title: "Scat soil models_RM" author: "PAUL OJO" date: "December 20, 2020" output: html_document
rm(list = ls())
#Load in libraries
library(readr)
library(lme4)## Loading required package: Matrixlibrary(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 lme4library(emmeans)
setwd("c:/users/Paul/Documents/Rwork")
scatted_soilsdata<- read.csv(file="scatted_soilsdata.csv")
scatted_soilsdata$ScatF<-as.factor(scatted_soilsdata$Scat)
scatted_soilsdata$TimeF<-as.factor(scatted_soilsdata$Time)
library(MANOVA.RM)
str(scatted_soilsdata)## 'data.frame': 96 obs. of 12 variables:
## $ Time : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Scat : chr "P" "P" "P" "P" ...
## $ Honeysuckle : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Subject : int 1 1 1 1 1 1 1 1 1 1 ...
## $ TotalN : num 2.19 2.11 3.99 5.87 7.3 ...
## $ TotalNO3 : num 1.05 1.66 3.49 5.32 6.76 ...
## $ Ncummulative : num 2.19 6.09 11.66 20.56 32.33 ...
## $ Nitratecummulative: num 1.05 3.37 7.98 15.8 26.45 ...
## $ Nmin : num 2.19 3.05 3.89 5.14 6.47 ...
## $ NNitr : num 1.05 1.69 2.66 3.95 5.29 ...
## $ ScatF : Factor w/ 2 levels "A","P": 2 2 2 2 2 2 2 2 2 2 ...
## $ TimeF : Factor w/ 24 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...model1 <- RM(Nmin ~ ScatF*TimeF, data=scatted_soilsdata,subject="Subject",no.subf = 2,iter=1,resampling="Perm",CPU=1,seed=1234)## Warning in RM(Nmin ~ ScatF * TimeF, data = scatted_soilsdata, subject =
## "Subject", : The covariance matrix is singular. The WTS provides no valid test
## statistic!summary(model1)## Call:
## Nmin ~ ScatF * TimeF
## A repeated measures analysis with 2 within-subject factor(s) ( ScatF,TimeF ) and 0 between-subject factor(s).
##
## Descriptive:
## ScatF TimeF n Means Lower 95 % CI Upper 95 % CI
## 1 A 1 2 4.935 -2.272 12.142
## 3 A 2 2 6.605 1.549 11.661
## 5 A 3 2 8.805 3.319 14.291
## 7 A 4 2 11.340 5.660 17.020
## 9 A 5 2 12.985 8.575 17.395
## 11 A 6 2 13.910 10.382 17.438
## 13 A 7 2 14.420 11.494 17.346
## 15 A 8 2 14.680 12.227 17.133
## 17 A 9 2 15.330 12.404 18.256
## 19 A 10 2 16.435 13.143 19.727
## 21 A 11 2 17.865 14.272 21.458
## 23 A 12 2 19.535 15.684 23.386
## 25 A 13 2 21.040 17.469 24.611
## 27 A 14 2 22.300 18.987 25.613
## 29 A 15 2 23.370 20.272 26.468
## 31 A 16 2 24.285 21.381 27.189
## 33 A 17 2 25.075 22.343 27.807
## 35 A 18 2 25.765 23.205 28.325
## 37 A 19 2 26.365 23.977 28.753
## 39 A 20 2 26.890 24.610 29.170
## 41 A 21 2 27.405 25.189 29.621
## 43 A 22 2 27.935 25.719 30.151
## 45 A 23 2 28.475 26.302 30.648
## 47 A 24 2 29.025 26.895 31.155
## 2 P 1 2 3.090 -0.782 6.962
## 4 P 2 2 3.445 1.745 5.145
## 6 P 3 2 4.395 2.222 6.568
## 8 P 4 2 5.700 3.291 8.109
## 10 P 5 2 7.170 4.158 10.182
## 12 P 6 2 8.820 5.378 12.262
## 14 P 7 2 10.570 6.827 14.313
## 16 P 8 2 12.380 8.422 16.338
## 18 P 9 2 14.615 10.162 19.068
## 20 P 10 2 