pastoreo:meses 0.5280 0.2640 2 134.001 0.1326 0.8759091
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
t.ratio
p.value
0 - 22
1.0925
0.2852
134.0095
3.8303
0.0006
0 - 29
0.7256
0.2880
134.0000
2.5194
0.0343
22 - 29
-0.3669
0.2852
134.0095
-1.2863
0.4053
meses
pastoreo
contrast
estimate
SE
df
t.ratio
p.value
0
.
No Browsing - Browsing
-1.4583
1.1364
7.1824
-1.2833
0.8525
22
.
No Browsing - Browsing
-1.1712
1.1336
7.1116
-1.0332
0.9427
29
.
No Browsing - Browsing
-1.3662
1.1364
7.1824
-1.2022
0.8868
.
Browsing
22 - 0
-1.2360
0.4034
134.0095
-3.0643
0.0183
.
Browsing
29 - 22
0.4644
0.4034
134.0095
1.1513
0.8686
.
No Browsing
22 - 0
-0.9489
0.4034
134.0095
-2.3525
0.1325
.
No Browsing
29 - 22
0.2694
0.4034
134.0095
0.6679
0.9928
Fe
fe_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: fe_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 6 0.3182 0.59314
meses 2 134 195.2049 < 2e-16 ***
pastoreo:meses 2 134 3.3444 0.03825 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
0.0451
0.0103
Inf
4.3856
0
0 - 29
0.1858
0.0092
Inf
20.2649
0
22 - 29
0.1406
0.0085
Inf
16.5696
0
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
-0.0998
0.0830
Inf
-1.2019
0.8386
22
.
No Browsing - Browsing
-0.0552
0.0828
Inf
-0.6671
0.9927
29
.
No Browsing - Browsing
-0.0941
0.0822
Inf
-1.1443
0.8696
.
Browsing
22 - 0
-0.0674
0.0148
Inf
-4.5577
0.0000
.
Browsing
29 - 22
-0.1212
0.0121
Inf
-10.0407
0.0000
.
No Browsing
22 - 0
-0.0228
0.0143
Inf
-1.5959
0.5594
.
No Browsing
29 - 22
-0.1601
0.0119
Inf
-13.4113
0.0000
K
k_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: k_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 6.00 3.5557 0.108303
meses 2 134.01 477.4712 < 2.2e-16 ***
pastoreo:meses 2 134.01 6.3832 0.002249 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
0.5391
0.1111
Inf
4.8522
0
0 - 29
1.7591
0.0935
Inf
18.8131
0
22 - 29
1.2200
0.0725
Inf
16.8366
0
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
-1.6632
0.2534
Inf
-6.5632
0.0000
22
.
No Browsing - Browsing
-0.3766
0.2246
Inf
-1.6770
0.4972
29
.
No Browsing - Browsing
-0.1544
0.1900
Inf
-0.8125
0.9770
.
Browsing
22 - 0
-1.1824
0.1867
Inf
-6.3323
0.0000
.
Browsing
29 - 22
-1.3311
0.1096
Inf
-12.1412
0.0000
.
No Browsing
22 - 0
0.1042
0.1204
Inf
0.8654
0.9674
.
No Browsing
29 - 22
-1.1089
0.0947
Inf
-11.7063
0.0000
Mg
mg_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: mg_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 6 0.7514 0.419348
meses 2 134 3.1375 0.046594 *
pastoreo:meses 2 134 4.7869 0.009817 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
-0.0295
0.0403
Inf
-0.7309
0.7451
0 - 29
0.0742
0.0376
Inf
1.9735
0.1188
22 - 29
0.1037
0.0377
Inf
2.7478
0.0165
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
-0.3266
0.2138
Inf
-1.5275
0.6124
22
.
No Browsing - Browsing
-0.1644
0.2139
Inf
-0.7685
0.9832
29
.
No Browsing - Browsing
-0.0761
0.2120
Inf
-0.3591
0.9999
.
