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Introduction

Read data
Rename the levels of zona:
- Pyric herbivorism (HP, from spanish Herbivorismo pírico): old Quemado con pastoreo
- Prescribed Fires (QP, from spanish Quemas Prescritas): old Quemado sin pastoreo

Modelo Efectos de la quema

Cobertura vegetal

\[Cobertura \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos Quema, variable = Cobertura
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	71	70.868	0.189	0.664
meses	5	85906	17181.183	45.938	0.000
treat:meses	5	965	192.905	0.516	0.764
Residuals	372	139132	374.012	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

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

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

OK: No outliers detected.

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos Quema, variable = Cobertura
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	5 - (-1)	-41.1094	4.8348	372	-8.5027	0.0000
HP	.	12 - 5	-0.4359	4.8348	372	-0.0902	1.0000
HP	.	17 - 12	11.9578	4.8348	372	2.4733	0.1998
HP	.	22 - 17	-1.1313	4.8348	372	-0.2340	1.0000
HP	.	29 - 22	6.4609	4.8348	372	1.3363	0.9600
QP	.	5 - (-1)	-49.1686	4.8348	372	-10.1696	0.0000
QP	.	12 - 5	2.1648	4.8348	372	0.4478	1.0000
QP	.	17 - 12	17.1006	4.8348	372	3.5370	0.0073
QP	.	22 - 17	-0.8781	4.8348	372	-0.1816	1.0000
QP	.	29 - 22	1.5391	4.8348	372	0.3183	1.0000
.	-1	QP - HP	2.2875	4.8348	372	0.4731	1.0000
.	5	QP - HP	-5.7717	4.8348	372	-1.1938	0.9858
.	12	QP - HP	-3.1709	4.8348	372	-0.6559	1.0000
.	17	QP - HP	1.9719	4.8348	372	0.4078	1.0000
.	22	QP - HP	2.2250	4.8348	372	0.4602	1.0000
.	29	QP - HP	-2.6969	4.8348	372	-0.5578	1.0000

Fitovolumen

\[Fitovolumen \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos Quema, variable = Fitovolumen
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	409567	409566.7	0.240	0.624
meses	5	484280716	96856143.2	56.803	0.000
treat:meses	5	547672	109534.4	0.064	0.997
Residuals	372	634309317	1705132.6	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p = 0.006).

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

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

OK: No outliers detected.

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos
No problemas con outlier

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc ANOVA Efectos Quema, variable = Fitovolumen
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	5 - (-1)	-3153.3055	326.4518	372	-9.6593	0.0000
HP	.	12 - 5	69.5070	326.4518	372	0.2129	1.0000
HP	.	17 - 12	269.1797	326.4518	372	0.8246	0.9998
HP	.	22 - 17	11.9375	326.4518	372	0.0366	1.0000
HP	.	29 - 22	180.9500	326.4518	372	0.5543	1.0000
QP	.	5 - (-1)	-3325.0494	326.4518	372	-10.1854	0.0000
QP	.	12 - 5	72.1835	326.4518	372	0.2211	1.0000
QP	.	17 - 12	262.1369	326.4518	372	0.8030	0.9998
QP	.	22 - 17	125.5094	326.4518	372	0.3845	1.0000
QP	.	29 - 22	27.3250	326.4518	372	0.0837	1.0000
.	-1	QP - HP	67.2869	326.4518	372	0.2061	1.0000
.	5	QP - HP	-104.4571	326.4518	372	-0.3200	1.0000
.	12	QP - HP	-101.7806	326.4518	372	-0.3118	1.0000
.	17	QP - HP	-108.8234	326.4518	372	-0.3334	1.0000
.	22	QP - HP	4.7484	326.4518	372	0.0145	1.0000
.	29	QP - HP	-148.8766	326.4518	372	-0.4560	1.0000

Riqueza

\[Riqueza \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos Quema, variable = Riqueza
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	502	501.878	52.899	0.000
meses	5	1822	364.467	38.416	0.000
treat:meses	5	69	13.846	1.459	0.202
Residuals	372	3529	9.487	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

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

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         53%
      tweedie         47%

Predicted Distribution of Response

               Distribution Probability
              beta-binomial         59%
          negative binomial         12%
 neg. binomial (zero-infl.)          9%

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos

Parace también que existe un problema con la distribución de probabilidad de los residuos (habría que especificar otro tipo de distribución: e.g. beta-binomial)

