#Load in libraries
library(readr)
library(lme4)
## Loading required package: Matrix
library(car)
## Loading required package: carData
## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4
library(emmeans)
#Spring 2018 model
setwd("c:/users/Paul/Documents/Rwork")
springonedata<- read.csv(file="springonedata.csv")
str(springonedata)
## 'data.frame': 20 obs. of 12 variables:
## $ Season : int 64 64 64 64 64 64 64 64 64 64 ...
## $ Time : chr "S1" "S1" "S1" "S1" ...
## $ Site : chr "Kramer" "Rhinehart" "Bachelor" "Western" ...
## $ Deer : chr "Con" "Con" "Con" "Con" ...
## $ Honeysuckle : chr "H " "H " "H " "H " ...
## $ Treatment : chr "ConH" "ConH" "ConH" "ConH" ...
## $ min_OM : num 29.9 22.9 36.6 27.7 66.1 ...
## $ NetNitr_OM : num 32 23.6 38.4 29.2 47.4 ...
## $ Nmin : num 1.24 0.85 0.96 0.88 1.57 1.86 1.16 1.02 2.79 1.45 ...
## $ NetNitr : num 1.32 1.16 0.99 0.98 1.64 1.63 1.22 0.7 2 1.37 ...
## $ NitrateN : num 22.5 19.7 16.9 16.7 27.9 ...
## $ Soilmicrobialresp: num 39 25.9 43.5 41 34.2 ...
mean(springonedata)
## Warning in mean.default(springonedata): argument is not numeric or logical:
## returning NA
## [1] NA
modela <- lm(NetNitr_OM ~ Treatment,data = springonedata)
modelab <- lm(min_OM ~ Treatment,data = springonedata)
summary(modela)
##
## Call:
## lm(formula = NetNitr_OM ~ Treatment, data = springonedata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.518 -4.061 -0.484 4.096 13.272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.118 3.157 10.808 9.22e-09 ***
## TreatmentConNH -5.732 4.464 -1.284 0.217
## TreatmentExcH -7.704 4.464 -1.726 0.104
## TreatmentExcNH -10.416 4.464 -2.333 0.033 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.059 on 16 degrees of freedom
## Multiple R-squared: 0.2683, Adjusted R-squared: 0.1311
## F-statistic: 1.956 on 3 and 16 DF, p-value: 0.1614
summary(modelab)
##
## Call:
## lm(formula = min_OM ~ Treatment, data = springonedata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.770 -7.855 -0.703 4.098 29.470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.630 4.893 7.487 1.3e-06 ***
## TreatmentConNH -8.866 6.919 -1.281 0.218
## TreatmentExcH -8.898 6.919 -1.286 0.217
## TreatmentExcNH -11.634 6.919 -1.681 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.94 on 16 degrees of freedom
## Multiple R-squared: 0.1675, Adjusted R-squared: 0.01143
## F-statistic: 1.073 on 3 and 16 DF, p-value: 0.3883
anova(modela)
## Analysis of Variance Table
##
## Response: NetNitr_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 292.36 97.452 1.9558 0.1614
## Residuals 16 797.24 49.827
anova(modelab)
## Analysis of Variance Table
##
## Response: min_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 385.35 128.45 1.0732 0.3883
## Residuals 16 1915.02 119.69
modelaaov<-aov(NetNitr_OM ~ Treatment,data = springonedata)
posthoca <- TukeyHSD(x=modelaaov, 'Treatment', conf.level=0.95)
posthoca
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NetNitr_OM ~ Treatment, data = springonedata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH -5.732 -18.50477 7.04077 0.5855554
## ExcH-ConH -7.704 -20.47677 5.06877 0.3433513
## ExcNH-ConH -10.416 -23.18877 2.35677 0.1318043
## ExcH-ConNH -1.972 -14.74477 10.80077 0.9702765
## ExcNH-ConNH -4.684 -17.45677 8.08877 0.7238964
## ExcNH-ExcH -2.712 -15.48477 10.06077 0.9282176
plot(posthoca)
#####Fall 2018 model
falldata<- read.csv(file="falldata.csv")
str(falldata)
