data_new <- read.table("/media/julius/059A-28F0/Sungjin/M_BWT02_YM/data_new.txt", quote="\"", comment.char="")
data <- data.frame(data_new[,2:12])
colnames(data) <- c("ARN","SIRE", "DAM","SSIRE","SEX","year","BD","RBATCH","MPAR","TAGE","BWT")
# required packages
# install.packages(c("ggplot2", "ggpubr", "tidyverse", "broom", "AICcmodavg"))
# ANOVA is a statistical test for estimating how a quantitative dependent
# variable changes according to the levels of one or more categorical independent variables.
# ANOVA tests whether there is a difference in means of the groups at each level of the independent variable.
# The null hypothesis (H0) of the ANOVA is no difference in means,
# and the alternate hypothesis (Ha) is that the means are different from one another.
library(ggplot2)
library(ggpubr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble 3.0.6 ✓ dplyr 1.0.3
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ✓ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(broom)
library(AICcmodavg)
#
# The Df column displays the degrees of freedom for the independent variable ,(the number of levels in the variable minus 1), and the degrees of freedom for the residuals (the total number of observations minus one and minus the number of levels in the independent variables).
# The Sum Sq column displays the sum of squares (a.k.a. the total variation between the group means and the overall mean).
# The Mean Sq column is the mean of the sum of squares, calculated by dividing the sum of squares by the degrees of freedom for each parameter.
# The F-value column is the test statistic from the F test. This is the mean square of each independent variable divided by the mean square of the residuals. The larger the F value, the more likely it is that the variation caused by the independent variable is real and not due to chance.
# The Pr(>F) column is the p-value of the F-statistic. This shows how likely it is that the F-value calculated from the test would have occurred if the null hypothesis of no difference among group means were true.
data$SEX <-factor(data$SEX)
data$MPAR <-factor(data$MPAR)
data$TAGE <-factor(data$TAGE)
data$TAGE <-factor(data$RBATCH)
anova1 <- aov(BWT~SEX, data=data)
summary(anova1)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.277 197.5 <2e-16 ***
## Residuals 17003 2519.9 0.148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova2 <- aov(BWT~SEX+MPAR, data=data)
summary(anova2)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.28 219.9 <2e-16 ***
## MPAR 8 257.6 32.20 241.9 <2e-16 ***
## Residuals 16995 2262.2 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova3 <- aov(BWT~SEX+MPAR+BD, data=data)
summary(anova3)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.28 219.985 <2e-16 ***
## MPAR 8 257.6 32.20 241.973 <2e-16 ***
## BD 1 0.6 0.60 4.473 0.0344 *
## Residuals 16994 2261.7 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova4 <- aov(BWT~SEX+MPAR+RBATCH, data=data)
summary(anova4)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.28 220.305 <2e-16 ***
## MPAR 8 257.6 32.20 242.325 <2e-16 ***
## RBATCH 56 11.2 0.20 1.504 0.0088 **
## Residuals 16939 2251.1 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova5 <- aov(BWT~SEX+MPAR+RBATCH+TAGE, data=data)
summary(anova5)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.28 220.305 <2e-16 ***
## MPAR 8 257.6 32.20 242.325 <2e-16 ***
## RBATCH 56 11.2 0.20 1.504 0.0088 **
## Residuals 16939 2251.1 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#checking the interaction effect
anova6 <- aov(BWT~SEX*TAGE,data=data)
summary(anova6)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.277 198.927 < 2e-16 ***
## TAGE 56 25.7 0.459 3.121 4.76e-14 ***
## Residuals 16947 2494.1 0.147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova7 <- aov(BWT~SEX+MPAR*RBATCH, data=data)
summary(anova7)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.28 220.377 < 2e-16 ***
## MPAR 8 257.6 32.20 242.405 < 2e-16 ***
## RBATCH 56 11.2 0.20 1.505 0.00875 **
## MPAR:RBATCH 80 11.4 0.14 1.069 0.31535
## Residuals 16859 2239.7 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova8 <- aov(BWT~SEX+MPAR+RBATCH+SEX*TAGE, data=data)
summary(anova8)
## Df Sum Sq Mean Sq F value Pr(>F)
## SEX 1 29.3 29.28 220.305 <2e-16 ***
## MPAR 8 257.6 32.20 242.325 <2e-16 ***
## RBATCH 56 11.2 0.20 1.504 0.0088 **
## Residuals 16939 2251.1 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Testing the best-fit model
model.set <-list(anova1,anova2,anova3,anova4,anova5,anova6,anova7,anova8)
model.names <- c("anova1","anova2","anova3","anova4","anova5","anova6","anova7","anova8")
# best-fit model based on AIC
# The Akaike information criterion (AIC) is a good test for model fit.
# AIC calculates the information value of each model by balancing the variation explained against the number of parameters used.