Browsing
22 - 0
-0.0516
0.0659
Inf
-0.7831
0.9813
.
Browsing
29 - 22
-0.1478
0.0576
Inf
-2.5634
0.0703
.
No Browsing
22 - 0
0.1106
0.0465
Inf
2.3798
0.1151
.
No Browsing
29 - 22
-0.0596
0.0487
Inf
-1.2234
0.8262
C/N
c_n ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: c_n ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 6.0002 2.1619 0.1919
meses 2 133.0248 61.8754 <2e-16 ***
pastoreo:meses 2 133.0248 0.9364 0.3946
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
0.0029
0.0030
Inf
0.9680
0.5972
0 - 29
-0.0401
0.0040
Inf
-10.0033
0.0000
22 - 29
-0.0431
0.0039
Inf
-10.9013
0.0000
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
0.0088
0.0085
Inf
1.0277
0.9210
22
.
No Browsing - Browsing
0.0109
0.0084
Inf
1.2943
0.7820
29
.
No Browsing - Browsing
0.0119
0.0099
Inf
1.1917
0.8444
.
Browsing
22 - 0
-0.0040
0.0039
Inf
-1.0175
0.9247
.
Browsing
29 - 22
0.0426
0.0052
Inf
8.2492
0.0000
.
No Browsing
22 - 0
-0.0019
0.0046
Inf
-0.4045
0.9997
.
No Browsing
29 - 22
0.0435
0.0060
Inf
7.2804
0.0000
MO
mo ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: mo ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 5.9992 0.4239 0.5391
meses 2 134.0502 20.5449 1.649e-08 ***
pastoreo:meses 2 134.0502 0.0116 0.9885
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
-0.0558
0.0162
Inf
-3.4368
0.0017
0 - 29
-0.1241
0.0199
Inf
-6.2501
0.0000
22 - 29
-0.0684
0.0216
Inf
-3.1659
0.0044
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
0.0148
0.0337
Inf
0.4410
0.9995
22
.
No Browsing - Browsing
0.0262
0.0376
Inf
0.6964
0.9905
29
.
No Browsing - Browsing
0.0350
0.0441
Inf
0.7930
0.9799
.
Browsing
22 - 0
0.0501
0.0221
Inf
2.2684
0.1522
.
Browsing
29 - 22
0.0640
0.0290
Inf
2.2053
0.1769
.
No Browsing
22 - 0
0.0614
0.0238
Inf
2.5847
0.0663
.
No Browsing
29 - 22
0.0727
0.0320
Inf
2.2739
0.1501
pH Agua
p_h_agua_eez ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_agua_eez ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 5.9999 0.8325 0.39674
meses 2 134.0153 23.1121 2.379e-09 ***
pastoreo:meses 2 134.0153 4.7246 0.01041 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
0.0002
3e-04
Inf
0.7249
0.7487
0 - 29
-0.0020
3e-04
Inf
-5.6670
0.0000
22 - 29
-0.0022
3e-04
Inf
-6.4407
0.0000
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
0.0001
0.0011
Inf
0.0564
1.0000
22
.
No Browsing - Browsing
-0.0004
0.0011
Inf
-0.3918
0.9998
29
.
No Browsing - Browsing
-0.0021
0.0011
Inf
-1.8180
0.3941
.
Browsing
22 - 0
0.0000
0.0005
Inf
0.0143
1.0000
.
Browsing
29 - 22
0.0031
0.0005
Inf
6.2022
0.0000
.
No Browsing
22 - 0
-0.0005
0.0005
Inf
-1.0401
0.9162
.
No Browsing
29 - 22
0.0014
0.0005
Inf
2.8942
0.0263
pH KCl
p_h_k_cl ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p_h_k_cl ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 5.9999 0.0072 0.93499
meses 2 134.0178 17.3528 1.987e-07 ***
pastoreo:meses 2 134.0178 3.0542 0.05046 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
-0.0005
4e-04
Inf
-1.1937
0.4569
0 - 29
-0.0024
4e-04
Inf
-5.7629
0.0000
22 - 29
-0.0019
4e-04
Inf
-4.6216
0.0000
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
0.0009
0.0013
Inf
0.6690
0.9926
22
.