## 'data.frame': 20 obs. of 12 variables:
## $ Season : int 293 293 293 293 293 293 293 293 293 293 ...
## $ Time : chr "S3" "S3" "S3" "S3" ...
## $ Site : chr "Kramer" "Rhinehart" "Bachelor" "Western" ...
## $ Deer : chr "Con" "Con" "Con" "Con" ...
## $ Honeysuckle : chr "H " "H " "H " "H " ...
## $ Treatment : chr "ConH" "ConH" "ConH" "ConH" ...
## $ min_OM : num 5.34 4.06 6.57 6.31 2.63 ...
## $ NetNitr_OM : num 6.97 5.09 8.37 7.64 5.17 ...
## $ Nmin : num 1.2 0.52 0.44 0.41 0.39 0.89 0.87 0.83 1.08 0.82 ...
## $ NetNitr : num 1.17 0.68 0.54 0.55 0.56 0.94 0.99 0.74 0.75 0.77 ...
## $ NitrateN : num 24.5 14.4 11.4 11.4 11.7 ...
## $ Soilmicrobialresp: num 13.5 12.9 18 16 12.4 ...
modelb <- lm(NetNitr_OM ~ Treatment,data = falldata)
summary(modelb)
##
## Call:
## lm(formula = NetNitr_OM ~ Treatment, data = falldata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.940 -1.498 -0.036 1.015 7.620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.648 1.220 5.449 5.36e-05 ***
## TreatmentConNH 0.142 1.726 0.082 0.9354
## TreatmentExcH 4.642 1.726 2.690 0.0161 *
## TreatmentExcNH -0.352 1.726 -0.204 0.8409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.728 on 16 degrees of freedom
## Multiple R-squared: 0.4133, Adjusted R-squared: 0.3033
## F-statistic: 3.758 on 3 and 16 DF, p-value: 0.03235
anova(modelb)
## Analysis of Variance Table
##
## Response: NetNitr_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 83.908 27.9693 3.7576 0.03235 *
## Residuals 16 119.094 7.4434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modelbaov<-aov(NetNitr_OM ~ Treatment,data = falldata)
posthocb <- TukeyHSD(x=modelbaov, 'Treatment', conf.level=0.95)
posthocb
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NetNitr_OM ~ Treatment, data = falldata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH 0.142 -4.7946854 5.07868535 0.9997912
## ExcH-ConH 4.642 -0.2946854 9.57868535 0.0691009
## ExcNH-ConH -0.352 -5.2886854 4.58468535 0.9968688
## ExcH-ConNH 4.500 -0.4366854 9.43668535 0.0805130
## ExcNH-ConNH -0.494 -5.4306854 4.44268535 0.9914977
## ExcNH-ExcH -4.994 -9.9306854 -0.05731465 0.0469090
plot(posthocb)
####summer 2018 model
summerdata<- read.csv(file="summerdata.csv")
str(summerdata)
## 'data.frame': 20 obs. of 12 variables:
## $ Season : int 183 183 183 183 183 183 183 183 183 183 ...
## $ Time : chr "S2" "S2" "S2" "S2" ...
## $ Site : chr "Kramer" "Rhinehart" "Bachelor" "Western" ...
## $ Deer : chr "Con" "Con" "Con" "Con" ...
## $ Honeysuckle : chr "H " "H " "H " "H " ...
## $ Treatment : chr "ConH" "ConH" "ConH" "ConH" ...
## $ min_OM : num 17.33 6.27 5.33 11.43 16.93 ...
## $ NetNitr_OM : num 16.9 7.8 7.6 13 11.7 ...
## $ Nmin : logi NA NA NA NA NA NA ...
## $ NetNitr : logi NA NA NA NA NA NA ...
## $ NitrateN : logi NA NA NA NA NA NA ...
## $ Soilmicrobialresp: logi NA NA NA NA NA NA ...
modelc <- lm(NetNitr_OM ~ Treatment,data = summerdata)
summary(modelc)
##
## Call:
## lm(formula = NetNitr_OM ~ Treatment, data = summerdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.728 -3.405 0.308 1.877 5.530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.420 1.499 7.620 1.04e-06 ***
## TreatmentConNH 0.976 2.120 0.460 0.651
## TreatmentExcH -0.416 2.120 -0.196 0.847
## TreatmentExcNH -0.962 2.120 -0.454 0.656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.351 on 16 degrees of freedom
## Multiple R-squared: 0.05298, Adjusted R-squared: -0.1246
## F-statistic: 0.2984 on 3 and 16 DF, p-value: 0.8261
anova(modelc)
## Analysis of Variance Table
##
## Response: NetNitr_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 10.053 3.3511 0.2984 0.8261
## Residuals 16 179.706 11.2316
modelcaov<-aov(NetNitr_OM ~ Treatment,data = summerdata)
posthocc <- TukeyHSD(x=modelcaov, 'Treatment', conf.level=0.95)
posthocc
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NetNitr_OM ~ Treatment, data = summerdata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH 0.976 -5.088176 7.040176 0.9665731
## ExcH-ConH -0.416 -6.480176 5.648176 0.9972078
## ExcNH-ConH -0.962 -7.026176 5.102176 0.9679075
## ExcH-ConNH -1.392 -7.456176 4.672176 0.9116377
## ExcNH-ConNH -1.938 -8.002176 4.126176 0.7976044
## ExcNH-ExcH -0.546 -6.610176 5.518176 0.9937640
plot(posthocc)
###spring 2019 model
springtwodata<- read.csv(file="springtwodata.csv")
str(springtwodata)
