VOR
VOR.null <- glmer(value ~ 1 + (1|Location/Pasture/year),
data=subset(veg.s.stack, response=="Visual Obstruction Reading"), family = Gamma (link = "identity"))
VOR.mgmt <- glmer(value ~ management + (1|Location/Pasture/year),
data=subset(veg.s.stack, response=="Visual Obstruction Reading"), family = Gamma (link = "identity"))
anova(VOR.null, VOR.mgmt) #global statistic - report test statistic(chi2 value) and p value
## Data: subset(veg.s.stack, response == "Visual Obstruction Reading")
## Models:
## VOR.null: value ~ 1 + (1 | Location/Pasture/year)
## VOR.mgmt: value ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## VOR.null 5 493.64 510.92 -241.82 483.64
## VOR.mgmt 8 467.75 495.40 -225.88 451.75 31.886 3 5.532e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCVOR <- glht(VOR.mgmt, linfct = mcp(management = "Tukey"))
summary(MCVOR) # 2x had signif greater cover than cont; no other mgmt types were signif different from one another
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value ~ management + (1 | Location/Pasture/year),
## data = subset(veg.s.stack, response == "Visual Obstruction Reading"),
## family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 -0.74263 0.41815 -1.776
## recently burned - continuous == 0 -1.28450 0.42233 -3.041
## twice-over - continuous == 0 0.31511 0.50414 0.625
## recently burned - not recently burned == 0 -0.54186 0.09259 -5.852
## twice-over - not recently burned == 0 1.05774 0.46571 2.271
## twice-over - recently burned == 0 1.59960 0.46980 3.405
## Pr(>|z|)
## not recently burned - continuous == 0 0.25350
## recently burned - continuous == 0 0.00979 **
## twice-over - continuous == 0 0.91406
## recently burned - not recently burned == 0 < 0.001 ***
## twice-over - not recently burned == 0 0.08846 .
## twice-over - recently burned == 0 0.00282 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCVOR) # state the signif mgmt types (estimate/regression coeff = 3.14, 95% CI: 0.22-6.1)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value ~ management + (1 | Location/Pasture/year),
## data = subset(veg.s.stack, response == "Visual Obstruction Reading"),
## family = Gamma(link = "identity"))
##
## Quantile = 2.4969
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 -0.7426 -1.7867 0.3014
## recently burned - continuous == 0 -1.2845 -2.3390 -0.2300
## twice-over - continuous == 0 0.3151 -0.9437 1.5739
## recently burned - not recently burned == 0 -0.5419 -0.7730 -0.3107
## twice-over - not recently burned == 0 1.0577 -0.1051 2.2206
## twice-over - recently burned == 0 1.5996 0.4266 2.7726
Litter
ltr.null <- glmer(value ~ 1 + (1|Location/Pasture/year),
data=subset(veg.s.stack, response=="Cumulative Litter Depth"), family = Gamma (link = "identity"))
ltr.mgmt <- glmer(value ~ management + (1|Location/Pasture/year),
data=subset(veg.s.stack, response=="Cumulative Litter Depth"), family = Gamma (link = "identity"))
anova(ltr.null, ltr.mgmt)
## Data: subset(veg.s.stack, response == "Cumulative Litter Depth")
## Models:
## ltr.null: value ~ 1 + (1 | Location/Pasture/year)
## ltr.mgmt: value ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## ltr.null 5 1454.8 1472.1 -722.41 1444.8
## ltr.mgmt 8 1396.9 1424.6 -690.47 1380.9 63.889 3 8.669e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCltr <- glht(ltr.mgmt, linfct = mcp(management = "Tukey"))
summary(MCltr)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value ~ management + (1 | Location/Pasture/year),
## data = subset(veg.s.stack, response == "Cumulative Litter Depth"),
## family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 -3.8178 2.3746 -1.608
## recently burned - continuous == 0 -10.3487 2.3919 -4.327
## twice-over - continuous == 0 -3.0722 3.2275 -0.952
## recently burned - not recently burned == 0 -6.5309 0.6742 -9.687
## twice-over - not recently burned == 0 0.7456 3.0117 0.248
## twice-over - recently burned == 0 7.2765 3.0282 2.403
## Pr(>|z|)
## not recently burned - continuous == 0 0.3411
## recently burned - continuous == 0 <0.001 ***
## twice-over - continuous == 0 0.7538
## recently burned - not recently burned == 0 <0.001 ***
## twice-over - not recently burned == 0 0.9939
## twice-over - recently burned == 0 0.0645 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCltr)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value ~ management + (1 | Location/Pasture/year),
## data = subset(veg.s.stack, response == "Cumulative Litter Depth"),
## family = Gamma(link = "identity"))
##
## Quantile = 2.5004
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 -3.8178 -9.7553 2.1198
