rm(list = ls())
#Load in libraries
library(readr)
library(lme4)
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 3.5.3
library(car)
## Warning: package 'car' was built under R version 3.5.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 3.5.2
library(emmeans)
## Warning: package 'emmeans' was built under R version 3.5.3
setwd("c:/users/Paul/Documents/Rwork")
scatexperimentdata<- read.csv(file="scatexperimentdata2.csv")
scatexperimentdata$WeekF =as.factor(scatexperimentdata$Week)
str(scatexperimentdata)
## 'data.frame': 48 obs. of 20 variables:
## $ Week : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Deer : Factor w/ 1 level "Excl": 1 1 1 1 1 1 1 1 1 1 ...
## $ Honeysuckle : Factor w/ 2 levels "H","NH": 1 1 1 1 1 1 1 1 1 1 ...
## $ CtotalN : num 3.26 4.23 7.04 9.84 9.16 ...
## $ TrtotalN : num 2.19 2.11 3.99 5.87 7.3 ...
## $ ConNO3 : num 2.1 3.75 6.51 9.27 8.6 ...
## $ TrtNO3 : num 1.05 1.66 3.49 5.32 6.76 ...
## $ CNcummulative : num 3.26 10.85 22.59 40.08 59.82 ...
## $ TrtNcummulative : num 2.19 6.09 11.66 20.56 32.33 ...
## $ CNitratecummulative : num 2.1 8.09 18.89 35.3 53.9 ...
## $ TrtNitratecummulative: num 1.05 3.37 7.98 15.8 26.45 ...
## $ DNcummulative : num -1.06 -4.75 -10.93 -19.52 -27.49 ...
## $ DNitratecummulative : num -1.05 -4.73 -10.92 -19.5 -27.45 ...
## $ Cmin : num 3.26 5.43 7.53 10.02 11.96 ...
## $ Trtmin : num 2.19 3.05 3.89 5.14 6.47 ...
## $ CNitr : num 2.1 4.05 6.3 8.83 10.78 ...
## $ TrtNitr : num 1.05 1.69 2.66 3.95 5.29 ...
## $ sqDmin : num 1.14 5.66 13.27 23.81 30.23 ...
## $ sqDNitr : num 1.1 5.57 13.23 23.77 30.14 ...
## $ WeekF : Factor w/ 24 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
attach(scatexperimentdata)
#Col names
#Week= Week as a continous variable,
#WeekF =Week as a factor
#Deer = Exclosure,
#Honeysuckle = Presence (H) or Absence of honeysuckle (NH),
#Ctotal N =Control total Nitrogen,
#TrttotalN=Treatment total Nitrogen,
#ConNO3 = Control Nitrate content,
#TrtNO3 = Treatment Nitrate content,
#Cncummulative= Accumulated Nitrogen in the control,
#TrtNcummulative = Accumulated Nitrogen in the treatment,
#CNitratecummulative =Accumulated Nitrate in the control,
#TrtNitratecummulate =Accumulated Nitrate in the treatment,
#DNcummulative =Difference in cummulative Nitrogen between control and treatment,
#DNitratecummulative =Difference in cummulative Nitrate content between control and treatment,
#Cmin = mineralization rate in the control,
#Trtmin = minerelization rate in the treatment,
#CNitr =Nitrification rate in the control,
#TrtNitr = Nitrification rate in the treatment
#sqDmin= Square of the Difference in minerelization rate between the treatment and control
#sqDNitr= Square of the Difference in nitrification rate between the treatment and control
##Let's begin with the cummulative variables##########################
#Boxplots of variables
boxplot(CNcummulative,TrtNcummulative)
boxplot(CNcummulative ~ Honeysuckle)
boxplot(TrtNcummulative ~ Honeysuckle)
#t-test for CNcummulative,TrtNcummulative
#Ho: mean CNcummulative= of TrtNcummulative
#two-sample t.test
t.test(CNcummulative,TrtNcummulative,mu=0,alt="two.sided",conf=0.95,var.eq=F, paired= F)
##
## Welch Two Sample t-test
##
## data: CNcummulative and TrtNcummulative
## t = -1.8943, df = 76.868, p-value = 0.06195
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -241.77349 6.03807
## sample estimates:
## mean of x mean of y
## 291.0502 408.9179
t.test(CNitratecummulative,TrtNitratecummulative,mu=0,alt="two.sided",conf=0.95,var.eq=F, paired= F)