## 'data.frame': 20 obs. of 12 variables:
## $ Season : int 137 137 137 137 137 137 137 137 137 137 ...
## $ Time : chr "S4" "S4" "S4" "S4" ...
## $ Site : chr "Kramer" "Rhinehart" "Bachelor" "Western" ...
## $ Deer : chr "Con" "Con" "Con" "Con" ...
## $ Honeysuckle : chr "H " "H " "H " "H " ...
## $ Treatment : chr "ConH" "ConH" "ConH" "ConH" ...
## $ min_OM : num 10.32 7.98 19.54 18.73 6.4 ...
## $ NetNitr_OM : num 12.51 11.1 19.02 19.75 7.82 ...
## $ Nmin : logi NA NA NA NA NA NA ...
## $ NetNitr : logi NA NA NA NA NA NA ...
## $ NitrateN : logi NA NA NA NA NA NA ...
## $ Soilmicrobialresp: logi NA NA NA NA NA NA ...
modeld <- lm(NetNitr_OM ~ Treatment,data = springtwodata)
summary(modeld)
##
## Call:
## lm(formula = NetNitr_OM ~ Treatment, data = springtwodata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.220 -3.217 -1.054 3.835 9.825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.040 2.197 6.392 1.21e-05 ***
## TreatmentConNH -2.295 3.295 -0.697 0.4967
## TreatmentExcH -6.650 3.106 -2.141 0.0491 *
## TreatmentExcNH -8.936 3.106 -2.877 0.0115 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.912 on 15 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4008, Adjusted R-squared: 0.281
## F-statistic: 3.345 on 3 and 15 DF, p-value: 0.04767
anova(modeld)
## Analysis of Variance Table
##
## Response: NetNitr_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 242.06 80.688 3.3447 0.04767 *
## Residuals 15 361.87 24.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modeldaov<-aov(NetNitr_OM ~ Treatment,data = springtwodata)
posthocd <- TukeyHSD(x=modeldaov, 'Treatment', conf.level=0.95)
posthocd
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NetNitr_OM ~ Treatment, data = springtwodata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH -2.295 -11.79122 7.20122043 0.8968149
## ExcH-ConH -6.650 -15.60312 2.30312248 0.1851614
## ExcNH-ConH -8.936 -17.88912 0.01712248 0.0505198
## ExcH-ConNH -4.355 -13.85122 5.14122043 0.5639729
## ExcNH-ConNH -6.641 -16.13722 2.85522043 0.2258424
## ExcNH-ExcH -2.286 -11.23912 6.66712248 0.8811364
plot(posthocd)
######################All season NetNitrification model
allseasondata<- read.csv(file="allseasondata.csv")
str(allseasondata)
## 'data.frame': 80 obs. of 12 variables:
## $ Season : int 64 64 64 64 64 64 64 64 64 64 ...
## $ Time : chr "Spring1" "Spring1" "Spring1" "Spring1" ...
## $ Site : chr "Kramer" "Rhinehart" "Bachelor" "Western" ...
## $ Deer : chr "Con" "Con" "Con" "Con" ...
## $ Honeysuckle : chr "H " "H " "H " "H " ...
## $ Treatment : chr "ConH" "ConH" "ConH" "ConH" ...
## $ min_OM : num 29.9 22.9 36.6 27.7 66.1 ...
## $ NetNitr_OM : num 32 23.6 38.4 29.2 47.4 ...
## $ Nmin : num 1.24 0.85 0.96 0.88 1.57 1.86 1.16 1.02 2.79 1.45 ...
## $ NetNitr : num 1.32 1.16 0.99 0.98 1.64 1.63 1.22 0.7 2 1.37 ...
## $ NitrateN : num 22.5 19.7 16.9 16.7 27.9 ...
## $ Soilmicrobialresp: num 39 25.9 43.5 41 34.2 ...
model2 <- lm(NetNitr_OM ~ Treatment,data = allseasondata)
summary(model2)
##
## Call:
## lm(formula = NetNitr_OM ~ Treatment, data = allseasondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.274 -6.920 -3.537 5.057 30.834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.557 2.179 7.597 6.94e-11 ***
## TreatmentConNH -1.565 3.122 -0.501 0.6177
## TreatmentExcH -2.532 3.082 -0.822 0.4139
## TreatmentExcNH -5.167 3.082 -1.676 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.746 on 75 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.038, Adjusted R-squared: -0.0004758
## F-statistic: 0.9876 on 3 and 75 DF, p-value: 0.4033
anova(model2)
## Analysis of Variance Table
##
## Response: NetNitr_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 281.4 93.810 0.9876 0.4033
## Residuals 75 7123.8 94.984
model2aov<-aov(NetNitr_OM ~ Treatment,data = allseasondata)
posthoc2 <- TukeyHSD(x=model2aov, 'Treatment', conf.level=0.95)
posthoc2
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NetNitr_OM ~ Treatment, data = allseasondata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH -1.5649211 -9.768849 6.639007 0.9585381
## ExcH-ConH -2.5320000 -10.630066 5.566066 0.8440951
## ExcNH-ConH -5.1665000 -13.264566 2.931566 0.3432866
## ExcH-ConNH -0.9670789 -9.171007 7.236849 0.9896146
## ExcNH-ConNH -3.6015789 -11.805507 4.602349 0.6577468
## ExcNH-ExcH -2.6345000 -10.732566 5.463566 0.8279144
plot(posthoc2)
###All season min_OM modeling