## recently burned - continuous == 0 -10.3487 -16.3293 -4.3681
## twice-over - continuous == 0 -3.0722 -11.1422 4.9978
## recently burned - not recently burned == 0 -6.5309 -8.2167 -4.8452
## twice-over - not recently burned == 0 0.7456 -6.7848 8.2760
## twice-over - recently burned == 0 7.2765 -0.2952 14.8482
POPR
POPR.null <- lmer(value ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="Kentucky bluegrass"), REML=FALSE)
POPR.mgmt <- lmer(value ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="Kentucky bluegrass"), REML=FALSE)
anova(POPR.null, POPR.mgmt)
## Data: subset(veg.c.stack, response == "Kentucky bluegrass")
## Models:
## POPR.null: value ~ 1 + (1 | Location/Pasture/year)
## POPR.mgmt: value ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## POPR.null 5 1824.6 1841.9 -907.32 1814.6
## POPR.mgmt 8 1801.5 1829.2 -892.77 1785.5 29.08 3 2.154e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCPOPR <- glht(POPR.mgmt, linfct = mcp(management = "Tukey"))
summary(MCPOPR)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = value ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "Kentucky bluegrass"),
## REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 3.691 4.376 0.843
## recently burned - continuous == 0 -7.760 4.717 -1.645
## twice-over - continuous == 0 10.594 5.378 1.970
## recently burned - not recently burned == 0 -11.451 2.224 -5.149
## twice-over - not recently burned == 0 6.903 4.763 1.449
## twice-over - recently burned == 0 18.353 5.078 3.614
## Pr(>|z|)
## not recently burned - continuous == 0 0.82263
## recently burned - continuous == 0 0.33402
## twice-over - continuous == 0 0.18504
## recently burned - not recently burned == 0 < 0.001 ***
## twice-over - not recently burned == 0 0.44802
## twice-over - recently burned == 0 0.00137 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCPOPR)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = value ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "Kentucky bluegrass"),
## REML = FALSE)
##
## Quantile = 2.5324
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 3.6909 -7.3905 14.7723
## recently burned - continuous == 0 -7.7597 -19.7039 4.1844
## twice-over - continuous == 0 10.5936 -3.0247 24.2120
## recently burned - not recently burned == 0 -11.4506 -17.0825 -5.8188
## twice-over - not recently burned == 0 6.9027 -5.1601 18.9656
## twice-over - recently burned == 0 18.3534 5.4934 31.2133
BRIN
BRIN.null <- glmer(value + 0.3 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="smooth brome"), family = Gamma (link = "identity"))
BRIN.mgmt <- glmer(value + 0.3 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="smooth brome"), family = Gamma (link = "identity"))
anova(BRIN.null, BRIN.mgmt)
## Data: subset(veg.c.stack, response == "smooth brome")
## Models:
## BRIN.null: value + 0.3 ~ 1 + (1 | Location/Pasture/year)
## BRIN.mgmt: value + 0.3 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## BRIN.null 5 1718.4 1735.7 -854.22 1708.4
## BRIN.mgmt 8 1715.5 1743.2 -849.77 1699.5 8.8879 3 0.03082 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCBRIN <- glht(BRIN.mgmt, linfct = mcp(management = "Tukey"))
summary(MCBRIN)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.3 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "smooth brome"), family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 -13.556 4.149 -3.267