##
## Welch Two Sample t-test
##
## data: CNitratecummulative and TrtNitratecummulative
## t = -1.9513, df = 76.118, p-value = 0.0547
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -238.183661 2.437411
## sample estimates:
## mean of x mean of y
## 275.5785 393.4517
#SIMPLE LINEAR MODELS OF sqDmin, sqDNitr,Cmin,Trtmin,CNitr, and TrtNitr using Week
#NOTE: Models with "WeekF" do NOT accept "*" and they return "N/A" for the variable "WeekF"
#Sample model using "*" at the end of the codes
#sqDmin
#with WeekF
sqDminmodel1<-lm(sqDmin~Honeysuckle+WeekF,data=scatexperimentdata)
summary(sqDminmodel1)
##
## Call:
## lm(formula = sqDmin ~ Honeysuckle + WeekF, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.577 -4.196 0.000 4.196 7.577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.978 4.762 -0.415 0.681822
## HoneysuckleNH 11.955 1.905 6.276 2.10e-06 ***
## WeekF2 6.590 6.599 0.999 0.328364
## WeekF3 16.070 6.599 2.435 0.023049 *
## WeekF4 28.415 6.599 4.306 0.000263 ***
## WeekF5 29.930 6.599 4.536 0.000148 ***
## WeekF6 21.925 6.599 3.322 0.002965 **
## WeekF7 10.910 6.599 1.653 0.111861
## WeekF8 1.410 6.599 0.214 0.832689
## WeekF9 -3.355 6.599 -0.508 0.616009
## WeekF10 -3.260 6.599 -0.494 0.625982
## WeekF11 1.060 6.599 0.161 0.873787
## WeekF12 9.180 6.599 1.391 0.177502
## WeekF13 21.825 6.599 3.307 0.003076 **
## WeekF14 39.795 6.599 6.030 3.77e-06 ***
## WeekF15 63.605 6.599 9.639 1.52e-09 ***
## WeekF16 93.645 6.599 14.191 7.27e-13 ***
## WeekF17 125.285 6.599 18.985 1.50e-15 ***
## WeekF18 156.405 6.599 23.701 < 2e-16 ***
## WeekF19 186.325 6.599 28.235 < 2e-16 ***
## WeekF20 214.675 6.599 32.531 < 2e-16 ***
## WeekF21 240.495 6.599 36.444 < 2e-16 ***
## WeekF22 262.550 6.599 39.786 < 2e-16 ***
## WeekF23 280.895 6.599 42.566 < 2e-16 ***
## WeekF24 295.655 6.599 44.803 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.599 on 23 degrees of freedom
## Multiple R-squared: 0.998, Adjusted R-squared: 0.9959
## F-statistic: 476 on 24 and 23 DF, p-value: < 2.2e-16
anova(sqDminmodel1)
## Analysis of Variance Table
##
## Response: sqDmin
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 1715 1715.1 39.384 2.104e-06 ***
## WeekF 23 495758 21554.7 494.974 < 2.2e-16 ***
## Residuals 23 1002 43.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid1<-resid(sqDminmodel1)
plot(resid1)
#with Week
sqDminmodel2<-lm(sqDmin~Honeysuckle*Week,data=scatexperimentdata)
summary(sqDminmodel2)
##
## Call:
## lm(formula = sqDmin ~ Honeysuckle * Week, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.918 -51.650 6.636 47.228 63.475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -70.0976 21.5430 -3.254 0.00219 **
## HoneysuckleNH 1.2975 30.4664 0.043 0.96622
## Week 12.4497 1.5077 8.257 1.73e-10 ***
## HoneysuckleNH:Week 0.8526 2.1322 0.400 0.69119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 51.13 on 44 degrees of freedom
## Multiple R-squared: 0.7693, Adjusted R-squared: 0.7535
## F-statistic: 48.9 on 3 and 44 DF, p-value: 4.632e-14
anova(sqDminmodel2)
## Analysis of Variance Table
##
## Response: sqDmin
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 1715 1715 0.6561 0.4223
## Week 1 381321 381321 145.8700 1.452e-15 ***
## Honeysuckle:Week 1 418 418 0.1599 0.6912
## Residuals 44 115021 2614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid2<-resid(sqDminmodel2)
plot(resid2)
##sqDNitr
#with WeekF
sqDNitrmodel1<-lm(sqDNitr~Honeysuckle + WeekF,data=scatexperimentdata)
summary(sqDNitrmodel1)