minmodel <- lm(min_OM ~ Treatment,data = allseasondata)
summary(minmodel)
##
## Call:
## lm(formula = min_OM ~ Treatment, data = allseasondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.786 -7.668 -4.308 4.337 49.684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.416 2.546 6.447 9.9e-09 ***
## TreatmentConNH -2.742 3.648 -0.752 0.455
## TreatmentExcH -3.226 3.601 -0.896 0.373
## TreatmentExcNH -5.078 3.601 -1.410 0.163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.39 on 75 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02645, Adjusted R-squared: -0.01249
## F-statistic: 0.6792 on 3 and 75 DF, p-value: 0.5675
anova(minmodel)
## Analysis of Variance Table
##
## Response: min_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 264.2 88.073 0.6792 0.5675
## Residuals 75 9725.2 129.669
minmodelaov<-aov(min_OM ~ Treatment,data = allseasondata)
minposthoc <- TukeyHSD(x=minmodelaov, 'Treatment', conf.level=0.95)
minposthoc
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = min_OM ~ Treatment, data = allseasondata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH -2.7417895 -12.32729 6.843715 0.8757130
## ExcH-ConH -3.2265000 -12.68832 6.235316 0.8069236
## ExcNH-ConH -5.0785000 -14.54032 4.383316 0.4969601
## ExcH-ConNH -0.4847105 -10.07022 9.100794 0.9991549
## ExcNH-ConNH -2.3367105 -11.92222 7.248794 0.9185086
## ExcNH-ExcH -1.8520000 -11.31382 7.609816 0.9554301
plot(minposthoc)
###All season Soilmicrobialresp_OM modeling
respmodel <- lm(Soilmicrobialresp ~ Treatment,data = allseasondata)
summary(respmodel)
##
## Call:
## lm(formula = Soilmicrobialresp ~ Treatment, data = allseasondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.140 -10.368 -4.973 11.396 30.681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.3575 2.7923 8.007 1.07e-11 ***
## TreatmentConNH 2.1915 3.9489 0.555 0.581
## TreatmentExcH 1.5100 3.9489 0.382 0.703
## TreatmentExcNH -0.2275 3.9489 -0.058 0.954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.49 on 76 degrees of freedom
## Multiple R-squared: 0.0069, Adjusted R-squared: -0.0323
## F-statistic: 0.176 on 3 and 76 DF, p-value: 0.9123
anova(respmodel)
## Analysis of Variance Table
##
## Response: Soilmicrobialresp
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 82.3 27.449 0.176 0.9123
## Residuals 76 11851.3 155.938
respmodelaov<-aov(Soilmicrobialresp ~ Treatment,data = allseasondata)
respposthoc <- TukeyHSD(x=respmodelaov, 'Treatment', conf.level=0.95)
respposthoc
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Soilmicrobialresp ~ Treatment, data = allseasondata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH 2.1915 -8.181443 12.564443 0.9449221
## ExcH-ConH 1.5100 -8.862943 11.882943 0.9808364
## ExcNH-ConH -0.2275 -10.600443 10.145443 0.9999307
## ExcH-ConNH -0.6815 -11.054443 9.691443 0.9981572
## ExcNH-ConNH -2.4190 -12.791943 7.953943 0.9277825
## ExcNH-ExcH -1.7375 -12.110443 8.635443 0.9713298
plot(respposthoc)
###All season NitrateN modeling
NNmodel <- lm(NitrateN ~ Treatment,data = allseasondata)
summary(NNmodel)
##
## Call:
## lm(formula = NitrateN ~ Treatment, data = allseasondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.418 -5.065 -1.122 4.343 16.510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.7865 1.4170 11.847 <2e-16 ***
## TreatmentConNH -1.1320 2.0039 -0.565 0.5738
## TreatmentExcH 0.7235 2.0039 0.361 0.7191
## TreatmentExcNH -4.3185 2.0039 -2.155 0.0343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.337 on 76 degrees of freedom
## Multiple R-squared: 0.08878, Adjusted R-squared: 0.05281
## F-statistic: 2.468 on 3 and 76 DF, p-value: 0.06841
anova(NNmodel)
## Analysis of Variance Table
##
## Response: NitrateN
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 297.36 99.121 2.4683 0.06841 .
## Residuals 76 3051.94 40.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
NNmodelaov<-aov(NitrateN ~ Treatment,data = allseasondata)
NNposthoc <- TukeyHSD(x=NNmodelaov, 'Treatment', conf.level=0.95)
NNposthoc
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NitrateN ~ Treatment, data = allseasondata)
##
## $Treatment
## diff lwr upr p adj
## ConNH-ConH -1.1320 -6.395897 4.1318972 0.9421571
## ExcH-ConH 0.7235 -4.540397 5.9873972 0.9837708
## ExcNH-ConH -4.3185 -9.582397 0.9453972 0.1453840
## ExcH-ConNH 1.8555 -3.408397 7.1193972 0.7910620
## ExcNH-ConNH -3.1865 -8.450397 2.0773972 0.3903249
## ExcNH-ExcH -5.0420 -10.305897 0.2218972 0.0654165
plot(NNposthoc)