## recently burned - continuous == 0 -15.572 4.260 -3.655
## twice-over - continuous == 0 -17.782 4.657 -3.818
## recently burned - not recently burned == 0 -2.016 1.332 -1.513
## twice-over - not recently burned == 0 -4.226 3.671 -1.151
## twice-over - recently burned == 0 -2.210 3.792 -0.583
## Pr(>|z|)
## not recently burned - continuous == 0 0.00504 **
## recently burned - continuous == 0 0.00102 **
## twice-over - continuous == 0 < 0.001 ***
## recently burned - not recently burned == 0 0.40179
## twice-over - not recently burned == 0 0.63303
## twice-over - recently burned == 0 0.93078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCBRIN)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.3 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "smooth brome"), family = Gamma(link = "identity"))
##
## Quantile = 2.5177
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 -13.5561 -24.0023 -3.1099
## recently burned - continuous == 0 -15.5719 -26.2971 -4.8467
## twice-over - continuous == 0 -17.7819 -29.5073 -6.0565
## recently burned - not recently burned == 0 -2.0158 -5.3694 1.3377
## twice-over - not recently burned == 0 -4.2258 -13.4671 5.0156
## twice-over - recently burned == 0 -2.2099 -11.7579 7.3380
NatC3
NatC3.null <- glmer(value + 0.7 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native C3"), family = Gamma (link = "identity"))
NatC3.mgmt <- glmer(value + 0.7 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native C3"), family = Gamma (link = "identity"))
anova(NatC3.null, NatC3.mgmt)
## Data: subset(veg.c.stack, response == "native C3")
## Models:
## NatC3.null: value + 0.7 ~ 1 + (1 | Location/Pasture/year)
## NatC3.mgmt: value + 0.7 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## NatC3.null 5 1393.8 1411.0 -691.88 1383.8
## NatC3.mgmt 8 1376.5 1404.2 -680.27 1360.5 23.211 3 3.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCNatC3 <- glht(NatC3.mgmt, linfct = mcp(management = "Tukey"))
summary(MCNatC3)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.7 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native C3"), family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 8.456505 0.004152 2036.5
## recently burned - continuous == 0 3.345839 0.004152 805.8
## twice-over - continuous == 0 2.017572 0.004152 485.9
## recently burned - not recently burned == 0 -5.110666 0.005872 -870.3
## twice-over - not recently burned == 0 -6.438933 0.005872 -1096.5
## twice-over - recently burned == 0 -1.328267 0.005872 -226.2
## Pr(>|z|)
## not recently burned - continuous == 0 <2e-16 ***
## recently burned - continuous == 0 <2e-16 ***
## twice-over - continuous == 0 <2e-16 ***
## recently burned - not recently burned == 0 <2e-16 ***
## twice-over - not recently burned == 0 <2e-16 ***
## twice-over - recently burned == 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCNatC3)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.7 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native C3"), family = Gamma(link = "identity"))
##
## Quantile = 2.5354
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 8.4565 8.4460 8.4670
## recently burned - continuous == 0 3.3458 3.3353 3.3564
## twice-over - continuous == 0 2.0176 2.0070 2.0281
## recently burned - not recently burned == 0 -5.1107 -5.1256 -5.0958
## twice-over - not recently burned == 0 -6.4389 -6.4538 -6.4240
## twice-over - recently burned == 0 -1.3283 -1.3432 -1.3134
NatC4
NatC4.null <- glmer(value + 0.7 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native C4"), family = Gamma (link = "identity"))
NatC4.mgmt <- glmer(value + 0.7 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native C4"), family = Gamma (link = "identity"))
anova(NatC4.null, NatC4.mgmt)
## Data: subset(veg.c.stack, response == "native C4")
## Models:
## NatC4.null: value + 0.7 ~ 1 + (1 | Location/Pasture/year)
## NatC4.mgmt: value + 0.7 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## NatC4.null 5 944.11 961.39 -467.06 934.11
## NatC4.mgmt 8 947.33 974.97 -465.66 931.33 2.7881 3 0.4255
MCNatC4 <- glht(NatC4.mgmt, linfct = mcp(management = "Tukey"))
summary(MCNatC4)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.7 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native C4"), family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 1.28916 0.69359 1.859