##
## Call:
## lm(formula = sqDNitr ~ Honeysuckle + WeekF, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.576 -4.175 0.000 4.175 7.576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.956 4.748 -0.412 0.684182
## HoneysuckleNH 11.932 1.899 6.283 2.07e-06 ***
## WeekF2 6.555 6.579 0.996 0.329460
## WeekF3 16.040 6.579 2.438 0.022909 *
## WeekF4 28.355 6.579 4.310 0.000260 ***
## WeekF5 29.785 6.579 4.527 0.000151 ***
## WeekF6 21.710 6.579 3.300 0.003132 **
## WeekF7 10.660 6.579 1.620 0.118807
## WeekF8 1.220 6.579 0.185 0.854515
## WeekF9 -3.435 6.579 -0.522 0.606592
## WeekF10 -3.180 6.579 -0.483 0.633422
## WeekF11 1.255 6.579 0.191 0.850393
## WeekF12 9.440 6.579 1.435 0.164795
## WeekF13 22.080 6.579 3.356 0.002734 **
## WeekF14 40.025 6.579 6.084 3.32e-06 ***
## WeekF15 63.770 6.579 9.693 1.37e-09 ***
## WeekF16 93.740 6.579 14.248 6.69e-13 ***
## WeekF17 125.245 6.579 19.037 1.41e-15 ***
## WeekF18 156.200 6.579 23.742 < 2e-16 ***
## WeekF19 185.900 6.579 28.256 < 2e-16 ***
## WeekF20 214.010 6.579 32.528 < 2e-16 ***
## WeekF21 239.670 6.579 36.429 < 2e-16 ***
## WeekF22 261.625 6.579 39.766 < 2e-16 ***
## WeekF23 279.900 6.579 42.543 < 2e-16 ***
## WeekF24 294.665 6.579 44.787 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.579 on 23 degrees of freedom
## Multiple R-squared: 0.998, Adjusted R-squared: 0.9959
## F-statistic: 475.7 on 24 and 23 DF, p-value: < 2.2e-16
anova(sqDNitrmodel1)
## Analysis of Variance Table
##
## Response: sqDNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 1708 1708.5 39.47 2.07e-06 ***
## WeekF 23 492443 21410.6 494.63 < 2.2e-16 ***
## Residuals 23 996 43.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid3<-resid(sqDNitrmodel1)
plot(resid3)
#with Week
sqDNitrmodel2<-lm(sqDNitr~Honeysuckle*Week,data=scatexperimentdata)
summary(sqDNitrmodel2)
##
## Call:
## lm(formula = sqDNitr ~ Honeysuckle * Week, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.455 -51.590 6.723 47.037 63.057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -69.8693 21.4244 -3.261 0.00215 **
## HoneysuckleNH 1.3295 30.2987 0.044 0.96520
## Week 12.4172 1.4994 8.281 1.6e-10 ***
## HoneysuckleNH:Week 0.8482 2.1205 0.400 0.69109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 50.85 on 44 degrees of freedom
## Multiple R-squared: 0.7703, Adjusted R-squared: 0.7546
## F-statistic: 49.17 on 3 and 44 DF, p-value: 4.212e-14
anova(sqDNitrmodel2)
## Analysis of Variance Table
##
## Response: sqDNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 1708 1708 0.6608 0.4206
## Week 1 379267 379267 146.6948 1.32e-15 ***
## Honeysuckle:Week 1 414 414 0.1600 0.6911
## Residuals 44 113758 2585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid4<-resid(sqDNitrmodel2)
plot(resid4)
##Cmin
#with WeekF
Cminmodel1<-lm(Cmin~Honeysuckle+WeekF,data=scatexperimentdata)
summary(Cminmodel1)
##
## Call:
## lm(formula = Cmin ~ Honeysuckle + WeekF, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8810 -0.2052 0.0000 0.2052 0.8810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1410 0.3094 13.383 2.43e-12 ***
## HoneysuckleNH 1.5879 0.1238 12.829 5.76e-12 ***
## WeekF2 1.6700 0.4288 3.895 0.00073 ***
## WeekF3 3.8700 0.4288 9.026 5.09e-09 ***
## WeekF4 6.4050 0.4288 14.938 2.49e-13 ***
## WeekF5 8.0500 0.4288 18.775 1.91e-15 ***
## WeekF6 8.9750 0.4288 20.932 < 2e-16 ***
## WeekF7 9.4850 0.4288 22.122 < 2e-16 ***
## WeekF8 9.7450 0.4288 22.728 < 2e-16 ***
## WeekF9 10.3950 0.4288 24.244 < 2e-16 ***
## WeekF10 11.5000 0.4288 26.822 < 2e-16 ***
## WeekF11 12.9300 0.4288 30.157 < 2e-16 ***
## WeekF12 14.6000 0.4288 34.052 < 2e-16 ***
## WeekF13 16.1050 0.4288 37.562 < 2e-16 ***
## WeekF14 17.3650 0.4288 40.501 < 2e-16 ***
## WeekF15 18.4350 0.4288 42.996 < 2e-16 ***
## WeekF16 19.3500 0.4288 45.130 < 2e-16 ***
## WeekF17 20.1400 0.4288 46.973 < 2e-16 ***
## WeekF18 20.8300 0.4288 48.582 < 2e-16 ***
## WeekF19 21.4300 0.4288 49.981 < 2e-16 ***
## WeekF20 21.9550 0.4288 51.206 < 2e-16 ***
## WeekF21 22.4700 0.4288 52.407 < 2e-16 ***
## WeekF22 23.0000 0.4288 53.643 < 2e-16 ***
## WeekF23 23.5400 0.4288 54.903 < 2e-16 ***
## WeekF24 24.0900 0.4288 56.185 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4288 on 23 degrees of freedom
## Multiple R-squared: 0.9983, Adjusted R-squared: 0.9966
## F-statistic: 567.2 on 24 and 23 DF, p-value: < 2.2e-16
anova(Cminmodel1)