####Split plot Analysis
library(agricolae)
trt= as.factor(allseasondata$Honeysuckle)
blc=as.factor(allseasondata$Time)
yr=as.factor(allseasondata$Deer)
model1=with(allseasondata, sp.plot(blc, yr, trt, NetNitr_OM))
##
## ANALYSIS SPLIT PLOT: NetNitr_OM
## Class level information
##
## yr : Con Excl
## trt : H NH
## blc : Spring1 Summer Fall Spring2
##
## Number of observations: 80
##
## Analysis of Variance Table
##
## Response: NetNitr_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## blc 3 5319.0 1772.98 20.5046 0.01678 *
## yr 1 173.8 173.80 2.0100 0.25129
## Ea 3 259.4 86.47 0.6207 0.60422
## trt 1 98.0 98.03 6.3256 0.04559 *
## yr:trt 1 4.2 4.18 0.2696 0.62218
## Eb 6 93.0 15.50 0.6207 0.60422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## cv(a) = 65.3 %, cv(b) = 27.7 %, Mean = 14.23114
#where fp is the name of datafile, trt, rep and yr are the column of the data table, yr = main plot factor, trt=subplot factor and blc=replications
gla1=model1$gl.a
glb1=model1$gl.b
ea1=model1$Ea
eb1=model1$Eb
#lsd test for main, sub plot and interaction effects (NetNitr_OM)
Mainplot=with(allseasondata, LSD.test(NetNitr_OM, yr, gla1, ea1, console = TRUE))
##
## Study: NetNitr_OM ~ yr
##
## LSD t Test for NetNitr_OM
##
## Mean Square Error: 86.46739
##
## yr, means and individual ( 95 %) CI
##
## NetNitr_OM std r LCL UCL Min Max
## Con 15.79410 10.688583 39 11.055449 20.53276 4.89 47.39
## Excl 12.70725 8.587012 40 8.028205 17.38630 1.30 36.98
##
## Alpha: 0.05 ; DF Error: 3
## Critical Value of t: 3.182446
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## NetNitr_OM groups
## Con 15.79410 a
## Excl 12.70725 a
Subplot=with(allseasondata, LSD.test(NetNitr_OM, trt, glb1, eb1, console = TRUE))
##
## Study: NetNitr_OM ~ trt
##
## LSD t Test for NetNitr_OM
##
## Mean Square Error: 15.49668
##
## trt, means and individual ( 95 %) CI
##
## NetNitr_OM std r LCL UCL Min Max
## H 15.29050 10.502839 40 13.76747 16.81353 1.75 47.39
## NH 13.14462 8.903325 39 11.60219 14.68704 1.30 32.38
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## NetNitr_OM groups
## H 15.29050 a
## NH 13.14462 a
Interaction=LSD.test(allseasondata$NetNitr_OM, yr:trt, glb1, eb1, console = TRUE)
##
## Study: allseasondata$NetNitr_OM ~ yr:trt
##
## LSD t Test for allseasondata$NetNitr_OM
##
## Mean Square Error: 15.49668
##
## yr:trt, means and individual ( 95 %) CI
##
## allseasondata.NetNitr_OM std r LCL UCL Min Max
## Con:H 16.55650 11.934784 20 14.402614 18.71039 5.09 47.39
## Con:NH 14.99158 9.460844 19 12.781739 17.20142 4.89 32.38
## Excl:H 14.02450 8.978404 20 11.870614 16.17839 1.75 36.98
## Excl:NH 11.39000 8.190833 20 9.236114 13.54389 1.30 28.42
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## allseasondata$NetNitr_OM groups
## Con:H 16.55650 a
## Con:NH 14.99158 a
## Excl:H 14.02450 ab
## Excl:NH 11.39000 b
plot(Interaction,ylab= "N net nitrification_OM group means",main ="")
##########lsd test for main, sub plot and interaction effects (min_OM)
model2=with(allseasondata, sp.plot(blc, yr, trt, min_OM))
##
## ANALYSIS SPLIT PLOT: min_OM
## Class level information
##
## yr : Con Excl
## trt : H NH
## blc : Spring1 Summer Fall Spring2
##
## Number of observations: 80
##
## Analysis of Variance Table
##
## Response: min_OM
## Df Sum Sq Mean Sq F value Pr(>F)
## blc 3 6755.2 2251.74 34.2468 0.008043 **
## yr 1 142.5 142.50 2.1672 0.237377
## Ea 3 197.3 65.75 0.4053 0.749716
## trt 1 115.9 115.94 5.0208 0.066281 .
## yr:trt 1 5.7 5.73 0.2481 0.636118
## Eb 6 138.5 23.09 0.4053 0.749716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## cv(a) = 59.4 %, cv(b) = 35.2 %, Mean = 13.65405
#where fp is the name of datafile, trt, rep and yr are the column of the data table, yr = main plot factor, trt=subplot factor and blc=replications
gla2=model2$gl.a
glb2=model2$gl.b
ea2=model2$Ea
eb2=model2$Eb
Mainplot=with(allseasondata, LSD.test(min_OM, yr, gla2, ea2, console = TRUE))
##
## Study: min_OM ~ yr
##
## LSD t Test for min_OM
##
## Mean Square Error: 65.75033
##
## yr, means and individual ( 95 %) CI
##
## min_OM std r LCL UCL Min Max
## Con 15.08026 12.729022 39 10.948091 19.21242 2.63 66.10
## Excl 12.26350 9.708151 40 8.183313 16.34369 0.19 42.27
##
## Alpha: 0.05 ; DF Error: 3
## Critical Value of t: 3.182446
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## min_OM groups
## Con 15.08026 a
## Excl 12.26350 a
Subplot=with(allseasondata, LSD.test(min_OM, trt, glb2, eb2, console = TRUE))