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos Quema, variable = Riqueza
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	5 - (-1)	1.4062	0.77	372	1.8262	0.6794
HP	.	12 - 5	-0.7188	0.77	372	-0.9334	0.9990
HP	.	17 - 12	6.2813	0.77	372	8.1570	0.0000
HP	.	22 - 17	-5.4062	0.77	372	-7.0207	0.0000
HP	.	29 - 22	2.6875	0.77	372	3.4901	0.0086
QP	.	5 - (-1)	0.8750	0.77	372	1.1363	0.9913
QP	.	12 - 5	0.6875	0.77	372	0.8928	0.9994
QP	.	17 - 12	4.5938	0.77	372	5.9656	0.0000
QP	.	22 - 17	-5.4375	0.77	372	-7.0613	0.0000
QP	.	29 - 22	1.6250	0.77	372	2.1103	0.4392
.	-1	QP - HP	-1.7500	0.77	372	-2.2726	0.3178
.	5	QP - HP	-2.2813	0.77	372	-2.9625	0.0507
.	12	QP - HP	-0.8750	0.77	372	-1.1363	0.9913
.	17	QP - HP	-2.5625	0.77	372	-3.3277	0.0153
.	22	QP - HP	-2.5938	0.77	372	-3.3683	0.0133
.	29	QP - HP	-3.6563	0.77	372	-4.7481	0.0000

Diversidad

\[Shannon \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos Quema, variable = Diversidad
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	1	1.296	5.023	0.026
meses	5	19	3.713	14.397	0.000
treat:meses	5	1	0.183	0.709	0.617
Residuals	372	96	0.258	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

OK: There is not clear evidence for different variances across groups (Bartlett Test, p = 0.939).

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         62%
      tweedie         16%
         beta         12%

Predicted Distribution of Response

 Distribution Probability
      tweedie         47%
      weibull         25%
         beta         12%

No, se detectan varios problemas:

Problemas de linealirad,
Correlaciones (problamente temporales) entre datos

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos Quema, variable = Diversidad
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	5 - (-1)	0.5124	0.127	372	4.0359	0.0011
HP	.	12 - 5	-0.1828	0.127	372	-1.4397	0.9268
HP	.	17 - 12	0.4215	0.127	372	3.3201	0.0157
HP	.	22 - 17	-0.3269	0.127	372	-2.5744	0.1544
HP	.	29 - 22	0.0749	0.127	372	0.5896	1.0000
QP	.	5 - (-1)	0.2398	0.127	372	1.8889	0.6264
QP	.	12 - 5	0.0446	0.127	372	0.3517	1.0000
QP	.	17 - 12	0.4674	0.127	372	3.6817	0.0042
QP	.	22 - 17	-0.4754	0.127	372	-3.7444	0.0033
QP	.	29 - 22	0.0783	0.127	372	0.6166	1.0000
.	-1	QP - HP	-0.0147	0.127	372	-0.1155	1.0000
.	5	QP - HP	-0.2872	0.127	372	-2.2624	0.3248
.	12	QP - HP	-0.0598	0.127	372	-0.4710	1.0000
.	17	QP - HP	-0.0139	0.127	372	-0.1094	1.0000
.	22	QP - HP	-0.1624	0.127	372	-1.2794	0.9727
.	29	QP - HP	-0.1590	0.127	372	-1.2523	0.9776

Modelo Efectos tras la quema

Quitamos el momento inicial

Cobertura vegetal

\[Cobertura \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos tras la Quema, variable = Cobertura
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	177	177.257	0.772	0.380
meses	4	19328	4832.032	21.049	0.000
treat:meses	4	774	193.603	0.843	0.499
Residuals	310	71163	229.556	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

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

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         53%
      tweedie         47%

Predicted Distribution of Response

 Distribution Probability
      tweedie         50%
        gamma         38%
      weibull          9%

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos
Problemas de distribución de los residuos