17.215 12.331 22.099
## 22 P 11 2 20.080 14.874 25.286
## 24 P 12 2 23.150 17.686 28.614
## 26 P 13 2 26.105 20.705 31.505
## 28 P 14 2 28.905 23.591 34.219
## 30 P 15 2 31.580 26.331 36.829
## 32 P 16 2 34.150 28.944 39.356
## 34 P 17 2 36.435 31.551 41.319
## 36 P 18 2 38.425 33.843 43.007
## 38 P 19 2 40.155 35.831 44.479
## 40 P 20 2 41.670 37.582 45.758
## 42 P 21 2 43.035 39.227 46.843
## 44 P 22 2 44.260 40.732 47.788
## 46 P 23 2 45.355 42.063 48.647
## 48 P 24 2 46.335 43.259 49.411
##
## Wald-Type Statistic (WTS):
## Test statistic df p-value
## ScatF "1216.825" "1" "<0.001"
## TimeF "1138.409" "23" "<0.001"
## ScatF:TimeF "126.316" "23" "<0.001"
##
## ANOVA-Type Statistic (ATS):
## Test statistic df1 df2 p-value
## ScatF "1216.825" "1" "Inf" "<0.001"
## TimeF "5097.093" "1" "Inf" "<0.001"
## ScatF:TimeF "326.315" "1" "Inf" "<0.001"
##
## p-values resampling:
## Perm (WTS)
## ScatF "<0.001"
## TimeF "<0.001"
## ScatF:TimeF "<0.001"model2 <- RM(NNitr ~ ScatF*TimeF, data=scatted_soilsdata,subject="Subject",no.subf = 2,iter=1000,resampling="Perm",CPU=1,seed=1234)## Warning in RM(NNitr ~ ScatF * TimeF, data = scatted_soilsdata, subject =
## "Subject", : The covariance matrix is singular. The WTS provides no valid test
## statistic!summary(model2)## Call:
## NNitr ~ ScatF * TimeF
## A repeated measures analysis with 2 within-subject factor(s) ( ScatF,TimeF ) and 0 between-subject factor(s).
##
## Descriptive:
## ScatF TimeF n Means Lower 95 % CI Upper 95 % CI
## 1 A 1 2 3.220 -1.599 8.039
## 3 A 2 2 5.120 0.516 9.724
## 5 A 3 2 7.490 2.370 12.610
## 7 A 4 2 10.075 4.718 15.432
## 9 A 5 2 11.745 7.593 15.897
## 11 A 6 2 12.685 9.350 16.020
## 13 A 7 2 13.210 10.456 15.964
## 15 A 8 2 13.480 11.157 15.803
## 17 A 9 2 14.150 11.353 16.947
## 19 A 10 2 15.260 12.076 18.444
## 21 A 11 2 16.705 13.198 20.212
## 23 A 12 2 18.400 14.614 22.186
## 25 A 13 2 19.910 16.425 23.395
## 27 A 14 2 21.165 17.960 24.370
## 29 A 15 2 22.215 19.225 25.205
## 31 A 16 2 23.100 20.303 25.897
## 33 A 17 2 23.865 21.262 26.468
## 35 A 18 2 24.530 22.077 26.983
## 37 A 19 2 25.110 22.787 27.433
## 39 A 20 2 25.615 23.442 27.788
## 41 A 21 2 26.115 23.985 28.245
## 43 A 22 2 26.630 24.522 28.738
## 45 A 23 2 27.155 25.068 29.242
## 47 A 24 2 27.690 25.625 29.755
## 2 P 1 2 1.380 -0.040 2.800
## 4 P 2 2 1.970 0.765 3.175
## 6 P 3 2 3.075 1.289 4.861
## 8 P 4 2 4.435 2.348 6.522
## 10 P 5 2 5.940 3.143 8.737
## 12 P 6 2 7.615 4.366 10.864
## 14 P 7 2 9.385 5.792 12.978
## 16 P 8 2 11.225 7.374 15.076
## 18 P 9 2 13.480 9.134 17.826
## 20 P 10 2 16.095 11.341 20.849
## 22 P 11 2 18.975 13.876 24.074
## 24 P 12 2 22.050 16.672 27.428
## 26 P 13 2 25.000 19.708 30.292
## 28 P 14 2 27.780 22.574 32.986
## 30 P 15 2 30.435 25.293 35.577
## 32 P 16 2 32.970 27.893 38.047
## 34 P 17 2 35.225 30.471 39.979
## 36 P 18 2 