No Browsing - Browsing
0.0001
0.0013
Inf
0.0618
1.0000
29
.
No Browsing - Browsing
-0.0013
0.0013
Inf
-0.9795
0.9377
.
Browsing
22 - 0
0.0009
0.0006
Inf
1.5184
0.6194
.
Browsing
29 - 22
0.0026
0.0006
Inf
4.4015
0.0001
.
No Browsing
22 - 0
0.0001
0.0006
Inf
0.1738
1.0000
.
No Browsing
29 - 22
0.0013
0.0006
Inf
2.1315
0.2096
NH4
# A tibble: 1 x 3
# Groups: meses, fecha [1]
meses fecha n
<fct> <date> <int>
1 0 2018-12-20 44
NO3
# A tibble: 1 x 3
# Groups: meses, fecha [1]
meses fecha n
<fct> <date> <int>
1 0 2018-12-20 47
P
p ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]Mixed Model Anova Table (Type 3 tests, KR-method)
Model: p ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
num Df den Df F Pr(>F)
pastoreo 1 5.9996 0.0232 0.8840
meses 2 134.0368 12.0243 1.574e-05 ***
pastoreo:meses 2 134.0368 1.5180 0.2229
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
z.ratio
p.value
0 - 22
0.1346
0.0954
Inf
1.4112
0.3350
0 - 29
0.5383
0.1077
Inf
4.9980
0.0000
22 - 29
0.4038
0.1095
Inf
3.6865
0.0007
meses
pastoreo
contrast
estimate
SE
df
z.ratio
p.value
0
.
No Browsing - Browsing
0.1041
0.1722
Inf
0.6045
0.9960
22
.
No Browsing - Browsing
0.1040
0.1767
Inf
0.5889
0.9966
29
.
No Browsing - Browsing
-0.2714
0.2032
Inf
-1.3357
0.7542
.
Browsing
22 - 0
-0.1345
0.1383
Inf
-0.9728
0.9398
.
Browsing
29 - 22
-0.2161
0.1506
Inf
-1.4343
0.6834
.
No Browsing
22 - 0
-0.1346
0.1313
Inf
-1.0250
0.9220
.
No Browsing
29 - 22
-0.5915
0.1590
Inf
-3.7194
0.0014
N
n_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: n_percent
Chisq Df Pr(>Chisq)
pastoreo 0.0361 1 0.849276
meses 12.5842 2 0.001851 **
pastoreo:meses 0.4221 2 0.809750
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
t.ratio
p.value
0 - 22
0.3931
0.1114
137
3.5284
0.0016
0 - 29
0.2217
0.1109
137
1.9989
0.1163
22 - 29
-0.1714
0.1144
137
-1.4980
0.2951
meses
pastoreo
contrast
estimate
SE
df
t.ratio
p.value
0
.
No Browsing - Browsing
0.0250
0.1612
137
0.1551
1.0000
22
.
No Browsing - Browsing
0.0160
0.1711
137
0.0936
1.0000
29
.
No Browsing - Browsing
-0.1061
0.1697
137
-0.6255
0.9951
.
Browsing
22 - 0
-0.3887
0.1578
137
-2.4635
0.1004
.
Browsing
29 - 22
0.2325
0.1601
137
1.4518
0.6764
.
No Browsing
22 - 0
-0.3976
0.1574
137
-2.5268
0.0852
.