## Analysis of Variance Table
##
## Response: Cmin
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 30.26 30.258 164.59 5.758e-12 ***
## WeekF 23 2472.43 107.497 584.75 < 2.2e-16 ***
## Residuals 23 4.23 0.184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid5<-resid(Cminmodel1)
plot(resid5)
#with Week
Cminmodel2<-lm(Cmin~Honeysuckle*Week,data=scatexperimentdata)
summary(Cminmodel2)
##
## Call:
## lm(formula = Cmin ~ Honeysuckle * Week, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1517 -0.6976 0.3211 1.1095 1.4632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.35428 0.53457 10.016 6.36e-13 ***
## HoneysuckleNH 2.46917 0.75600 3.266 0.00212 **
## Week 1.05739 0.03741 28.263 < 2e-16 ***
## HoneysuckleNH:Week -0.07050 0.05291 -1.332 0.18956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.269 on 44 degrees of freedom
## Multiple R-squared: 0.9717, Adjusted R-squared: 0.9698
## F-statistic: 504.5 on 3 and 44 DF, p-value: < 2.2e-16
anova(Cminmodel2)
## Analysis of Variance Table
##
## Response: Cmin
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 30.26 30.26 18.7979 8.336e-05 ***
## Week 1 2402.98 2402.98 1492.8700 < 2.2e-16 ***
## Honeysuckle:Week 1 2.86 2.86 1.7755 0.1896
## Residuals 44 70.82 1.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid6<-resid(Cminmodel2)
plot(resid6)
#Trtmin with WeekF
Trtminmodel1<-lm(Trtmin~Honeysuckle+WeekF,data=scatexperimentdata)
summary(Trtminmodel1)
##
## Call:
## lm(formula = Trtmin ~ Honeysuckle + WeekF, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.545 -0.195 0.000 0.195 0.545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1500 0.2552 8.425 1.74e-08 ***
## HoneysuckleNH 1.8800 0.1021 18.418 2.89e-15 ***
## WeekF2 0.3550 0.3536 1.004 0.32583
## WeekF3 1.3050 0.3536 3.691 0.00121 **
## WeekF4 2.6100 0.3536 7.381 1.66e-07 ***
## WeekF5 4.0800 0.3536 11.539 4.81e-11 ***
## WeekF6 5.7300 0.3536 16.205 4.48e-14 ***
## WeekF7 7.4800 0.3536 21.154 < 2e-16 ***
## WeekF8 9.2900 0.3536 26.273 < 2e-16 ***
## WeekF9 11.5250 0.3536 32.594 < 2e-16 ***
## WeekF10 14.1250 0.3536 39.947 < 2e-16 ***
## WeekF11 16.9900 0.3536 48.050 < 2e-16 ***
## WeekF12 20.0600 0.3536 56.732 < 2e-16 ***
## WeekF13 23.0150 0.3536 65.089 < 2e-16 ***
## WeekF14 25.8150 0.3536 73.008 < 2e-16 ***
## WeekF15 28.4900 0.3536 80.573 < 2e-16 ***
## WeekF16 31.0600 0.3536 87.842 < 2e-16 ***
## WeekF17 33.3450 0.3536 94.304 < 2e-16 ***
## WeekF18 35.3350 0.3536 99.932 < 2e-16 ***
## WeekF19 37.0650 0.3536 104.825 < 2e-16 ***
## WeekF20 38.5800 0.3536 109.109 < 2e-16 ***
## WeekF21 39.9450 0.3536 112.970 < 2e-16 ***
## WeekF22 41.1700 0.3536 116.434 < 2e-16 ***
## WeekF23 42.2650 0.3536 119.531 < 2e-16 ***
## WeekF24 43.2450 0.3536 122.303 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3536 on 23 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9995
## F-statistic: 3617 on 24 and 23 DF, p-value: < 2.2e-16
anova(Trtminmodel1)
## Analysis of Variance Table
##
## Response: Trtmin
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 42.4 42.41 339.23 2.89e-15 ***
## WeekF 23 10811.5 470.07 3759.74 < 2.2e-16 ***
## Residuals 23 2.9 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid7<-resid(Trtminmodel1)
plot(resid7)