## recently burned - continuous == 0 1.27785 0.77578 1.647
## twice-over - continuous == 0 1.13084 0.99014 1.142
## recently burned - not recently burned == 0 -0.01131 0.45012 -0.025
## twice-over - not recently burned == 0 -0.15832 0.92466 -0.171
## twice-over - recently burned == 0 -0.14701 0.98799 -0.149
## Pr(>|z|)
## not recently burned - continuous == 0 0.231
## recently burned - continuous == 0 0.334
## twice-over - continuous == 0 0.647
## recently burned - not recently burned == 0 1.000
## twice-over - not recently burned == 0 0.998
## twice-over - recently burned == 0 0.999
## (Adjusted p values reported -- single-step method)
confint(MCNatC4)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.7 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native C4"), family = Gamma(link = "identity"))
##
## Quantile = 2.5353
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 1.28916 -0.46929 3.04762
## recently burned - continuous == 0 1.27785 -0.68900 3.24470
## twice-over - continuous == 0 1.13084 -1.37946 3.64114
## recently burned - not recently burned == 0 -0.01131 -1.15251 1.12989
## twice-over - not recently burned == 0 -0.15832 -2.50261 2.18598
## twice-over - recently burned == 0 -0.14701 -2.65186 2.35785
NatLeg
NatLeg.null <- glmer(value + 0.5 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native legume"), family = Gamma (link = "identity"))
NatLeg.mgmt <- glmer(value + 0.5 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native legume"), family = Gamma (link = "identity"))
anova(NatLeg.null, NatLeg.mgmt)
## Data: subset(veg.c.stack, response == "native legume")
## Models:
## NatLeg.null: value + 0.5 ~ 1 + (1 | Location/Pasture/year)
## NatLeg.mgmt: value + 0.5 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## NatLeg.null 5 1055.5 1072.8 -522.77 1045.5
## NatLeg.mgmt 8 1050.1 1077.8 -517.06 1034.1 11.403 3 0.009734 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCNatLeg <- glht(NatLeg.mgmt, linfct = mcp(management = "Tukey"))
summary(MCNatLeg)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native legume"),
## family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 3.2920 0.8640 3.810
## recently burned - continuous == 0 3.4401 1.0077 3.414
## twice-over - continuous == 0 6.7340 1.4156 4.757
## recently burned - not recently burned == 0 0.1481 0.6882 0.215
## twice-over - not recently burned == 0 3.4421 1.3500 2.550
## twice-over - recently burned == 0 3.2940 1.4391 2.289
## Pr(>|z|)
## not recently burned - continuous == 0 < 0.001 ***
## recently burned - continuous == 0 0.00304 **
## twice-over - continuous == 0 < 0.001 ***
## recently burned - not recently burned == 0 0.99623
## twice-over - not recently burned == 0 0.04839 *
## twice-over - recently burned == 0 0.09346 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCNatLeg)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native legume"),
## family = Gamma(link = "identity"))
##
## Quantile = 2.5357
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 3.29196 1.10110 5.48283
## recently burned - continuous == 0 3.44006 0.88493 5.99518
## twice-over - continuous == 0 6.73404 3.14450 10.32359
## recently burned - not recently burned == 0 0.14809 -1.59708 1.89326
## twice-over - not recently burned == 0 3.44208 0.01888 6.86528
## twice-over - recently burned == 0 3.29399 -0.35504 6.94301
IntLeg
IntLeg.null <- glmer(value + 0.5 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="introduced legume"), family = Gamma (link = "identity"))
IntLeg.mgmt <- glmer(value + 0.5 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="introduced legume"), family = Gamma (link = "identity"))
anova(IntLeg.null, IntLeg.mgmt)
## Data: subset(veg.c.stack, response == "introduced legume")
## Models:
## IntLeg.null: value + 0.5 ~ 1 + (1 | Location/Pasture/year)
## IntLeg.mgmt: value + 0.5 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## IntLeg.null 5 901.52 918.80 -445.76 891.52
## IntLeg.mgmt 8 898.20 925.85 -441.10 882.20 9.3153 3 0.02538 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCIntLeg <- glht(IntLeg.mgmt, linfct = mcp(management = "Tukey"))
summary(MCIntLeg)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "introduced legume"),