##
## Study: min_OM ~ trt
##
## LSD t Test for min_OM
##
## Mean Square Error: 23.09079
##
## trt, means and individual ( 95 %) CI
##
## min_OM std r LCL UCL Min Max
## H 14.80275 12.967421 40 12.94363 16.66187 0.19 66.10
## NH 12.47590 9.353436 39 10.59309 14.35870 2.97 34.03
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## min_OM groups
## H 14.80275 a
## NH 12.47590 a
Interaction2=LSD.test(allseasondata$min_OM, yr:trt, glb2, eb2, console = TRUE)
##
## Study: allseasondata$min_OM ~ yr:trt
##
## LSD t Test for allseasondata$min_OM
##
## Mean Square Error: 23.09079
##
## yr:trt, means and individual ( 95 %) CI
##
## allseasondata.min_OM std r LCL UCL Min Max
## Con:H 16.41600 15.160168 20 13.786805 19.04519 2.63 66.10
## Con:NH 13.67421 9.766820 19 10.976714 16.37171 4.19 34.03
## Excl:H 13.18950 10.480859 20 10.560305 15.81869 0.19 42.27
## Excl:NH 11.33750 9.044511 20 8.708305 13.96669 2.97 33.06
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## allseasondata$min_OM groups
## Con:H 16.41600 a
## Con:NH 13.67421 ab
## Excl:H 13.18950 ab
## Excl:NH 11.33750 b
plot(Interaction2,ylab= "N min_OM group means")
##########lsd test for main, sub plot and interaction effects (Soilmicrobialresp)
model3=with(allseasondata, sp.plot(blc, yr, trt, Soilmicrobialresp))
##
## ANALYSIS SPLIT PLOT: Soilmicrobialresp
## Class level information
##
## yr : Con Excl
## trt : H NH
## blc : Spring1 Summer Fall Spring2
##
## Number of observations: 80
##
## Analysis of Variance Table
##
## Response: Soilmicrobialresp
## Df Sum Sq Mean Sq F value Pr(>F)
## blc 3 8467.5 2822.51 50.1716 0.00461 **
## yr 1 4.1 4.13 0.0734 0.80397
## Ea 3 168.8 56.26 0.5154 0.67312
## trt 1 1.0 1.03 0.0428 0.84293
## yr:trt 1 77.2 77.19 3.2060 0.12356
## Eb 6 144.4 24.07 0.5154 0.67312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## cv(a) = 32.3 %, cv(b) = 21.1 %, Mean = 23.226
#where fp is the name of datafile, trt, rep and yr are the column of the data table, yr = main plot factor, trt=subplot factor and blc=replications
gla3=model3$gl.a
glb3=model3$gl.b
ea3=model3$Ea
eb3=model3$Eb
Mainplot=with(allseasondata, LSD.test(Soilmicrobialresp, yr, gla3, ea3, console = TRUE))
##
## Study: Soilmicrobialresp ~ yr
##
## LSD t Test for Soilmicrobialresp
##
## Mean Square Error: 56.25718
##
## yr, means and individual ( 95 %) CI
##
## Soilmicrobialresp std r LCL UCL Min Max
## Con 23.45325 11.92472 40 19.67909 27.22741 10.46 55.23
## Excl 22.99875 12.79395 40 19.22459 26.77291 5.99 51.66
##
## Alpha: 0.05 ; DF Error: 3
## Critical Value of t: 3.182446
##
## least Significant Difference: 5.337465
##
## Treatments with the same letter are not significantly different.
##
## Soilmicrobialresp groups
## Con 23.45325 a
## Excl 22.99875 a
Subplot=with(allseasondata, LSD.test(Soilmicrobialresp, trt, glb3, eb3, console = TRUE))
##
## Study: Soilmicrobialresp ~ trt
##
## LSD t Test for Soilmicrobialresp
##
## Mean Square Error: 24.075
##
## trt, means and individual ( 95 %) CI
##
## Soilmicrobialresp std r LCL UCL Min Max
## H 23.1125 12.03731 40 21.21417 25.01083 8.20 51.66
## NH 23.3395 12.69121 40 21.44117 25.23783 5.99 55.23
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 2.684643
##
## Treatments with the same letter are not significantly different.
##
## Soilmicrobialresp groups
## NH 23.3395 a
## H 23.1125 a
Interaction3=LSD.test(allseasondata$Soilmicrobialresp, yr:trt, glb3, eb3, console = TRUE)
##
## Study: allseasondata$Soilmicrobialresp ~ yr:trt
##
## LSD t Test for allseasondata$Soilmicrobialresp
##
## Mean Square Error: 24.075
##
## yr:trt, means and individual ( 95 %) CI
##
## allseasondata.Soilmicrobialresp std r LCL UCL Min
## Con:H 22.3575 11.00991 20 19.67286 25.04214 10.55
## Con:NH 24.5490 12.96673 20 21.86436 27.23364 10.46
## Excl:H 23.8675 13.22882 20 21.18286 26.55214 8.20
## Excl:NH 22.1300 12.62517 20 19.44536 24.81464 5.99
## Max
## Con:H 43.45
## Con:NH 55.23
## Excl:H 51.66
## Excl:NH 43.69
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 3.796658
##
## Treatments with the same letter are not significantly different.
##
## allseasondata$Soilmicrobialresp groups
## Con:NH 24.5490 a
## Excl:H 23.8675 a
## Con:H 22.3575 a
## Excl:NH 22.1300 a
plot(Interaction3,ylab= "Soil microbial respiration group means")
###Soil N minerilization modeling
model4=with(allseasondata, sp.plot(blc, yr, trt, Nmin))