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos tras la Quema, variable = Cobertura
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	12 - 5	-0.4359	3.7878	310	-0.1151	1.0000
HP	.	17 - 12	11.9578	3.7878	310	3.1569	0.0225
HP	.	22 - 17	-1.1313	3.7878	310	-0.2987	1.0000
HP	.	29 - 22	6.4609	3.7878	310	1.7057	0.7026
QP	.	12 - 5	2.1648	3.7878	310	0.5715	1.0000
QP	.	17 - 12	17.1006	3.7878	310	4.5147	0.0001
QP	.	22 - 17	-0.8781	3.7878	310	-0.2318	1.0000
QP	.	29 - 22	1.5391	3.7878	310	0.4063	1.0000
.	5	QP - HP	-5.7717	3.7878	310	-1.5238	0.8329
.	12	QP - HP	-3.1709	3.7878	310	-0.8371	0.9988
.	17	QP - HP	1.9719	3.7878	310	0.5206	1.0000
.	22	QP - HP	2.2250	3.7878	310	0.5874	1.0000
.	29	QP - HP	-2.6969	3.7878	310	-0.7120	0.9998

Fitovolumen

\[Fitovolumen \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos tras la Quema, variable = Fitovolumen
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	674735	674735.35	1.639	0.201
meses	4	12314211	3078552.87	7.478	0.000
treat:meses	4	210063	52515.76	0.128	0.972
Residuals	310	127618607	411672.92	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

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

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         41%
    lognormal         19%
            F         12%

Predicted Distribution of Response

               Distribution Probability
                  lognormal         72%
                          F         12%
 neg. binomial (zero-infl.)          6%

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos
No problemas con outlier

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos tras la Quema, variable = Fitovolumen
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	12 - 5	69.5070	160.4044	310	0.4333	1.0000
HP	.	17 - 12	269.1797	160.4044	310	1.6781	0.7242
HP	.	22 - 17	11.9375	160.4044	310	0.0744	1.0000
HP	.	29 - 22	180.9500	160.4044	310	1.1281	0.9801
QP	.	12 - 5	72.1835	160.4044	310	0.4500	1.0000
QP	.	17 - 12	262.1369	160.4044	310	1.6342	0.7574
QP	.	22 - 17	125.5094	160.4044	310	0.7825	0.9994
QP	.	29 - 22	27.3250	160.4044	310	0.1704	1.0000
.	5	QP - HP	-104.4571	160.4044	310	-0.6512	0.9999
.	12	QP - HP	-101.7806	160.4044	310	-0.6345	0.9999
.	17	QP - HP	-108.8234	160.4044	310	-0.6784	0.9999
.	22	QP - HP	4.7484	160.4044	310	0.0296	1.0000
.	29	QP - HP	-148.8766	160.4044	310	-0.9281	0.9966

Riqueza

\[Riqueza \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos tras la Quema, variable = Riqueza
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	458	458.403	45.980	0.000
meses	4	1447	361.730	36.283	0.000
treat:meses	4	64	15.927	1.598	0.175
Residuals	310	3091	9.970	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

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

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

               Distribution Probability
                     normal         53%
                    tweedie         44%
 neg. binomial (zero-infl.)          3%

Predicted Distribution of Response

         Distribution Probability
        beta-binomial         69%
    negative binomial          9%
 poisson (zero-infl.)          9%

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos

Parace también que existe un problema con la distribución de probabilidad de los residuos (habría que especificar otro tipo de distribución: e.g. beta-binomial)

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

ANOVA Efectos tras la Quema, variable = Riqueza
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	12 - 5	-0.7188	0.7894	310	-0.9105	0.9972
HP	.	17 - 12	6.2812	0.7894	310	7.9573	0.0000
HP	.	22 - 17	-5.4062	0.7894	310	-6.8488	0.0000
HP	.	29 - 22	2.6875	0.7894	310	3.4046	0.0097
QP	.	12 - 5	0.6875	0.7894	310	0.8709	0.9982
QP	.	17 - 12	4.5937	0.7894	310	5.8195	0.0000
QP	.	22 - 17	-5.4375	0.7894	310	-6.8884	0.0000
QP	.	29 - 22	1.6250	0.7894	310	2.0586	0.4147
.	5	QP - HP	-2.2812	0.7894	310	-2.8900	0.0523
.	12	QP - HP	-0.8750	0.7894	310	-1.1085	0.9828
.	17	QP - HP	-2.5625	0.7894	310	-3.2463	0.0167
.	22	QP - HP	-2.5937	0.7894	310	-3.2859	0.0146
.	29	QP - HP	-3.6562	0.7894	310	-4.6319	0.0001

Diversidad

\[Shannon \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos tras la Quema, variable = Diversidad
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	1	1.490	5.840	0.016
meses	4	8	2.032	7.963	0.000
treat:meses	4	1	0.179	0.703	0.591
Residuals	310	79	0.255	NA	NA

Visualizando el modelo

Version	Author	Date
8d3328b	ajpelu	2022-02-03

¿Es el modelo válido?