#with Week
Trtminmodel2<-lm(Trtmin~Honeysuckle*Week,data=scatexperimentdata)
summary(Trtminmodel2)
##
## Call:
## lm(formula = Trtmin ~ Honeysuckle * Week, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2493 -1.5946 0.0858 1.5408 3.4893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.24848 0.77663 -4.183 0.000135 ***
## HoneysuckleNH 1.58402 1.09831 1.442 0.156318
## Week 2.14148 0.05435 39.400 < 2e-16 ***
## HoneysuckleNH:Week 0.02368 0.07687 0.308 0.759501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.843 on 44 degrees of freedom
## Multiple R-squared: 0.9862, Adjusted R-squared: 0.9853
## F-statistic: 1051 on 3 and 44 DF, p-value: < 2.2e-16
anova(Trtminmodel2)
## Analysis of Variance Table
##
## Response: Trtmin
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 42.4 42.4 12.4842 0.0009782 ***
## Week 1 10664.6 10664.6 3139.1204 < 2.2e-16 ***
## Honeysuckle:Week 1 0.3 0.3 0.0949 0.7595008
## Residuals 44 149.5 3.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid8<-resid(Trtminmodel2)
plot(resid8)
#CNitr with weekF
CNitrmodel1<-lm(CNitr~Honeysuckle+WeekF,data=scatexperimentdata)
summary(CNitrmodel1)
##
## Call:
## lm(formula = CNitr ~ Honeysuckle + WeekF, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5075 -0.1750 0.0000 0.1750 0.5075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.48250 0.23873 10.399 3.64e-10 ***
## HoneysuckleNH 1.47500 0.09549 15.446 1.24e-13 ***
## WeekF2 1.90000 0.33080 5.744 7.52e-06 ***
## WeekF3 4.27000 0.33080 12.908 5.08e-12 ***
## WeekF4 6.85500 0.33080 20.723 2.23e-16 ***
## WeekF5 8.52500 0.33080 25.771 < 2e-16 ***
## WeekF6 9.46500 0.33080 28.613 < 2e-16 ***
## WeekF7 9.99000 0.33080 30.200 < 2e-16 ***
## WeekF8 10.26000 0.33080 31.016 < 2e-16 ***
## WeekF9 10.93000 0.33080 33.041 < 2e-16 ***
## WeekF10 12.04000 0.33080 36.397 < 2e-16 ***
## WeekF11 13.48500 0.33080 40.765 < 2e-16 ***
## WeekF12 15.18000 0.33080 45.889 < 2e-16 ***
## WeekF13 16.69000 0.33080 50.454 < 2e-16 ***
## WeekF14 17.94500 0.33080 54.248 < 2e-16 ***
## WeekF15 18.99500 0.33080 57.422 < 2e-16 ***
## WeekF16 19.88000 0.33080 60.097 < 2e-16 ***
## WeekF17 20.64500 0.33080 62.410 < 2e-16 ***
## WeekF18 21.31000 0.33080 64.420 < 2e-16 ***
## WeekF19 21.89000 0.33080 66.174 < 2e-16 ***
## WeekF20 22.39500 0.33080 67.700 < 2e-16 ***
## WeekF21 22.89500 0.33080 69.212 < 2e-16 ***
## WeekF22 23.41000 0.33080 70.769 < 2e-16 ***
## WeekF23 23.93500 0.33080 72.356 < 2e-16 ***
## WeekF24 24.47000 0.33080 73.973 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3308 on 23 degrees of freedom
## Multiple R-squared: 0.999, Adjusted R-squared: 0.998
## F-statistic: 964.4 on 24 and 23 DF, p-value: < 2.2e-16
anova(CNitrmodel1)
## Analysis of Variance Table
##
## Response: CNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 26.11 26.107 238.59 1.236e-13 ***
## WeekF 23 2506.59 108.982 995.94 < 2.2e-16 ***
## Residuals 23 2.52 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid9<-resid(CNitrmodel1)
plot(resid9)
#with Week
CNitrmodel2<-lm(CNitr~Honeysuckle*Week,data=scatexperimentdata)
summary(CNitrmodel2)
##
## Call:
## lm(formula = CNitr ~ Honeysuckle * Week, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1452 -0.7197 0.3105 1.1302 1.5646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.19065 0.57578 7.278 4.48e-09 ***
## HoneysuckleNH 2.16739 0.81428 2.662 0.0108 *
## Week 1.05455 0.04030 26.170 < 2e-16 ***
## HoneysuckleNH:Week -0.05539 0.05699 -0.972 0.3364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 44 degrees of freedom
## Multiple R-squared: 0.9676, Adjusted R-squared: 0.9654
## F-statistic: 437.9 on 3 and 44 DF, p-value: < 2.2e-16
anova(CNitrmodel2)