No Browsing
29 - 22
0.1103
0.1635
137
0.6748
0.9923
Na
na_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: na_percent
Chisq Df Pr(>Chisq)
pastoreo 3.3148 1 0.0686596 .
meses 188.1372 2 < 2.2e-16 ***
pastoreo:meses 17.5761 2 0.0001525 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post-hoc
meses
estimate
SE
df
t.ratio
p.value
0 - 22
-0.3395
0.0750
138
-4.5284
0
0 - 29
-0.9219
0.0695
138
-13.2591
0
22 - 29
-0.5824
0.0616
138
-9.4576
0
meses
pastoreo
contrast
estimate
SE
df
t.ratio
p.value
0
.
No Browsing - Browsing
0.6720
0.1683
138
3.9932
0.0007
22
.
No Browsing - Browsing
0.1388
0.1555
138
0.8923
0.9622
29
.
No Browsing - Browsing
0.1200
0.1447
138
0.8295
0.9746
.
Browsing
22 - 0
0.6061
0.1152
138
5.2589
0.0000
.
Browsing
29 - 22
0.5918
0.0892
138
6.6373
0.0000
.
No Browsing
22 - 0
0.0729
0.0959
138
0.7605
0.9844
.
No Browsing
29 - 22
0.5730
0.0849
138
6.7521
0.0000
General Overview
Mean + SE table
Characteristic
Browsing
No Browsing
0 , N = 2422 , N = 2529 , N = 240 , N = 2422 , N = 2529 , N = 24
humedad
11.39 (0.79)
10.07 (0.54)
11.91 (0.60)
12.86 (0.86)
6.94 (0.47)
10.67 (0.39) fe_percent
1.72 (0.07)
1.98 (0.09)
2.61 (0.15)
1.89 (0.04)
1.97 (0.04)
2.89 (0.08) k_percent
0.29 (0.02)
0.43 (0.02)
1.06 (0.05)
0.54 (0.03)
0.51 (0.03)
1.18 (0.04) mg_percent
1.10 (0.07)
1.19 (0.08)
1.44 (0.10)
1.74 (0.21)
1.42 (0.12)
1.58 (0.16) na_percent
0.03 (0.00)
0.05 (0.00)
0.08 (0.01)
0.05 (0.00)
0.05 (0.01)
0.09 (0.00) n_percent
0.28 (0.03)
0.19 (0.01)
0.24 (0.02)
0.30 (0.04)
0.20 (0.02)
0.22 (0.02) c_percent
8.73 (0.35)
7.46 (0.38)
7.96 (0.45)
7.27 (0.42)
6.31 (0.36)
6.59 (0.38) c_n
13.59 (0.74)
14.34 (0.58)
8.87 (0.35)
11.88 (0.72)
12.12 (0.38)
7.94 (0.29) cic
15.58 (0.47)
15.56 (0.35)
19.38 (0.30)
15.08 (0.36)
14.44 (0.42)
18.29 (0.50) p
4.91 (0.35)
4.28 (0.47)
3.46 (0.38)
5.50 (0.33)
4.80 (0.80)
2.67 (0.25) mo
6.15 (0.46)
4.68 (0.28)
3.60 (0.24)
5.86 (0.65)
4.24 (0.42)
3.25 (0.32) p_h_k_cl
7.57 (0.03)
7.51 (0.02)
7.37 (0.03)
7.52 (0.03)
7.51 (0.03)
7.44 (0.03) p_h_agua_eez
7.92 (0.03)
7.91 (0.03)
7.73 (0.03)
7.91 (0.02)
7.94 (0.02)
7.85 (0.03)
Anovas table
zona
fecha
zona X fecha
Variables
F
p
F
p
F
p
c_n
2.162
0.192
61.875
0.000
0.936
0.395
cic
1.327
0.293
68.631
0.000
0.468
0.627
c_percent
1.503
0.266
7.566
0.001
0.133
0.876
k_percent
3.556
0.108
477.471
0.000
6.383
0.002
humedad
0.623
0.460
24.553
0.000
9.009
0.000
fe_percent
0.318
0.593
195.205
0.000
3.344
0.038
mg_percent
0.751
0.419
3.138
0.047
4.787
0.010
mo
0.424