## family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 2.050180 0.002781 737.26
## recently burned - continuous == 0 1.762071 0.002781 633.63
## twice-over - continuous == 0 -0.452036 0.002872 -157.39
## recently burned - not recently burned == 0 -0.288108 0.003933 -73.26
## twice-over - not recently burned == 0 -2.502216 0.003998 -625.91
## twice-over - recently burned == 0 -2.214108 0.003998 -553.83
## Pr(>|z|)
## not recently burned - continuous == 0 <2e-16 ***
## recently burned - continuous == 0 <2e-16 ***
## twice-over - continuous == 0 <2e-16 ***
## recently burned - not recently burned == 0 <2e-16 ***
## twice-over - not recently burned == 0 <2e-16 ***
## twice-over - recently burned == 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCIntLeg)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "introduced legume"),
## family = Gamma(link = "identity"))
##
## Quantile = 2.5356
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 2.0502 2.0431 2.0572
## recently burned - continuous == 0 1.7621 1.7550 1.7691
## twice-over - continuous == 0 -0.4520 -0.4593 -0.4448
## recently burned - not recently burned == 0 -0.2881 -0.2981 -0.2781
## twice-over - not recently burned == 0 -2.5022 -2.5124 -2.4921
## twice-over - recently burned == 0 -2.2141 -2.2242 -2.2040
NatFrb
NatFrb.null <- lmer(value + 0.5 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native forb"), REML=FALSE)
NatFrb.mgmt <- lmer(value + 0.5 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native forb"), REML=FALSE)
anova(NatFrb.null, NatFrb.mgmt)
## Data: subset(veg.c.stack, response == "native forb")
## Models:
## NatFrb.null: value + 0.5 ~ 1 + (1 | Location/Pasture/year)
## NatFrb.mgmt: value + 0.5 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## NatFrb.null 5 1524.2 1541.5 -757.12 1514.2
## NatFrb.mgmt 8 1521.5 1549.2 -752.77 1505.5 8.7039 3 0.0335 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCNatFrb <- glht(NatFrb.mgmt, linfct = mcp(management = "Tukey"))
summary(MCNatFrb)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native forb"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 11.721 2.409 4.865
## recently burned - continuous == 0 11.904 2.586 4.604
## twice-over - continuous == 0 14.369 3.296 4.360
## recently burned - not recently burned == 0 0.183 1.186 0.154
## twice-over - not recently burned == 0 2.648 2.995 0.884
## twice-over - recently burned == 0 2.466 3.138 0.786
## Pr(>|z|)
## not recently burned - continuous == 0 <1e-04 ***
## recently burned - continuous == 0 <1e-04 ***
## twice-over - continuous == 0 <1e-04 ***
## recently burned - not recently burned == 0 0.999
## twice-over - not recently burned == 0 0.799
## twice-over - recently burned == 0 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCNatFrb)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native forb"), REML = FALSE)
##
## Quantile = 2.5276
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 11.7209 5.6311 17.8108
## recently burned - continuous == 0 11.9039 5.3686 18.4392
## twice-over - continuous == 0 14.3694 6.0397 22.6992
## recently burned - not recently burned == 0 0.1830 -2.8137 3.1798
## twice-over - not recently burned == 0 2.6485 -4.9214 10.2185
## twice-over - recently burned == 0 2.4655 -5.4672 10.3982
IntFrb
IntFrb.null <- glmer(value + 0.5 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="introduced forb"), family = Gamma (link = "identity"))
IntFrb.mgmt <- glmer(value + 0.5 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="introduced forb"), family = Gamma (link = "identity"))
anova(IntFrb.null, IntFrb.mgmt) #global statistic - report test statistic(chi2 value) and p value
## Data: subset(veg.c.stack, response == "introduced forb")
## Models:
## IntFrb.null: value + 0.5 ~ 1 + (1 | Location/Pasture/year)
## IntFrb.mgmt: value + 0.5 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## IntFrb.null 5 934.90 952.18 -462.45 924.90
## IntFrb.mgmt 8 934.62 962.27 -459.31 918.62 6.2798 3 0.09876 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCIntFrb <- glht(IntFrb.mgmt, linfct = mcp(management = "Tukey"))
summary(MCIntFrb) # 2x had signif greater cover than cont; no other mgmt types were signif different from one another