# A lower number means more information explained!
aictab(model.set,modname=model.names)
## Warning in aictab.AICaov.lm(model.set, modname = model.names):
## Check model structure carefully as some models may be redundant
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## anova3 12 13976.05 0.00 0.77 0.77 -6976.01
## anova2 11 13978.52 2.47 0.23 1.00 -6978.25
## anova4 67 14006.68 30.63 0.00 1.00 -6936.07
## anova5 67 14006.68 30.63 0.00 1.00 -6936.07
## anova8 67 14006.68 30.63 0.00 1.00 -6936.07
## anova7 147 14082.64 106.59 0.00 1.00 -6893.03
## anova6 59 15734.40 1758.35 0.00 1.00 -7807.99
## anova1 3 15796.48 1820.43 0.00 1.00 -7895.24
# Plotting to test Homoscedasticity
plot(anova5)
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
# Post-hoc testing
# TukeyHSD test (Tukey's Honestly-Significant Difference)
# TukeHSD only accepts factors
tukey.anova5 <- (TukeyHSD(anova5))
tukey.anova5
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = BWT ~ SEX + MPAR + RBATCH + TAGE, data = data)
##
## $SEX
## diff lwr upr p adj
## 2-1 0.08303652 0.07207083 0.09400221 0
##
## $MPAR
## diff lwr upr p adj
## 1-0 -0.026103090 -0.052811183 0.0006050035 0.0614142
## 2-0 0.253082977 0.224946775 0.2812191789 0.0000000
## 3-0 0.277939127 0.244766775 0.3111114788 0.0000000
## 4-0 0.307590770 0.265228744 0.3499527958 0.0000000
## 5-0 0.258685039 0.203161750 0.3142083275 0.0000000
## 6-0 0.206586807 0.135403837 0.2777697777 0.0000000
## 7-0 0.147558136 0.060583750 0.2345325219 0.0000051
## 8-0 0.194849610 0.034517575 0.3551816439 0.0051507
## 2-1 0.279186066 0.243884188 0.3144879452 0.0000000
## 3-1 0.304042216 0.264608960 0.3434754729 0.0000000
## 4-1 0.333693859 0.286268992 0.3811187266 0.0000000
## 5-1 0.284788128 0.225311978 0.3442642779 0.0000000
## 6-1 0.232689897 0.158382481 0.3069973128 0.0000000
## 7-1 0.173661226 0.084111683 0.2632107682 0.0000001
## 8-1 0.220952699 0.059209266 0.3826961319 0.0007634
## 3-2 0.024856150 -0.015558023 0.0652703225 0.6082230
## 4-2 0.054507793 0.006264227 0.1027513589 0.0135704
## 5-2 0.005602062 -0.054528929 0.0657330523 0.9999986
## 6-2 -0.046496170 -0.121328754 0.0283364148 0.5945087
## 7-2 -0.105524841 -0.195510641 -0.0155390408 0.0084383
## 8-2 -0.058233367 -0.220218742 0.1037520076 0.9721359
## 4-3 0.029651643 -0.021692302 0.0809955885 0.6879873
## 5-3 -0.019254088 -0.081899879 0.0433917026 0.9897483
## 6-3 -0.071352320 -0.148220224 0.0055155852 0.0936834
## 7-3 -0.130380991 -0.222066340 -0.0386956414 0.0003533
## 8-3 -0.083089517 -0.246025151 0.0798461166 0.8149740
## 5-4 -0.048905731 -0.116866245 0.0190547824 0.3844655
## 6-4 -0.101003963 -0.182261646 -0.0197462793 0.0036947
## 7-4 -0.160032634 -0.255428334 -0.0646369337 0.0000070
## 8-4 -0.112741160 -0.277793143 0.0523108226 0.4601726
## 6-5 -0.052098231 -0.140930126 0.0367336628 0.6693169
## 7-5 -0.111126903 -0.213051551 -0.0092022543 0.0206411
## 8-5 -0.063835429 -0.232745024 0.1050741663 0.9623012
## 7-6 -0.059028671 -0.170261821 0.0522044787 0.7790718
## 8-6 -0.011737198 -0.186421573 0.1629471780 0.9999999
## 8-7 0.047291474 -0.134401013 0.2289839600 0.9966906
##
## $RBATCH
## diff lwr upr p adj
## T2017010-0 8.755099e-02 -0.6513708 0.8264728 1.0000000
## T2017011-0 1.482372e-02 -0.4308566 0.4605041 1.0000000
## T2017012-0 -3.244901e-02 -0.6933806 0.6284826 1.0000000
## T2017013-0 2.958843e-01 -0.1308349 0.7226036 0.8370169
## T2017014-0 2.057328e-01 -0.2399475 0.6514131 0.9999558
## T2017015-0 8.755099e-02 -0.4350084 0.6101104 1.0000000
## T2017016-0 3.040813e-02 -0.5282146 0.5890309 1.0000000
## T2017017-0 1.732202e-01 -0.2724601 0.6189005 0.9999998
## T2017018-0 -2.156353e-02 -0.7604853 0.7173583 1.0000000
## T2017019-0 -7.139955e-02 -0.5640881 0.4212890 1.0000000
## T2017020-0 1.266136e-01 -0.3408059 0.5940330 1.0000000
## T2017021-0 2.420650e-01 -0.3612984 0.8454284 0.9999995
## T2017022-0 -2.876836e-02 -0.5513278 0.4937911 1.0000000