0.539
20.545
0.000
0.012
0.989
p
0.023
0.884
12.024
0.000
1.518
0.223
p_h_agua_eez
0.832
0.397
23.112
0.000
4.725
0.010
p_h_k_cl
0.007
0.935
17.353
0.000
3.054
0.050
n_percent
0.036
0.849
12.584
0.002
0.422
0.810
na_percent
3.315
0.069
188.137
0.000
17.576
0.000
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gtsummary_1.4.2 plotrix_3.8-1 kableExtra_1.3.1 car_3.0-10
[5] carData_3.0-4 glmmADMB_0.8.3.3 glmmTMB_1.0.2.1 DHARMa_0.3.3.0
[9] afex_0.28-1 performance_0.8.0 multcomp_1.4-16 TH.data_1.0-10
[13] mvtnorm_1.1-1 emmeans_1.5.4 lmerTest_3.1-3 lme4_1.1-27.1
[17] Matrix_1.3-2 fitdistrplus_1.1-3 survival_3.2-7 MASS_7.3-53
[21] ggpubr_0.4.0 janitor_2.1.0 here_1.0.1 forcats_0.5.1
[25] stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0
[29] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
[33] rmdformats_1.0.1 knitr_1.31 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6
[4] TMB_1.7.19 splines_4.0.2 digest_0.6.27
[7] foreach_1.5.1 htmltools_0.5.2 fansi_0.4.2
[10] magrittr_2.0.1 checkmate_2.0.0 openxlsx_4.2.3
[13] modelr_0.1.8 sandwich_3.0-0 colorspace_2.0-2
[16] rvest_1.0.0 haven_2.3.1 xfun_0.23
[19] callr_3.7.0 crayon_1.4.1 jsonlite_1.7.2
[22] zoo_1.8-8 iterators_1.0.13 glue_1.4.2
[25] gtable_0.3.0 R2admb_0.7.16.2 webshot_0.5.2
[28] abind_1.4-5 scales_1.1.1.9000 DBI_1.1.1
[31] rstatix_0.6.0 Rcpp_1.0.7 viridisLite_0.4.0
[34] xtable_1.8-4 foreign_0.8-81 httr_1.4.2
[37] ellipsis_0.3.2 farver_2.1.0 pkgconfig_2.0.3
[40] sass_0.3.1 dbplyr_2.1.1 utf8_1.1.4
[43] labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.12
[46] reshape2_1.4.4 later_1.1.0.1 munsell_0.5.0
[49] cellranger_1.1.0 tools_4.0.2 cli_2.5.0
[52] generics_0.1.0 broom_0.7.9 evaluate_0.14
[55] fastmap_1.1.0 yaml_2.2.1 processx_3.5.1
[58] fs_1.5.0 zip_2.1.1 nlme_3.1-152
[61] whisker_0.4 formatR_1.8 xml2_1.3.2
[64] compiler_4.0.2 pbkrtest_0.5-0.1 rstudioapi_0.13
[67] curl_4.3 ggsignif_0.6.0 gt_0.3.0
[70] reprex_2.0.0 broom.helpers_1.4.0 bslib_0.2.4
[73] stringi_1.7.4 highr_0.8 ps_1.5.0
[76] lattice_0.20-41 commonmark_1.7 nloptr_1.2.2.2
[79] vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.1
[82] jquerylib_0.1.3 estimability_1.3 data.table_1.14.0
[85] insight_0.14.4 httpuv_1.5.5 R6_2.5.1
[88] bookdown_0.21.6 promises_1.2.0.1 rio_0.5.16
[91] codetools_0.2-18 boot_1.3-26 assertthat_0.2.1
[94] rprojroot_2.0.2 withr_2.4.1 parallel_4.0.2
[97] hms_1.0.0 grid_4.0.2 coda_0.19-4
[100] minqa_1.2.4
[ reached getOption("max.print") -- omitted 6 entries ]