##
## ANALYSIS SPLIT PLOT: Nmin
## Class level information
##
## yr : Con Excl
## trt : H NH
## blc : Spring1 Summer Fall Spring2
##
## Number of observations: 80
##
## Analysis of Variance Table
##
## Response: Nmin
## Df Sum Sq Mean Sq F value Pr(>F)
## blc 3 7.2943 2.43144 8.3482 0.05745 .
## yr 1 0.0567 0.05671 0.1947 0.68889
## Ea 3 0.8738 0.29125 0.3532 0.78693
## trt 1 0.3740 0.37401 8.8903 0.02458 *
## yr:trt 1 0.3290 0.32896 7.8195 0.03132 *
## Eb 6 0.2524 0.04207 0.3532 0.78693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## cv(a) = 70.4 %, cv(b) = 26.7 %, Mean = 0.766875
#where fp is the name of datafile, trt, rep and yr are the column of the data table, yr = main plot factor, trt=subplot factor and blc=replications
gla4=model4$gl.a
glb4=model4$gl.b
ea4=model4$Ea
eb4=model4$Eb
Mainplot=with(allseasondata, LSD.test(Nmin, yr, gla4, ea4, console = TRUE))
##
## Study: Nmin ~ yr
##
## LSD t Test for Nmin
##
## Mean Square Error: 0.2912546
##
## yr, means and individual ( 95 %) CI
##
## Nmin std r LCL UCL Min Max
## Con 0.74025 0.3536657 40 0.468689 1.011811 0.25 1.57
## Excl 0.79350 0.5132428 40 0.521939 1.065061 0.12 2.79
##
## Alpha: 0.05 ; DF Error: 3
## Critical Value of t: 3.182446
##
## least Significant Difference: 0.3840453
##
## Treatments with the same letter are not significantly different.
##
## Nmin groups
## Excl 0.79350 a
## Con 0.74025 a
Subplot=with(allseasondata, LSD.test(Nmin, trt, glb4, eb4, console = TRUE))
##
## Study: Nmin ~ trt
##
## LSD t Test for Nmin
##
## Mean Square Error: 0.04206958
##
## trt, means and individual ( 95 %) CI
##
## Nmin std r LCL UCL Min Max
## H 0.83525 0.5058022 40 0.7558953 0.9146047 0.12 2.79
## NH 0.69850 0.3528823 40 0.6191453 0.7778547 0.18 1.41
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 0.1122244
##
## Treatments with the same letter are not significantly different.
##
## Nmin groups
## H 0.83525 a
## NH 0.69850 b
Interaction4=LSD.test(allseasondata$Nmin, yr:trt, glb4, eb4, console = TRUE)
##
## Study: allseasondata$Nmin ~ yr:trt
##
## LSD t Test for allseasondata$Nmin
##
## Mean Square Error: 0.04206958
##
## yr:trt, means and individual ( 95 %) CI
##
## allseasondata.Nmin std r LCL UCL Min Max
## Con:H 0.7445 0.3649870 20 0.6322756 0.8567244 0.31 1.57
## Con:NH 0.7360 0.3514092 20 0.6237756 0.8482244 0.25 1.39
## Excl:H 0.9260 0.6120320 20 0.8137756 1.0382244 0.12 2.79
## Excl:NH 0.6610 0.3593840 20 0.5487756 0.7732244 0.18 1.41
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 0.1587093
##
## Treatments with the same letter are not significantly different.
##
## allseasondata$Nmin groups
## Excl:H 0.9260 a
## Con:H 0.7445 b
## Con:NH 0.7360 b
## Excl:NH 0.6610 b
plot(Interaction4,ylab= " N mineralization group means")
##########lsd test for main, sub plot and interaction effects (NetNitr)
model5=with(allseasondata, sp.plot(blc, yr, trt, NetNitr))
##
## ANALYSIS SPLIT PLOT: NetNitr
## Class level information
##
## yr : Con Excl
## trt : H NH
## blc : Spring1 Summer Fall Spring2
##
## Number of observations: 80
##
## Analysis of Variance Table
##
## Response: NetNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## blc 3 4.8344 1.61148 7.0286 0.071773 .
## yr 1 0.0605 0.06050 0.2639 0.642912
## Ea 3 0.6878 0.22928 0.0777 0.971856
## trt 1 0.4805 0.48050 19.8499 0.004305 **
## yr:trt 1 0.1882 0.18818 7.7739 0.031654 *
## Eb 6 0.1452 0.02421 0.0777 0.971856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## cv(a) = 59.8 %, cv(b) = 19.4 %, Mean = 0.8005
#where fp is the name of datafile, trt, rep and yr are the column of the data table, yr = main plot factor, trt=subplot factor and blc=replications
gla5=model5$gl.a
glb5=model5$gl.b
ea5=model5$Ea
eb5=model5$Eb
Mainplot=with(allseasondata, LSD.test(NetNitr, yr, gla5, ea5, console = TRUE))
##
## Study: NetNitr ~ yr
##
## LSD t Test for NetNitr
##
## Mean Square Error: 0.2292767
##
## yr, means and individual ( 95 %) CI
##
## NetNitr std r LCL UCL Min Max
## Con 0.828 0.3303394 40 0.5870588 1.068941 0.34 1.64
## Excl 0.773 0.4206479 40 0.5320588 1.013941 0.05 2.00
##
## Alpha: 0.05 ; DF Error: 3
## Critical Value of t: 3.182446
##
## least Significant Difference: 0.3407423
##
## Treatments with the same letter are not significantly different.
##
## NetNitr groups
## Con 0.828 a
## Excl 0.773 a
Subplot=with(allseasondata, LSD.test(NetNitr, trt, glb5, eb5, console = TRUE))