## Analysis of Variance Table
##
## Response: CNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 26.11 26.11 13.9808 0.0005301 ***
## Week 1 2425.18 2425.18 1298.7068 < 2.2e-16 ***
## Honeysuckle:Week 1 1.76 1.76 0.9448 0.3363717
## Residuals 44 82.16 1.87
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid10<-resid(CNitrmodel2)
plot(resid10)
#TrtNitr with WeekF
TrtNitrmodel1<-lm(TrtNitr~Honeysuckle+WeekF,data=scatexperimentdata)
summary(TrtNitrmodel1)
##
## Call:
## lm(formula = TrtNitr ~ Honeysuckle + WeekF, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6019 -0.2231 0.0000 0.2231 0.6019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4981 0.2987 1.668 0.108966
## HoneysuckleNH 1.7637 0.1195 14.761 3.20e-13 ***
## WeekF2 0.5900 0.4139 1.425 0.167468
## WeekF3 1.6950 0.4139 4.095 0.000444 ***
## WeekF4 3.0550 0.4139 7.381 1.66e-07 ***
## WeekF5 4.5600 0.4139 11.017 1.19e-10 ***
## WeekF6 6.2350 0.4139 15.063 2.09e-13 ***
## WeekF7 8.0050 0.4139 19.340 1.00e-15 ***
## WeekF8 9.8450 0.4139 23.785 < 2e-16 ***
## WeekF9 12.1000 0.4139 29.233 < 2e-16 ***
## WeekF10 14.7150 0.4139 35.551 < 2e-16 ***
## WeekF11 17.5950 0.4139 42.509 < 2e-16 ***
## WeekF12 20.6700 0.4139 49.938 < 2e-16 ***
## WeekF13 23.6200 0.4139 57.065 < 2e-16 ***
## WeekF14 26.4000 0.4139 63.781 < 2e-16 ***
## WeekF15 29.0550 0.4139 70.196 < 2e-16 ***
## WeekF16 31.5900 0.4139 76.320 < 2e-16 ***
## WeekF17 33.8450 0.4139 81.768 < 2e-16 ***
## WeekF18 35.8000 0.4139 86.491 < 2e-16 ***
## WeekF19 37.5000 0.4139 90.598 < 2e-16 ***
## WeekF20 38.9950 0.4139 94.210 < 2e-16 ***
## WeekF21 40.3400 0.4139 97.460 < 2e-16 ***
## WeekF22 41.5450 0.4139 100.371 < 2e-16 ***
## WeekF23 42.6250 0.4139 102.980 < 2e-16 ***
## WeekF24 43.5950 0.4139 105.324 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4139 on 23 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9993
## F-statistic: 2643 on 24 and 23 DF, p-value: < 2.2e-16
anova(TrtNitrmodel1)
## Analysis of Variance Table
##
## Response: TrtNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 37.3 37.33 217.89 3.201e-13 ***
## WeekF 23 10830.2 470.88 2748.45 < 2.2e-16 ***
## Residuals 23 3.9 0.17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid11<-resid(TrtNitrmodel1)
plot(resid11)
#with Week
TrtNitrmodel2<-lm(TrtNitr~Honeysuckle * Week,data=scatexperimentdata)
summary(TrtNitrmodel2)
##
## Call:
## lm(formula = TrtNitr ~ Honeysuckle * Week, data = scatexperimentdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4415 -1.5231 0.0285 1.6274 3.2885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.37496 0.75927 -5.762 7.54e-07 ***
## HoneysuckleNH 1.27641 1.07376 1.189 0.241
## Week 2.13643 0.05314 40.206 < 2e-16 ***
## HoneysuckleNH:Week 0.03899 0.07515 0.519 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.802 on 44 degrees of freedom
## Multiple R-squared: 0.9869, Adjusted R-squared: 0.986
## F-statistic: 1101 on 3 and 44 DF, p-value: < 2.2e-16
anova(TrtNitrmodel2)
## Analysis of Variance Table
##
## Response: TrtNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 37.3 37.3 11.4962 0.001482 **
## Week 1 10690.4 10690.4 3292.2540 < 2.2e-16 ***
## Honeysuckle:Week 1 0.9 0.9 0.2692 0.606498
## Residuals 44 142.9 3.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
resid12<-resid(TrtNitrmodel2)
plot(resid12)
######################################
##########Repeated Measures
#Cmin
repeated.split.plot.modela <- lmer(Cmin ~ Honeysuckle * Week +
(Week |Honeysuckle),
data = scatexperimentdata,
contrasts = list(Honeysuckle = contr.sum),
REML = TRUE)