## T2018001-0 2.178225e-01 -0.2278578 0.6635029 0.9997814
## T2018002-0 2.003877e-01 -0.4029757 0.8037511 1.0000000
## T2018004-0 1.737125e-01 -0.4872191 0.8346441 1.0000000
## T2018005-0 2.087104e-01 -0.3946530 0.8120738 1.0000000
## T2018006-0 3.872757e-01 -0.1713470 0.9458985 0.8373389
## T2018007-0 -2.686932e-02 -0.6302327 0.5764941 1.0000000
## T2018008-0 -6.551725e-02 -0.7264489 0.5954144 1.0000000
## T2018009-0 -4.432151e-02 -0.6029442 0.5143012 1.0000000
## T2018010-0 1.736068e-01 -0.3489526 0.6961662 1.0000000
## T2018011-0 2.189442e-01 -0.8259862 1.2638747 1.0000000
## T2018012-0 6.402574e-02 -0.7891820 0.9172335 1.0000000
## T2018013-0 4.937182e-02 -0.3963085 0.4950522 1.0000000
## T2018014-0 2.408312e-01 -0.2817282 0.7633906 0.9999578
## T2018015-0 1.048377e-01 -0.4537850 0.6634605 1.0000000
## T2018016-0 1.869145e-01 -0.4740171 0.8478461 1.0000000
## T2018017-0 1.403403e-01 -0.7128674 0.9935481 1.0000000
## T2018018-0 1.025062e-01 -0.3242131 0.5292254 1.0000000
## T2019001-0 -5.321597e-03 -0.5278810 0.5172378 1.0000000
## T2019002-0 1.455544e-01 -0.5933674 0.8844762 1.0000000
## T2019003-0 -1.300778e-01 -0.8689996 0.6088440 1.0000000
## T2019004-0 9.371250e-02 -0.5672191 0.7546441 1.0000000
## T2019006-0 2.984543e-01 -0.3624773 0.9593860 0.9999770
## T2019007-0 1.195916e-01 -0.6193302 0.8585133 1.0000000
## T2019008-0 4.792246e-01 -0.1817070 1.1401562 0.7445439
## T2019009-0 2.288672e-01 -0.2638213 0.7215558 0.9999469
## T2019010-0 2.423584e-01 -0.2503301 0.7350470 0.9997403
## T2019011-0 -1.067672e-01 -0.7101306 0.4965962 1.0000000
## T2019012-0 1.530955e-01 -0.4502679 0.7564588 1.0000000
## T2019013-0 1.676840e-01 -0.2997355 0.6351034 1.0000000
## T2019015-0 2.660514e-01 -0.3948802 0.9269830 0.9999994
## T2019016-0 2.710646e-01 -0.2875582 0.8296873 0.9998199
## T2019018-0 1.537125e-01 -0.5072191 0.8146441 1.0000000
## T2019020-0 1.383794e-02 -0.7250839 0.7527597 1.0000000
## T2019022-0 6.784615e-02 -0.6710756 0.8067679 1.0000000
## T2019023-0 -2.822253e-02 -0.6891541 0.6327091 1.0000000
## T2020001-0 1.529427e-01 -0.3397458 0.6456313 1.0000000
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## T2017011-T2017010 -7.272727e-02 -0.9354983 0.7900438 1.0000000
## T2017012-T2017010 -1.200000e-01 -1.1112484 0.8712484 1.0000000
## T2017013-T2017010 2.083333e-01 -0.6447975 1.0614642 1.0000000
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## T2017015-T2017010 -1.110223e-15 -0.9048819 0.9048819 1.0000000
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## T2017017-T2017010 8.566921e-02 -0.7771018 0.9484402 1.0000000
## T2017018-T2017010 -1.091145e-01 -1.1539821 0.9357531 1.0000000
## T2017019-T2017010 -1.589505e-01 -1.0469173 0.7290162 1.0000000
## T2017020-T2017010 3.906258e-02 -0.8351364 0.9132615 1.0000000
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## T2017022-T2017010 -1.163194e-01 -1.0212012 0.7885625 1.0000000
## T2018001-T2017010 1.302716e-01 -0.7324995 0.9930426 1.0000000
## T2018002-T2017010 1.128367e-01 -0.8409926 1.0666659 1.0000000
## T2018004-T2017010 8.616151e-02 -0.9050869 1.0774100 1.0000000
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## T2018007-T2017010 -1.144203e-01 -1.0682496 0.8394090 1.0000000
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## T2018009-T2017010 -1.318725e-01 -1.0580486 0.7943036 1.0000000
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## T2018012-T2017010 -2.352525e-02 -1.1521112 1.1050608 1.0000000