Version	Author	Date
8d3328b	ajpelu	2022-02-03

Warning: Autocorrelated residuals detected (p < .001).

OK: There is not clear evidence for different variances across groups (Bartlett Test, p = 0.863).

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

         Distribution Probability
               normal         94%
            bernoulli          3%
 poisson (zero-infl.)          3%

Predicted Distribution of Response

 Distribution Probability
      weibull         84%
       normal          9%
         beta          3%

No, se detectan varios problemas:

Problemas de linealirad,
Correlaciones (problamente temporales) entre datos

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos tras la Quema, variable = Diversidad
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	12 - 5	-0.1828	0.1263	310	-1.4475	0.8768
HP	.	17 - 12	0.4215	0.1263	310	3.3381	0.0122
HP	.	22 - 17	-0.3269	0.1263	310	-2.5884	0.1236
HP	.	29 - 22	0.0749	0.1263	310	0.5928	1.0000
QP	.	12 - 5	0.0446	0.1263	310	0.3536	1.0000
QP	.	17 - 12	0.4674	0.1263	310	3.7017	0.0033
QP	.	22 - 17	-0.4754	0.1263	310	-3.7647	0.0026
QP	.	29 - 22	0.0783	0.1263	310	0.6199	1.0000
.	5	QP - HP	-0.2872	0.1263	310	-2.2747	0.2670
.	12	QP - HP	-0.0598	0.1263	310	-0.4736	1.0000
.	17	QP - HP	-0.0139	0.1263	310	-0.1100	1.0000
.	22	QP - HP	-0.1624	0.1263	310	-1.2863	0.9444
.	29	QP - HP	-0.1590	0.1263	310	-1.2591	0.9525

Tasa de consumo

\[Consumo \sim treat + meses + treat \times meses\]

Tabla ANOVA

ANOVA Efectos tras la Quema, variable = Tasa Consumo
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
treat	1	6	5.980	35.126	0
meses	4	30	7.557	44.389	0
treat:meses	4	4	1.063	6.242	0
Residuals	310	53	0.170	NA	NA

Visualizando el modelo

Version	Author	Date
0c68dda	ajpelu	2022-02-04

¿Es el modelo válido?

Version	Author	Date
0c68dda	ajpelu	2022-02-04

Warning: Autocorrelated residuals detected (p = 0.002).

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

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

OK: No outliers detected.

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         53%
      tweedie         34%
         beta          9%

Predicted Distribution of Response

         Distribution Probability
              tweedie         88%
                 beta          6%
 poisson (zero-infl.)          6%

No, se detectan varios problemas:

No homogeneidad varianza,
Problemas de linealirad,
Correlaciones (problamente temporales) entre datos

Por tanto, las conclusiones que se obtuvieran de este modelado no son válidas!!. Alternativas: Modelos GLMMs, Modelos GAMMs, … ver Zuur et al. (2013)

Tabla posthoc

Post-hoc Efectos tras la Quema, variable = Tasa consumo
treat	meses	contrast	estimate	SE	df	t.ratio	p.value
HP	.	12 - 5	1.2113	0.1032	310	11.7427	0.0000
HP	.	17 - 12	-0.7063	0.1032	310	-6.8469	0.0000
HP	.	22 - 17	-0.2596	0.1032	310	-2.5169	0.1491
HP	.	29 - 22	-0.0007	0.1032	310	-0.0069	1.0000
QP	.	12 - 5	0.5609	0.1032	310	5.4377	0.0000
QP	.	17 - 12	-0.4509	0.1032	310	-4.3713	0.0002
QP	.	22 - 17	0.0404	0.1032	310	0.3912	1.0000
QP	.	29 - 22	-0.1323	0.1032	310	-1.2822	0.9457
.	5	QP - HP	0.0000	0.1032	310	0.0000	1.0000
.	12	QP - HP	-0.6504	0.1032	310	-6.3050	0.0000
.	17	QP - HP	-0.3950	0.1032	310	-3.8294	0.0020
.	22	QP - HP	-0.0950	0.1032	310	-0.9214	0.9968
.	29	QP - HP	-0.2266	0.1032	310	-2.1967	0.3159