# This gives you the correct type 3 Anova; interpret P-values from here!
# Must use the capital "A" Anova function from the "car" package
Anova(repeated.split.plot.modela, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: Cmin
## F Df Df.res Pr(>F)
## (Intercept) 47.2706 1 1818 8.471e-12 ***
## Honeysuckle 1.6596 1 1818 0.1978
## Week 0.9304 1 113271392 0.3347
## Honeysuckle:Week 0.0011 1 113271392 0.9735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#pairwise comparisons of deer*honeysuckle interaction
emmeans(repeated.split.plot.modela, pairwise ~ Honeysuckle*Week)
## $emmeans
## Honeysuckle Week emmean SE df lower.CL upper.CL
## H 12.5 18.6 18.8 1.21e+09 -18.2 55.4
## NH 12.5 20.2 18.8 1.21e+09 -16.6 56.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## H,12.5 - NH,12.5 -1.59 26.5 1.21e+09 -0.060 0.9523
##
## Degrees-of-freedom method: kenward-roger
###Trtmin
repeated.split.plot.modelb <- lmer(Trtmin ~ Honeysuckle * Week +
(Week |Honeysuckle),
data = scatexperimentdata,
contrasts = list(Honeysuckle = contr.sum),
REML = TRUE)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge: degenerate Hessian with 2 negative
## eigenvalues
# This gives you the correct type 3 Anova; interpret P-values from here!
# Must use the capital "A" Anova function from the "car" package
Anova(repeated.split.plot.modelb, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: Trtmin
## F Df Df.res Pr(>F)
## (Intercept) 3.1217 1 1808 0.07742 .
## Honeysuckle 0.3245 1 1808 0.56898
## Week 1.9592 1 112958433 0.16160
## Honeysuckle:Week 0.0001 1 112958433 0.99386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#pairwise comparisons of deer*honeysuckle interaction
emmeans(repeated.split.plot.modelb, pairwise ~ Honeysuckle*Week)
## $emmeans
## Honeysuckle Week emmean SE df lower.CL upper.CL
## H 12.5 23.5 27.2 1.21e+09 -29.9 76.9
## NH 12.5 25.4 27.2 1.21e+09 -28.0 78.8
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## H,12.5 - NH,12.5 -1.88 38.5 1.21e+09 -0.049 0.9611
##
## Degrees-of-freedom method: kenward-roger
#CNitr
repeated.split.plot.modelc <- lmer(CNitr ~ Honeysuckle * Week +
(Week |Honeysuckle),
data = scatexperimentdata,
contrasts = list(Honeysuckle = contr.sum),
REML = TRUE)
# This gives you the correct type 3 Anova; interpret P-values from here!
# Must use the capital "A" Anova function from the "car" package
Anova(repeated.split.plot.modelc, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: CNitr
## F Df Df.res Pr(>F)
## (Intercept) 26.0085 1 1832 3.751e-07 ***
## Honeysuckle 1.0980 1 1832 0.2948
## Week 0.5759 1 223777097 0.4479
## Honeysuckle:Week 0.0004 1 223777097 0.9837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#pairwise comparisons of deer*honeysuckle interaction
emmeans(repeated.split.plot.modelc, pairwise ~ Honeysuckle*Week)
## $emmeans
## Honeysuckle Week emmean SE df lower.CL upper.CL
## H 12.5 17.4 23.9 2.38e+09 -29.5 64.3
## NH 12.5 18.8 23.9 2.38e+09 -28.0 65.7
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## H,12.5 - NH,12.5 -1.48 33.8 2.38e+09 -0.044 0.9652
##
## Degrees-of-freedom method: kenward-roger
####TrtNitr
repeated.split.plot.modeld <- lmer(Cmin ~ Honeysuckle * Week +
(Week |Honeysuckle),
data = scatexperimentdata,
contrasts = list(Honeysuckle = contr.sum),
REML = TRUE)