## T2018013-T2017010 -3.817917e-02 -0.9009502 0.8245919 1.0000000
## T2018014-T2017010 1.532802e-01 -0.7516017 1.0581621 1.0000000
## T2018015-T2017010 1.728674e-02 -0.9088894 0.9434629 1.0000000
## T2018016-T2017010 9.936349e-02 -0.8918850 1.0906119 1.0000000
## T2018017-T2017010 5.278935e-02 -1.0757967 1.1813753 1.0000000
## T2018018-T2017010 1.495516e-02 -0.8381757 0.8680860 1.0000000
## T2019001-T2017010 -9.287259e-02 -0.9977545 0.8120093 1.0000000
## T2019002-T2017010 5.800338e-02 -0.9868642 1.1028710 1.0000000
## T2019003-T2017010 -2.176288e-01 -1.2624964 0.8272388 1.0000000
## T2019004-T2017010 6.161510e-03 -0.9850869 0.9974100 1.0000000
## T2019006-T2017010 2.109034e-01 -0.7803451 1.2021518 1.0000000
## T2019007-T2017010 3.204056e-02 -1.0128270 1.0769082 1.0000000
## T2019008-T2017010 3.916736e-01 -0.5995748 1.3829221 0.9999997
## T2019009-T2017010 1.413162e-01 -0.7466505 1.0292830 1.0000000
## T2019010-T2017010 1.548074e-01 -0.7331593 1.0427741 1.0000000
## T2019011-T2017010 -1.943182e-01 -1.1481475 0.7595110 1.0000000
## T2019012-T2017010 6.554448e-02 -0.8882848 1.0193737 1.0000000
## T2019013-T2017010 8.013298e-02 -0.7940660 0.9543319 1.0000000
## T2019015-T2017010 1.785004e-01 -0.8127480 1.1697489 1.0000000
## T2019016-T2017010 1.835136e-01 -0.7426626 1.1096897 1.0000000
## T2019018-T2017010 6.616151e-02 -0.9250869 1.0574100 1.0000000
## T2019020-T2017010 -7.371305e-02 -1.1185807 0.9711546 1.0000000
## T2019022-T2017010 -1.970484e-02 -1.0645724 1.0251628 1.0000000
## T2019023-T2017010 -1.157735e-01 -1.1070220 0.8754749 1.0000000
## T2020001-T2017010 6.539173e-02 -0.8225750 0.9533584 1.0000000
## T2020002-T2017010 -1.000000e-01 -1.7520807 1.5520807 1.0000000
## T2020003-T2017010 6.719660e-02 -0.8589795 0.9933727 1.0000000
## T2020004-T2017010 -9.263635e-02 -0.9975182 0.8122455 1.0000000
## T2020005-T2017010 -7.956164e-02 -1.0057378 0.8466145 1.0000000
## T2020006-T2017010 1.196114e-02 -0.8760056 0.8999279 1.0000000
## T2020007-T2017010 3.843719e-02 -0.9153921 0.9922665 1.0000000
## T2020009-T2017010 -3.333622e-01 -1.9854430 1.3187185 1.0000000
## T2017012-T2017011 -4.727273e-02 -0.8442665 0.7497210 1.0000000
## T2017013-T2017011 2.810606e-01 -0.3357520 0.8978732 0.9999698
## T2017014-T2017011 1.909091e-01 -0.4391698 0.8209880 1.0000000
## T2017015-T2017011 7.272727e-02 -0.6138853 0.7593398 1.0000000
## T2017016-T2017011 1.558442e-02 -0.6988579 0.7300267 1.0000000
## T2017017-T2017011 1.583965e-01 -0.4716824 0.7884754 1.0000000
## T2017018-T2017011 -3.638724e-02 -0.8991583 0.8263838 1.0000000
## T2017019-T2017011 -8.622326e-02 -0.7503847 0.5779382 1.0000000
## T2017020-T2017011 1.117899e-01 -0.5338489 0.7574286 1.0000000
## T2017021-T2017011 2.272413e-01 -0.5227026 0.9771851 1.0000000
## T2017022-T2017011 -4.359208e-02 -0.7302046 0.6430205 1.0000000
## T2018001-T2017011 2.029988e-01 -0.4270800 0.8330777 1.0000000
## T2018002-T2017011 1.855640e-01 -0.5643799 0.9355078 1.0000000
## T2018004-T2017011 1.588888e-01 -0.6381050 0.9558825 1.0000000
## T2018005-T2017011 1.938867e-01 -0.5560572 0.9438305 1.0000000
## T2018006-T2017011 3.724520e-01 -0.3419903 1.0868943 0.9988753
## T2018007-T2017011 -4.169304e-02 -0.7916369 0.7082508 1.0000000
## T2018008-T2017011 -8.034097e-02 -0.8773347 0.7166528 1.0000000
## T2018009-T2017011 -5.914522e-02 -0.7735875 0.6552971 1.0000000
## T2018010-T2017011 1.587831e-01 -0.5278295 0.8453956 1.0000000
## T2018011-T2017011 2.041205e-01 -0.9317703 1.3400114 1.0000000
## T2018012-T2017011 4.920203e-02 -0.9132593 1.0116634 1.0000000
## T2018013-T2017011 3.454811e-02 -0.5955308 0.6646270 1.0000000
## T2018014-T2017011 2.260075e-01 -0.4606050 0.9126200 1.0000000
## T2018015-T2017011 9.001401e-02 -0.6244283 0.8044563 1.0000000
## T2018016-T2017011 1.720908e-01 -0.6249030 0.9690845 1.0000000
## T2018017-T2017011 1.255166e-01 -0.8369448 1.0879780 1.0000000
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## T2019001-T2017011 -2.014531e-02 -0.7067578 0.6664672 1.0000000
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## T2019012-T2017017 -2.012473e-02 -0.7700686 0.7298191 1.0000000
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## T2018007-T2017018 -5.305793e-03 -0.9591351 0.9485235 1.0000000
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## T2019023-T2017018 -6.659001e-03 -0.9979074 0.9845894 1.0000000