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "introduced forb"),
## family = Gamma(link = "identity"))
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 1.3194 0.8672 1.522
## recently burned - continuous == 0 1.9276 0.9742 1.979
## twice-over - continuous == 0 3.1436 1.1551 2.722
## recently burned - not recently burned == 0 0.6081 0.5202 1.169
## twice-over - not recently burned == 0 1.8241 1.0547 1.729
## twice-over - recently burned == 0 1.2160 1.1420 1.065
## Pr(>|z|)
## not recently burned - continuous == 0 0.4049
## recently burned - continuous == 0 0.1823
## twice-over - continuous == 0 0.0294 *
## recently burned - not recently burned == 0 0.6287
## twice-over - not recently burned == 0 0.2906
## twice-over - recently burned == 0 0.6952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCIntFrb) # state the signif mgmt types (estimate/regression coeff = 3.14, 95% CI: 0.22-6.1)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "introduced forb"),
## family = Gamma(link = "identity"))
##
## Quantile = 2.5353
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 1.3194 -0.8791 3.5180
## recently burned - continuous == 0 1.9276 -0.5424 4.3975
## twice-over - continuous == 0 3.1436 0.2150 6.0721
## recently burned - not recently burned == 0 0.6081 -0.7107 1.9270
## twice-over - not recently burned == 0 1.8241 -0.8500 4.4982
## twice-over - recently burned == 0 1.2160 -1.6795 4.1115
NatWdy
NatWdy.null <- lmer(value + 0.5 ~ 1 + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native woody"), REML=FALSE)
NatWdy.mgmt <- lmer(value + 0.5 ~ management + (1|Location/Pasture/year),
data=subset(veg.c.stack, response=="native woody"), REML=FALSE)
anova(NatWdy.null, NatWdy.mgmt) #global statistic - report test statistic(chi2 value) and p value
## Data: subset(veg.c.stack, response == "native woody")
## Models:
## NatWdy.null: value + 0.5 ~ 1 + (1 | Location/Pasture/year)
## NatWdy.mgmt: value + 0.5 ~ management + (1 | Location/Pasture/year)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## NatWdy.null 5 1362.0 1379.3 -676.01 1352.0
## NatWdy.mgmt 8 1359.7 1387.3 -671.85 1343.7 8.3144 3 0.03994 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MCNatWdy <- glht(NatWdy.mgmt, linfct = mcp(management = "Tukey"))
summary(MCNatWdy) # 2x had signif greater cover than cont; no other mgmt types were signif different from one another
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native woody"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value
## not recently burned - continuous == 0 0.008006 1.390542 0.006
## recently burned - continuous == 0 0.507787 1.560801 0.325
## twice-over - continuous == 0 5.680564 1.683809 3.374
## recently burned - not recently burned == 0 0.499781 0.895684 0.558
## twice-over - not recently burned == 0 5.672558 1.488336 3.811
## twice-over - recently burned == 0 5.172777 1.648527 3.138
## Pr(>|z|)
## not recently burned - continuous == 0 1.00000
## recently burned - continuous == 0 0.98737
## twice-over - continuous == 0 0.00411 **
## recently burned - not recently burned == 0 0.94121
## twice-over - not recently burned == 0 < 0.001 ***
## twice-over - recently burned == 0 0.00861 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
confint(MCNatWdy) # state the signif mgmt types (estimate/regression coeff = 3.14, 95% CI: 0.22-6.1)
##
## Simultaneous Confidence Intervals
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = value + 0.5 ~ management + (1 | Location/Pasture/year),
## data = subset(veg.c.stack, response == "native woody"), REML = FALSE)
##
## Quantile = 2.5412
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## not recently burned - continuous == 0 0.008006 -3.525668 3.541680
## recently burned - continuous == 0 0.507787 -3.458555 4.474128
## twice-over - continuous == 0 5.680564 1.401633 9.959495
## recently burned - not recently burned == 0 0.499781 -1.776351 2.775913
## twice-over - not recently burned == 0 5.672558 1.890366 9.454751
## twice-over - recently burned == 0 5.172777 0.983506 9.362049