##
## Study: NetNitr ~ trt
##
## LSD t Test for NetNitr
##
## Mean Square Error: 0.02420667
##
## trt, means and individual ( 95 %) CI
##
## NetNitr std r LCL UCL Min Max
## H 0.878 0.3913828 40 0.8178056 0.9381944 0.30 2.0
## NH 0.723 0.3494553 40 0.6628056 0.7831944 0.05 1.4
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 0.08512768
##
## Treatments with the same letter are not significantly different.
##
## NetNitr groups
## H 0.878 a
## NH 0.723 b
Interaction5=LSD.test(allseasondata$NetNitr, yr:trt, glb5, eb5, console = TRUE)
##
## Study: allseasondata$NetNitr ~ yr:trt
##
## LSD t Test for allseasondata$NetNitr
##
## Mean Square Error: 0.02420667
##
## yr:trt, means and individual ( 95 %) CI
##
## allseasondata.NetNitr std r LCL UCL Min Max
## Con:H 0.857 0.3348542 20 0.7718723 0.9421277 0.39 1.64
## Con:NH 0.799 0.3318037 20 0.7138723 0.8841277 0.34 1.34
## Excl:H 0.899 0.4487398 20 0.8138723 0.9841277 0.30 2.00
## Excl:NH 0.647 0.3583456 20 0.5618723 0.7321277 0.05 1.40
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 0.1203887
##
## Treatments with the same letter are not significantly different.
##
## allseasondata$NetNitr groups
## Excl:H 0.899 a
## Con:H 0.857 a
## Con:NH 0.799 a
## Excl:NH 0.647 b
plot(Interaction5,ylab= " N net nitrification group means")
##########lsd test for main, sub plot and interaction effects (NitrateN)
model6=with(allseasondata, sp.plot(blc, yr, trt, NitrateN))
##
## ANALYSIS SPLIT PLOT: NitrateN
## Class level information
##
## yr : Con Excl
## trt : H NH
## blc : Spring1 Summer Fall Spring2
##
## Number of observations: 80
##
## Analysis of Variance Table
##
## Response: NitrateN
## Df Sum Sq Mean Sq F value Pr(>F)
## blc 3 835.54 278.513 2.8971 0.202811
## yr 1 30.33 30.332 0.3155 0.613547
## Ea 3 288.41 96.135 0.1133 0.952039
## trt 1 190.59 190.591 17.9716 0.005444 **
## yr:trt 1 76.44 76.441 7.2079 0.036306 *
## Eb 6 63.63 10.605 0.1133 0.952039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## cv(a) = 62.8 %, cv(b) = 20.9 %, Mean = 15.60475
#where fp is the name of datafile, trt, rep and yr are the column of the data table, yr = main plot factor, trt=subplot factor and blc=replications
gla6=model6$gl.a
glb6=model6$gl.b
ea6=model6$Ea
eb6=model6$Eb
Mainplot=with(allseasondata, LSD.test(NitrateN, yr, gla6, ea6, console = TRUE))
##
## Study: NitrateN ~ yr
##
## LSD t Test for NitrateN
##
## Mean Square Error: 96.13501
##
## yr, means and individual ( 95 %) CI
##
## NitrateN std r LCL UCL Min Max
## Con 16.2205 5.785652 40 11.28681 21.15419 7.04 28.04
## Excl 14.9890 7.185261 40 10.05531 19.92269 1.05 34.02
##
## Alpha: 0.05 ; DF Error: 3
## Critical Value of t: 3.182446
##
## least Significant Difference: 6.977292
##
## Treatments with the same letter are not significantly different.
##
## NitrateN groups
## Con 16.2205 a
## Excl 14.9890 a
Subplot=with(allseasondata, LSD.test(NitrateN, trt, glb6, eb6, console = TRUE))
##
## Study: NitrateN ~ trt
##
## LSD t Test for NitrateN
##
## Mean Square Error: 10.60514
##
## trt, means and individual ( 95 %) CI
##
## NitrateN std r LCL UCL Min Max
## H 17.14825 6.631574 40 15.88832 18.40818 6.28 34.02
## NH 14.06125 6.083975 40 12.80132 15.32118 1.05 28.04
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 1.78181
##
## Treatments with the same letter are not significantly different.
##
## NitrateN groups
## H 17.14825 a
## NH 14.06125 b
Interaction6=LSD.test(allseasondata$NitrateN, yr:trt, glb6, eb6, console = TRUE)
##
## Study: allseasondata$NitrateN ~ yr:trt
##
## LSD t Test for allseasondata$NitrateN
##
## Mean Square Error: 10.60514
##
## yr:trt, means and individual ( 95 %) CI
##
## allseasondata.NitrateN std r LCL UCL Min Max
## Con:H 16.7865 5.691473 20 15.00469 18.56831 8.09 27.93
## Con:NH 15.6545 5.970093 20 13.87269 17.43631 7.04 28.04
## Excl:H 17.5100 7.589585 20 15.72819 19.29181 6.28 34.02
## Excl:NH 12.4680 5.915365 20 10.68619 14.24981 1.05 23.81
##
## Alpha: 0.05 ; DF Error: 6
## Critical Value of t: 2.446912
##
## least Significant Difference: 2.51986
##
## Treatments with the same letter are not significantly different.
##
## allseasondata$NitrateN groups
## Excl:H 17.5100 a
## Con:H 16.7865 a
## Con:NH 15.6545 a
## Excl:NH 12.4680 b
plot(Interaction6,ylab= "Nitrate nitrogen group means")