37.180 32.705 41.655
## 38 P 19 2 38.880 34.663 43.097
## 40 P 20 2 40.375 36.395 44.355
## 42 P 21 2 41.720 38.020 45.420
## 44 P 22 2 42.925 39.461 46.389
## 46 P 23 2 44.005 40.800 47.210
## 48 P 24 2 44.975 41.985 47.965
##
## Wald-Type Statistic (WTS):
## Test statistic df p-value
## ScatF "1248.954" "1" "<0.001"
## TimeF "179.569" "23" "<0.001"
## ScatF:TimeF "124.499" "23" "<0.001"
##
## ANOVA-Type Statistic (ATS):
## Test statistic df1 df2 p-value
## ScatF "1248.954" "1" "Inf" "<0.001"
## TimeF "7268.208" "1" "Inf" "<0.001"
## ScatF:TimeF "322.809" "1" "Inf" "<0.001"
##
## p-values resampling:
## Perm (WTS)
## ScatF "0.013"
## TimeF "<0.001"
## ScatF:TimeF "<0.001"plot(model1, factor="ScatF", "main= Effect of Deer scat on NNitr")
plot(model1, factor="ScatF:TimeF", legendpos="topleft", col=c(2,4))
plot(model2, factor="ScatF", "main= Effect of Deer scat on NNitr")
plot(model2, factor="ScatF:TimeF", legendpos="topleft", col=c(2,4))
attach(scatted_soilsdata)
results1<-aov(Nmin~TimeF+Error(ScatF/TimeF))
summary(results1)##
## Error: ScatF
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 622.9 622.9
##
## Error: ScatF:TimeF
## Df Sum Sq Mean Sq F value Pr(>F)
## TimeF 23 11716 509.4 7.472 4.42e-06 ***
## Residuals 23 1568 68.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 48 79.77 1.662results2<-aov(NNitr~TimeF+Error(ScatF/TimeF))
summary(results2)##
## Error: ScatF
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 624.8 624.8
##
## Error: ScatF:TimeF
## Df Sum Sq Mean Sq F value Pr(>F)
## TimeF 23 11775 512.0 7.541 4.06e-06 ***
## Residuals 23 1561 67.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 48 69.89 1.456reslt1<-tapply(Nmin,TimeF, mean)
plot(reslt1, type ="o", xlab= "Time", ylab="Nmin")
reslt2<-tapply(NNitr,TimeF, mean)
plot(reslt2, type ="o", xlab= "Time", ylab="NNitr")
require(stats)
pairwise.t.test(Nmin, TimeF, p.adjust.method="bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: Nmin and TimeF
##
## 1 2 3 4 5 6 7 8 9
## 2 1.00000 - - - - - - - -
## 3 1.00000 1.00000 - - - - - - -
## 4 1.00000 1.00000 1.00000 - - - - - -
## 5 1.00000 1.00000 1.00000 1.00000 - - - - -
## 6 1.00000 1.00000 1.00000 1.00000 1.00000 - - - -
## 7 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 - - -
## 8 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 - -
## 9 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -
## 10 0.52063 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 11 0.09235 0.21286 0.72721 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 12 0.01156 0.02865 0.11080 0.51665 1.00000 1.00000 1.00000 1.00000 1.00000
## 13 0.00143 0.00375 0.01598 0.08542 0.30573 0.82276 1.00000 1.00000 1.00000
## 14 0.00019 0.00053 0.00244 0.01447 0.05712 0.16816 0.41447 0.90949 1.00000
## 15 2.9e-05 8.2e-05 0.00040 0.00255 0.01087 0.03439 0.09080 0.21329 0.65934
## 