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## T2019023-T2018006 -4.154983e-01 -1.2807308 0.4497343 0.9998645
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## T2020004-T2018006 -3.923611e-01 -1.1571259 0.3724037 0.9992376
## T2020005-T2018006 -3.792864e-01 -1.1691320 0.4105593 0.9998646
## T2020006-T2018006 -2.877636e-01 -1.0324372 0.4569100 0.9999999
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## T2019007-T2018007 1.464609e-01 -0.8073684 1.1002901 1.0000000
## T2019008-T2018007 5.060939e-01 -0.3886772 1.4008651 0.9931713
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## T2019013-T2018007 1.945533e-01 -0.5685101 0.9576167 1.0000000
## T2019015-T2018007 2.929207e-01 -0.6018504 1.1876919 1.0000000
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## T2019018-T2018007 1.805818e-01 -0.7141893 1.0753530 1.0000000
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## T2019023-T2018007 -1.353208e-03 -0.8961244 0.8934179 1.0000000
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## T2020002-T2018007 1.442031e-02 -1.5816413 1.6104819 1.0000000
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## T2018013-T2018008 1.148891e-01 -0.6821047 0.9118828 1.0000000
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## T2018017-T2018008 2.058576e-01 -0.8732770 1.2849922 1.0000000
## T2018018-T2018008 1.680234e-01 -0.6185244 0.9545712 1.0000000
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## T2019003-T2018008 -6.456053e-02 -1.0558090 0.9266879 1.0000000
## T2019004-T2018008 1.592298e-01 -0.7753282 1.0937877 1.0000000
## T2019006-T2018008 3.639716e-01 -0.5705864 1.2985296 0.9999998
## T2019007-T2018008 1.851088e-01 -0.8061396 1.1763572 1.0000000
## T2019008-T2018008 5.447418e-01 -0.3898161 1.4792998 0.9876999
## T2019009-T2018008 2.943845e-01 -0.5298182 1.1185872 1.0000000
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## T2019015-T2018008 3.315687e-01 -0.6029893 1.2661267 1.0000000
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## T2019022-T2018008 1.333634e-01 -0.8578850 1.1246118 1.0000000
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## T2020006-T2018008 1.650294e-01 -0.6591733 0.9892321 1.0000000
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## T2020009-T2018008 -1.802940e-01 -1.7989959 1.4384079 1.0000000
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## T2018012-T2018009 1.083473e-01 -0.9113391 1.1280336 1.0000000
## T2018013-T2018009 9.369333e-02 -0.6207490 0.8081356 1.0000000
## T2018014-T2018009 2.851527e-01 -0.4796121 1.0499175 1.0000000
## T2018015-T2018009 1.491592e-01 -0.6406864 0.9390049 1.0000000
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## T2018017-T2018009 1.846618e-01 -0.8350245 1.2043482 1.0000000
## T2018018-T2018009 1.468277e-01 -0.5559427 0.8495980 1.0000000
## T2019001-T2018009 3.899991e-02 -0.7257649 0.8037647 1.0000000
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## T2019003-T2018009 -8.575628e-02 -1.0119324 0.8404199 1.0000000
## T2019004-T2018009 1.380340e-01 -0.7271986 1.0032666 1.0000000
## T2019006-T2018009 3.427759e-01 -0.5224567 1.2080084 0.9999997
## T2019007-T2018009 1.639131e-01 -0.7622631 1.0900892 1.0000000
## T2019008-T2018009 5.235461e-01 -0.3416865 1.3887787 0.9758906
## T2019009-T2018009 2.731887e-01 -0.4714849 1.0178624 1.0000000
## T2019010-T2018009 2.866799e-01 -0.4579937 1.0313536 0.9999999
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## T2019013-T2018009 2.120055e-01 -0.5161962 0.9402072 1.0000000