# This gives you the correct type 3 Anova; interpret P-values from here!
# Must use the capital "A" Anova function from the "car" package
Anova(repeated.split.plot.modeld, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: Cmin
## F Df Df.res Pr(>F)
## (Intercept) 47.2706 1 1818 8.471e-12 ***
## Honeysuckle 1.6596 1 1818 0.1978
## Week 0.9304 1 113271392 0.3347
## Honeysuckle:Week 0.0011 1 113271392 0.9735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#pairwise comparisons of deer*honeysuckle interaction
emmeans(repeated.split.plot.modeld, pairwise ~ Honeysuckle*Week)
## $emmeans
## Honeysuckle Week emmean SE df lower.CL upper.CL
## H 12.5 18.6 18.8 1.21e+09 -18.2 55.4
## NH 12.5 20.2 18.8 1.21e+09 -16.6 56.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## H,12.5 - NH,12.5 -1.59 26.5 1.21e+09 -0.060 0.9523
##
## Degrees-of-freedom method: kenward-roger
################Sample Model##################
Sample<-lm(TrtNitr~Honeysuckle*WeekF,data=scatexperimentdata)
summary(Sample)
##
## Call:
## lm(formula = TrtNitr ~ Honeysuckle * WeekF, data = scatexperimentdata)
##
## Residuals:
## ALL 48 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.05 NA NA NA
## HoneysuckleNH 0.66 NA NA NA
## WeekF2 0.64 NA NA NA
## WeekF3 1.61 NA NA NA
## WeekF4 2.90 NA NA NA
## WeekF5 4.24 NA NA NA
## WeekF6 5.81 NA NA NA
## WeekF7 7.50 NA NA NA
## WeekF8 9.28 NA NA NA
## WeekF9 11.42 NA NA NA
## WeekF10 13.94 NA NA NA
## WeekF11 16.74 NA NA NA
## WeekF12 19.75 NA NA NA
## WeekF13 22.72 NA NA NA
## WeekF14 25.52 NA NA NA
## WeekF15 28.19 NA NA NA
## WeekF16 30.74 NA NA NA
## WeekF17 33.07 NA NA NA
## WeekF18 35.09 NA NA NA
## WeekF19 36.85 NA NA NA
## WeekF20 38.40 NA NA NA
## WeekF21 39.81 NA NA NA
## WeekF22 41.07 NA NA NA
## WeekF23 42.21 NA NA NA
## WeekF24 43.23 NA NA NA
## HoneysuckleNH:WeekF2 -0.10 NA NA NA
## HoneysuckleNH:WeekF3 0.17 NA NA NA
## HoneysuckleNH:WeekF4 0.31 NA NA NA
## HoneysuckleNH:WeekF5 0.64 NA NA NA
## HoneysuckleNH:WeekF6 0.85 NA NA NA
## HoneysuckleNH:WeekF7 1.01 NA NA NA
## HoneysuckleNH:WeekF8 1.13 NA NA NA
## HoneysuckleNH:WeekF9 1.36 NA NA NA
## HoneysuckleNH:WeekF10 1.55 NA NA NA
## HoneysuckleNH:WeekF11 1.71 NA NA NA
## HoneysuckleNH:WeekF12 1.84 NA NA NA
## HoneysuckleNH:WeekF13 1.80 NA NA NA
## HoneysuckleNH:WeekF14 1.76 NA NA NA
## HoneysuckleNH:WeekF15 1.73 NA NA NA
## HoneysuckleNH:WeekF16 1.70 NA NA NA
## HoneysuckleNH:WeekF17 1.55 NA NA NA
## HoneysuckleNH:WeekF18 1.42 NA NA NA
## HoneysuckleNH:WeekF19 1.30 NA NA NA
## HoneysuckleNH:WeekF20 1.19 NA NA NA
## HoneysuckleNH:WeekF21 1.06 NA NA NA
## HoneysuckleNH:WeekF22 0.95 NA NA NA
## HoneysuckleNH:WeekF23 0.83 NA NA NA
## HoneysuckleNH:WeekF24 0.73 NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 47 and 0 DF, p-value: NA
anova(Sample)
## Warning in anova.lm(Sample): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
##
## Response: TrtNitr
## Df Sum Sq Mean Sq F value Pr(>F)
## Honeysuckle 1 37.3 37.33
## WeekF 23 10830.2 470.88
## Honeysuckle:WeekF 23 3.9 0.17
## Residuals 0 0.0
sampleresid<-resid(Sample)
plot(sampleresid)