16 4.8e-06 1.4e-05 6.9e-05 0.00047 0.00214 0.00716 0.02000 0.04966 0.16713
## 17 9.4e-07 2.7e-06 1.4e-05 0.00010 0.00048 0.00168 0.00491 0.01272 0.04574
## 18 2.2e-07 6.6e-07 3.5e-06 2.6e-05 0.00013 0.00046 0.00138 0.00369 0.01396
## 19 6.4e-08 1.9e-07 1.0e-06 7.7e-06 3.9e-05 0.00014 0.00044 0.00121 0.00478
## 20 2.1e-08 6.4e-08 3.5e-07 2.7e-06 1.4e-05 5.1e-05 0.00016 0.00045 0.00182
## 21 7.8e-09 2.3e-08 1.3e-07 9.8e-07 5.1e-06 1.9e-05 6.2e-05 0.00018 0.00073
## 22 3.0e-09 9.0e-09 4.9e-08 3.9e-07 2.0e-06 7.8e-06 2.5e-05 7.3e-05 0.00031
## 23 1.2e-09 3.7e-09 2.0e-08 1.6e-07 8.5e-07 3.3e-06 1.1e-05 3.1e-05 0.00014
## 24 5.4e-10 1.6e-09 8.9e-09 7.1e-08 3.8e-07 1.5e-06 4.9e-06 1.4e-05 6.3e-05
## 10 11 12 13 14 15 16 17 18
## 2 - - - - - - - - -
## 3 - - - - - - - - -
## 4 - - - - - - - - -
## 5 - - - - - - - - -
## 6 - - - - - - - - -
## 7 - - - - - - - - -
## 8 - - - - - - - - -
## 9 - - - - - - - - -
## 10 - - - - - - - - -
## 11 1.00000 - - - - - - - -
## 12 1.00000 1.00000 - - - - - - -
## 13 1.00000 1.00000 1.00000 - - - - - -
## 14 1.00000 1.00000 1.00000 1.00000 - - - - -
## 15 1.00000 1.00000 1.00000 1.00000 1.00000 - - - -
## 16 0.71636 1.00000 1.00000 1.00000 1.00000 1.00000 - - -
## 17 0.21589 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 - -
## 18 0.07104 0.40966 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -
## 19 0.02582 0.16142 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 20 0.01032 0.06881 0.47286 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 21 0.00432 0.03047 0.22523 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 22 0.00188 0.01392 0.10965 0.64821 1.00000 1.00000 1.00000 1.00000 1.00000
## 23 0.00086 0.00659 0.05484 0.34484 1.00000 1.00000 1.00000 1.00000 1.00000
## 24 0.00040 0.00322 0.02815 0.18697 0.90614 1.00000 1.00000 1.00000 1.00000
## 19 20 21 22 23
## 2 - - - - -
## 3 - - - - -
## 4 - - - - -
## 5 - - - - -
## 6 - - - - -
## 7 - - - - -
## 8 - - - - -
## 9 - - - - -
## 10 - - - - -
## 11 - - - - -
## 12 - - - - -
## 13 - - - - -
## 14 - - - - -
## 15 - - - - -
## 16 - - - - -
## 17 - - - - -
## 18 - - - - -
## 19 - - - - -
## 20 1.00000 - - - -
## 21 1.00000 1.00000 - - -
## 22 1.00000 1.00000 1.00000 - -
## 23 1.00000 1.00000 1.00000 1.00000 -
## 24 1.00000 1.00000 1.00000 1.00000 1.00000
##
## P value adjustment method: bonferronipairwise.t.test(NNitr, TimeF, p.adjust.method="bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: NNitr and TimeF
##
## 1 2 3 4 5 6 7 8 9
## 2 1.00000 - - - - - - - -
## 3 1.00000 1.00000 - - - - - - -
## 4 1.00000 1.00000 1.00000 - - - - - -
## 5 1.00000 1.00000 1.00000 1.00000 - - - - -
## 6 1.00000 1.00000 1.00000 1.00000 1.00000 - - - -
## 7 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 - - -
## 8 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 - -
## 9 