## T2019015-T2018009 3.103729e-01 -0.5548597 1.1756055 1.0000000
## T2019016-T2018009 3.153861e-01 -0.4744596 1.1052317 0.9999996
## T2019018-T2018009 1.980340e-01 -0.6671986 1.0632666 1.0000000
## T2019020-T2018009 5.815945e-02 -0.8680167 0.9843356 1.0000000
## T2019022-T2018009 1.121677e-01 -0.8140085 1.0383438 1.0000000
## T2019023-T2018009 1.609898e-02 -0.8491336 0.8813316 1.0000000
## T2020001-T2018009 1.972642e-01 -0.5474094 0.9419379 1.0000000
## T2020002-T2018009 3.187250e-02 -1.5478188 1.6115638 1.0000000
## T2020003-T2018009 1.990691e-01 -0.5907766 0.9889148 1.0000000
## T2020004-T2018009 3.923614e-02 -0.7255286 0.8040009 1.0000000
## T2020005-T2018009 5.231085e-02 -0.7375348 0.8421565 1.0000000
## T2020006-T2018009 1.438336e-01 -0.6008400 0.8885073 1.0000000
## T2020007-T2018009 1.703097e-01 -0.6517877 0.9924071 1.0000000
## T2020009-T2018009 -2.014897e-01 -1.7811811 1.3782016 1.0000000
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## T2018012-T2018010 -1.095810e-01 -1.1099656 0.8908035 1.0000000
## T2018013-T2018010 -1.242349e-01 -0.8108475 0.5623776 1.0000000
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## T2018015-T2018010 -6.876904e-02 -0.8335338 0.6959957 1.0000000
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## T2018017-T2018010 -3.326643e-02 -1.0336510 0.9671181 1.0000000
## T2018018-T2018010 -7.110062e-02 -0.7455598 0.6033585 1.0000000
## T2019001-T2018010 -1.789284e-01 -0.9177613 0.5599046 1.0000000
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## T2019007-T2018010 -5.401522e-02 -0.9588971 0.8508667 1.0000000
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## T2019009-T2018010 5.526046e-02 -0.6627561 0.7732770 1.0000000
## T2019010-T2018010 6.875165e-02 -0.6492649 0.7867682 1.0000000
## T2019011-T2018010 -2.803740e-01 -1.0784048 0.5176568 1.0000000
## T2019012-T2018010 -2.051131e-02 -0.8185421 0.7775195 1.0000000
## T2019013-T2018010 -5.922801e-03 -0.7068413 0.6949957 1.0000000
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## T2019016-T2018010 9.745779e-02 -0.6673070 0.8622226 1.0000000
## T2019018-T2018010 -1.989427e-02 -0.8622935 0.8225049 1.0000000
## T2019020-T2018010 -1.597688e-01 -1.0646507 0.7451131 1.0000000
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## T2020002-T2018010 -1.860558e-01 -1.7533572 1.3812456 1.0000000
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## T2020007-T2018010 -4.761859e-02 -0.8456494 0.7504122 1.0000000
## T2020009-T2018010 -4.194180e-01 -1.9867194 1.1478834 1.0000000
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## T2018013-T2018011 -1.695724e-01 -1.3054633 0.9663184 1.0000000
## T2018014-T2018011 2.188696e-02 -1.1463105 1.1900845 1.0000000
## T2018015-T2018011 -1.141065e-01 -1.2988750 1.0706620 1.0000000
## T2018016-T2018011 -3.202976e-02 -1.2683338 1.2042743 1.0000000
## T2018017-T2018011 -7.860391e-02 -1.4275222 1.2703144 1.0000000
## T2018018-T2018011 -1.164381e-01 -1.2450241 1.0121479 1.0000000
## T2019001-T2018011 -2.242658e-01 -1.3924633 0.9439317 1.0000000
## T2019002-T2018011 -7.338988e-02 -1.3530861 1.2063064 1.0000000
## T2019003-T2018011 -3.490220e-01 -1.6287183 0.9306742 1.0000000
## T2019004-T2018011 -1.252317e-01 -1.3615358 1.1110723 1.0000000
## T2019006-T2018011 7.951011e-02 -1.1567939 1.3158141 1.0000000
## T2019007-T2018011 -9.935269e-02 -1.3790489 1.1803435 1.0000000
## T2019008-T2018011 2.602804e-01 -0.9760237 1.4965844 1.0000000
## T2019009-T2018011 9.922990e-03 -1.1452216 1.1650676 1.0000000
## T2019010-T2018011 2.341417e-02 -1.1317304 1.1785588 1.0000000
## T2019011-T2018011 -3.257115e-01 -1.5322206 0.8807977 1.0000000
## T2019012-T2018011 -6.584878e-02 -1.2723580 1.1406604 1.0000000
## T2019013-T2018011 -5.126027e-02 -1.1958554 1.0933348 1.0000000
## T2019015-T2018011 4.710717e-02 -1.1891969 1.2834112 1.0000000
## T2019016-T2018011 5.212032e-02 -1.1326482 1.2368888 1.0000000
## T2019018-T2018011 -6.523174e-02 -1.3015358 1.1710723 1.0000000
## T2019020-T2018011 -2.051063e-01 -1.4848025 1.0745899 1.0000000
## T2019022-T2018011 -1.510981e-01 -1.4307943 1.1285981 1.0000000
## T2019023-T2018011 -2.471668e-01 -1.4834708 0.9891373 1.0000000
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## T2020002-T2018011 -2.313933e-01 -2.0411570 1.5783705 1.0000000