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] emmeans_1.5.4     plotrix_3.8-1     gtsummary_1.4.2   patchwork_1.1.1  
 [5] performance_0.8.0 broom_0.7.9       tidymv_3.2.1      ggpubr_0.4.0     
 [9] kableExtra_1.3.1  janitor_2.1.0     here_1.0.1        forcats_0.5.1    
[13] stringr_1.4.0     dplyr_1.0.6       purrr_0.3.4       readr_1.4.0      
[17] tidyr_1.1.3       tibble_3.1.2      ggplot2_3.3.5     tidyverse_1.3.1  
[21] workflowr_1.7.0  

loaded via a namespace (and not attached):
  [1] TH.data_1.0-10      colorspace_2.0-2    ggsignif_0.6.0     
  [4] ggridges_0.5.3      ellipsis_0.3.2      rio_0.5.16         
  [7] rprojroot_2.0.2     estimability_1.3    snakecase_0.11.0   
 [10] parameters_0.14.0   fs_1.5.0            rstudioapi_0.13    
 [13] farver_2.1.0        ggrepel_0.9.1       fansi_0.4.2        
 [16] mvtnorm_1.1-1       lubridate_1.7.10    xml2_1.3.2         
 [19] codetools_0.2-18    splines_4.0.2       robustbase_0.93-7  
 [22] knitr_1.31          jsonlite_1.7.2      gt_0.3.0           
 [25] dbplyr_2.1.1        effectsize_0.4.5    compiler_4.0.2     
 [28] httr_1.4.2          backports_1.2.1     assertthat_0.2.1   
 [31] Matrix_1.3-2        fastmap_1.1.0       cli_2.5.0          
 [34] later_1.1.0.1       htmltools_0.5.2     tools_4.0.2        
 [37] coda_0.19-4         gtable_0.3.0        glue_1.4.2         
 [40] Rcpp_1.0.7          carData_3.0-4       cellranger_1.1.0   
 [43] jquerylib_0.1.3     vctrs_0.3.8         nlme_3.1-152       
 [46] broom.helpers_1.4.0 insight_0.14.4      xfun_0.23          
 [49] ps_1.5.0            openxlsx_4.2.3      rvest_1.0.0        
 [52] lifecycle_1.0.1     rstatix_0.6.0       DEoptimR_1.0-8     
 [55] MASS_7.3-53         zoo_1.8-8           getPass_0.2-2      
 [58] scales_1.1.1.9000   hms_1.0.0           promises_1.2.0.1   
 [61] sandwich_3.0-0      qqplotr_0.0.5       yaml_2.2.1         
 [64] curl_4.3            gridExtra_2.3       see_0.6.4          
 [67] sass_0.3.1          stringi_1.7.4       bayestestR_0.9.0   
 [70] highr_0.8           randomForest_4.6-14 zip_2.1.1          
 [73] rlang_0.4.12        pkgconfig_2.0.3     evaluate_0.14      
 [76] lattice_0.20-41     labeling_0.4.2      processx_3.5.1     
 [79] tidyselect_1.1.1    ggsci_2.9           plyr_1.8.6         
 [82] magrittr_2.0.1      R6_2.5.1            generics_0.1.0     
 [85] multcomp_1.4-16     DBI_1.1.1           pillar_1.6.1       
 [88] haven_2.3.1         whisker_0.4         foreign_0.8-81     
 [91] withr_2.4.1         mgcv_1.8-33         survival_3.2-7     
 [94] abind_1.4-5         modelr_0.1.8        crayon_1.4.1       
 [97] car_3.0-10          utf8_1.1.4          rmarkdown_2.8      
[100] grid_4.0.2          readxl_1.3.1        data.table_1.14.0  
[103] callr_3.7.0         git2r_0.28.0        reprex_2.0.0       
[106] digest_0.6.27       webshot_0.5.2       xtable_1.8-4       
[109] httpuv_1.5.5        munsell_0.5.0       viridisLite_0.4.0  
[112] bslib_0.2.4

Modelos anovas informe final O2P

ajpelu

2022-02-03

Introduction

Modelo Efectos de la quema

Cobertura vegetal

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Fitovolumen

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Riqueza

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Diversidad

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Modelo Efectos tras la quema

Cobertura vegetal

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Fitovolumen

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Riqueza

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Diversidad

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc

Tasa de consumo

Tabla ANOVA

Visualizando el modelo

¿Es el modelo válido?

Tabla posthoc