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -
## 10 0.32431 0.84517 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 11 0.05403 0.15451 0.61342 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 12 0.00635 0.01980 0.09001 0.44653 1.00000 1.00000 1.00000 1.00000 1.00000
## 13 0.00075 0.00251 0.01269 0.07234 0.26896 0.74269 1.00000 1.00000 1.00000
## 14 0.00010 0.00035 0.00192 0.01218 0.04998 0.15105 0.38046 0.85308 1.00000
## 15 1.5e-05 5.4e-05 0.00031 0.00216 0.00956 0.03103 0.08376 0.20118 0.63600
## 16 2.5e-06 9.3e-06 5.6e-05 0.00041 0.00192 0.00659 0.01881 0.04776 0.16434
## 17 5.0e-07 1.9e-06 1.2e-05 8.9e-05 0.00044 0.00158 0.00470 0.01246 0.04583
## 18 1.2e-07 4.7e-07 3.0e-06 2.3e-05 0.00012 0.00044 0.00135 0.00370 0.01431
## 19 3.6e-08 1.4e-07 8.8e-07 7.1e-06 3.7e-05 0.00014 0.00044 0.00124 0.00501
## 20 1.2e-08 4.7e-08 3.0e-07 2.5e-06 1.3e-05 5.1e-05 0.00016 0.00047 0.00194
## 21 4.5e-09 1.7e-08 1.1e-07 9.3e-07 5.0e-06 2.0e-05 6.4e-05 0.00019 0.00079
## 22 1.8e-09 6.8e-09 4.5e-08 3.7e-07 2.0e-06 8.0e-06 2.6e-05 7.8e-05 0.00034
## 23 7.3e-10 2.8e-09 1.9e-08 1.6e-07 8.6e-07 3.4e-06 1.1e-05 3.4e-05 0.00015
## 24 3.2e-10 1.3e-09 8.3e-09 7.0e-08 3.9e-07 1.6e-06 5.2e-06 1.6e-05 7.1e-05
## 10 11 12 13 14 15 16 17 18
## 2 - - - - - - - - -
## 3 - - - - - - - - -
## 4 - - - - - - - - -
## 5 - - - - - - - - -
## 6 - - - - - - - - -
## 7 - - - - - - - - -
## 8 - - - - - - - - -
## 9 - - - - - - - - -
## 10 - - - - - - - - -
## 11 1.00000 - - - - - - - -
## 12 1.00000 1.00000 - - - - - - -
## 13 1.00000 1.00000 1.00000 - - - - - -
## 14 1.00000 1.00000 1.00000 1.00000 - - - - -
## 15 1.00000 1.00000 1.00000 1.00000 1.00000 - - - -
## 16 0.71388 1.00000 1.00000 1.00000 1.00000 1.00000 - - -
## 17 0.21906 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 - -
## 18 0.07374 0.43116 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -
## 19 0.02736 0.17334 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 20 0.01112 0.07516 0.52126 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 21 0.00473 0.03382 0.25225 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## 22 0.00210 0.01570 0.12484 0.73297 1.00000 1.00000 1.00000 1.00000 1.00000
## 23 0.00097 0.00754 0.06335 0.39566 1.00000 1.00000 1.00000 1.00000 1.00000
## 24 0.00046 0.00373 0.03294 0.21729 1.00000 1.00000 1.00000 1.00000 1.00000
## 19 20 21 22 23
## 2 - - - - -
## 3 - - - - -
## 4 - - - - -
## 5 - - - - -
## 6 - - - - -
## 7 - - - - -
## 8 - - - - -
## 9 - - - - -
## 10 - - - - -
## 11 - - - - -
## 12 - - - - -
## 13 - - - - -
## 14 - - - - -
## 15 - - - - -
## 16 - - - - -
## 17 - - - - -
## 18 - - - - -
## 19 - - - - -
## 20 1.00000 - - - -
## 21 1.00000 1.00000 - - -
## 22 1.00000 1.00000 1.00000 - -
## 23 1.00000 1.00000 1.00000 1.00000 -
## 24 1.00000 1.00000 1.00000 1.00000 1.00000
##
## P value adjustment method: bonferroni```