## T2020003-T2018011 -6.419666e-02 -1.2489652 1.1205718 1.0000000
## T2020004-T2018011 -2.240296e-01 -1.3922271 0.9441679 1.0000000
## T2020005-T2018011 -2.109549e-01 -1.3957234 0.9738136 1.0000000
## T2020006-T2018011 -1.194321e-01 -1.2745767 1.0357125 1.0000000
## T2020007-T2018011 -9.295606e-02 -1.2994652 1.1135531 1.0000000
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## T2018013-T2018012 -1.465392e-02 -0.9771153 0.9478075 1.0000000
## T2018014-T2018012 1.768055e-01 -0.8235791 1.1771900 1.0000000
## T2018015-T2018012 4.081199e-02 -0.9788744 1.0604984 1.0000000
## T2018016-T2018012 1.228887e-01 -0.9562459 1.2020234 1.0000000
## T2018017-T2018012 7.631459e-02 -1.1301946 1.2828238 1.0000000
## T2018018-T2018012 3.848041e-02 -0.9153489 0.9923097 1.0000000
## T2019001-T2018012 -6.934734e-02 -1.0697319 0.9310372 1.0000000
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## T2019003-T2018012 -1.941035e-01 -1.3226895 0.9344825 1.0000000
## T2019004-T2018012 2.968676e-02 -1.0494479 1.1088214 1.0000000
## T2019006-T2018012 2.344286e-01 -0.8447060 1.3135632 1.0000000
## T2019007-T2018012 5.556581e-02 -1.0730202 1.1841518 1.0000000
## T2019008-T2018012 4.151989e-01 -0.6639358 1.4943335 0.9999999
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## T2019015-T2018012 2.020257e-01 -0.8771090 1.2811603 1.0000000
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## T2019011-T2018014 -3.475984e-01 -1.1456292 0.4504324 0.9999924
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## T2019022-T2019006 -2.306082e-01 -1.2218566 0.7606402 1.0000000
## T2019023-T2019006 -3.266769e-01 -1.2612349 0.6078811 1.0000000
## T2020001-T2019006 -1.455116e-01 -0.9697143 0.6786911 1.0000000
## T2020002-T2019006 -3.109034e-01 -1.9296053 1.3077986 1.0000000
## T2020003-T2019006 -1.437068e-01 -1.0089393 0.7215258 1.0000000
## T2020004-T2019006 -3.035397e-01 -1.1459389 0.5388595 1.0000000
## T2020005-T2019006 -2.904650e-01 -1.1556976 0.5747676 1.0000000
## T2020006-T2019006 -1.989422e-01 -1.0231449 0.6252605 1.0000000
## T2020007-T2019006 -1.724662e-01 -1.0672373 0.7223050 1.0000000
## T2020009-T2019006 -5.442656e-01 -2.1629675 1.0744363 1.0000000
## T2019008-T2019007 3.596330e-01 -0.6316154 1.3508815 1.0000000
## T2019009-T2019007 1.092757e-01 -0.7786910 0.9972424 1.0000000
## T2019010-T2019007 1.227669e-01 -0.7651999 1.0107336 1.0000000
## T2019011-T2019007 -2.263588e-01 -1.1801880 0.7274705 1.0000000
## T2019012-T2019007 3.350391e-02 -0.9203253 0.9873332 1.0000000
## T2019013-T2019007 4.809242e-02 -0.8261065 0.9222914 1.0000000
## T2019015-T2019007 1.464599e-01 -0.8447886 1.1377083 1.0000000
## T2019016-T2019007 1.514730e-01 -0.7747031 1.0776491 1.0000000
## T2019018-T2019007 3.412095e-02 -0.9571275 1.0253694 1.0000000
## T2019020-T2019007 -1.057536e-01 -1.1506212 0.9391140 1.0000000
## T2019022-T2019007 -5.174540e-02 -1.0966130 0.9931222 1.0000000
## T2019023-T2019007 -1.478141e-01 -1.1390625 0.8434344 1.0000000
## T2020001-T2019007 3.335117e-02 -0.8546155 0.9213179 1.0000000
## T2020002-T2019007 -1.320406e-01 -1.7841213 1.5200402 1.0000000
## T2020003-T2019007 3.515603e-02 -0.8910201 0.9613322 1.0000000
## T2020004-T2019007 -1.246769e-01 -1.0295588 0.7802050 1.0000000
## T2020005-T2019007 -1.116022e-01 -1.0377783 0.8145739 1.0000000
## T2020006-T2019007 -2.007942e-02 -0.9080461 0.8678873 1.0000000
## T2020007-T2019007 6.396631e-03 -0.9474326 0.9602259 1.0000000
## T2020009-T2019007 -3.654028e-01 -2.0174835 1.2866779 1.0000000
## T2019009-T2019008 -2.503574e-01 -1.0745600 0.5738453 1.0000000
## T2019010-T2019008 -2.368662e-01 -1.0610689 0.5873365 1.0000000
## T2019011-T2019008 -5.859918e-01 -1.4807630 0.3087793 0.9190152
## T2019012-T2019008 -3.261291e-01 -1.2209003 0.5686420 1.0000000
## T2019013-T2019008 -3.115406e-01 -1.1208916 0.4978103 0.9999999
## T2019015-T2019008 -2.131732e-01 -1.1477312 0.7213848 1.0000000
## T2019016-T2019008 -2.081600e-01 -1.0733926 0.6570725 1.0000000
## T2019018-T2019008 -3.255121e-01 -1.2600701 0.6090459 1.0000000
## T2019020-T2019008 -4.653867e-01 -1.4566351 0.5258618 0.9999280
## T2019022-T2019008 -4.113784e-01 -1.4026269 0.5798700 0.9999984
## T2019023-T2019008 -5.074471e-01 -1.4420051 0.4271109 0.9971260
## T2020001-T2019008 -3.262819e-01 -1.1504846 0.4979208 0.9999997
## T2020002-T2019008 -4.916736e-01 -2.1103755 1.1270283 1.0000000
## T2020003-T2019008 -3.244770e-01 -1.1897096 0.5407556 1.0000000
## T2020004-T2019008 -4.843100e-01 -1.3267092 0.3580892 0.9905549
## T2020005-T2019008 -4.712353e-01 -1.3364678 0.3939973 0.9969266
## T2020006-T2019008 -3.797125e-01 -1.2039151 0.4444902 0.9999583
## T2020007-T2019008 -3.532364e-01 -1.2480076 0.5415347 0.9999997
## T2020009-T2019008 -7.250359e-01 -2.3437378 0.8936661 0.9999820
## T2019010-T2019009 1.349118e-02 -0.6830872 0.7100696 1.0000000
## T2019011-T2019009 -3.356345e-01 -1.1144328 0.4431639 0.9999946
## T2019012-T2019009 -7.577177e-02 -0.8545701 0.7030266 1.0000000
## T2019013-T2019009 -6.118326e-02 -0.7401239 0.6177574 1.0000000
## T2019015-T2019009 3.718418e-02 -0.7870185 0.8613869 1.0000000
## T2019016-T2019009 4.219733e-02 -0.7024763 0.7868710 1.0000000
## T2019018-T2019009 -7.515473e-02 -0.8993574 0.7490479 1.0000000
## T2019020-T2019009 -2.150293e-01 -1.1029960 0.6729374 1.0000000
## T2019022-T2019009 -1.610211e-01 -1.0489878 0.7269456 1.0000000
## T2019023-T2019009 -2.570898e-01 -1.0812924 0.5671129 1.0000000
## T2020001-T2019009 -7.592451e-02 -0.7725029 0.6206539 1.0000000
## T2020002-T2019009 -2.413162e-01 -1.7989129 1.3162804 1.0000000
## T2020003-T2019009 -7.411965e-02 -0.8187933 0.6705540 1.0000000
## T2020004-T2019009 -2.339526e-01 -0.9519692 0.4840640 1.0000000
## T2020005-T2019009 -2.208779e-01 -0.9655515 0.5237957 1.0000000
## T2020006-T2019009 -1.293551e-01 -0.8259335 0.5672233 1.0000000
## T2020007-T2019009 -1.028790e-01 -0.8816774 0.6759193 1.0000000
## T2020009-T2019009 -4.746785e-01 -2.0322751 1.0829182 1.0000000
## T2019011-T2019010 -3.491256e-01 -1.1279240 0.4296727 0.9999815
## T2019012-T2019010 -8.926295e-02 -0.8680613 0.6895354 1.0000000
## T2019013-T2019010 -7.467445e-02 -0.7536151 0.6042662 1.0000000
## T2019015-T2019010 2.369299e-02 -0.8005097 0.8478957 1.0000000
## T2019016-T2019010 2.870614e-02 -0.7159675 0.7733798 1.0000000
## T2019018-T2019010 -8.864592e-02 -0.9128486 0.7355568 1.0000000
## T2019020-T2019010 -2.285205e-01 -1.1164872 0.6594462 1.0000000
## T2019022-T2019010 -1.745123e-01 -1.0624790 0.7134544 1.0000000
## T2019023-T2019010 -2.705809e-01 -1.0947836 0.5536217 1.0000000
## T2020001-T2019010 -8.941569e-02 -0.7859941 0.6071627 1.0000000
## T2020002-T2019010 -2.548074e-01 -1.8124041 1.3027892 1.0000000
## T2020003-T2019010 -8.761083e-02 -0.8322845 0.6570628 1.0000000
## T2020004-T2019010 -2.474438e-01 -0.9654604 0.4705728 1.0000000
## T2020005-T2019010 -2.343691e-01 -0.9790427 0.5103046 1.0000000
## T2020006-T2019010 -1.428463e-01 -0.8394247 0.5537321 1.0000000
## T2020007-T2019010 -1.163702e-01 -0.8951686 0.6624281 1.0000000
## T2020009-T2019010 -4.881697e-01 -2.0457663 1.0694270 1.0000000
## T2019012-T2019011 2.598627e-01 -0.5932681 1.1129935 1.0000000
## T2019013-T2019011 2.744512e-01 -0.4886122 1.0375146 1.0000000
## T2019015-T2019011 3.728186e-01 -0.5219525 1.2675898 0.9999982
## T2019016-T2019011 3.778318e-01 -0.4442657 1.1999292 0.9999611
## T2019018-T2019011 2.604797e-01 -0.6342914 1.1552509 1.0000000
## T2019020-T2019011 1.206052e-01 -0.8332241 1.0744344 1.0000000
## T2019022-T2019011 1.746134e-01 -0.7792159 1.1284426 1.0000000
## T2019023-T2019011 7.854469e-02 -0.8162265 0.9733159 1.0000000
## T2020001-T2019011 2.597099e-01 -0.5190884 1.0385083 1.0000000
## T2020002-T2019011 9.431821e-02 -1.5017434 1.6903798 1.0000000
## T2020003-T2019011 2.615148e-01 -0.5605826 1.0836122 1.0000000
## T2020004-T2019011 1.016819e-01 -0.6963490 0.8997127 1.0000000
## T2020005-T2019011 1.147566e-01 -0.7073409 0.9368540 1.0000000
## T2020006-T2019011 2.062794e-01 -0.5725190 0.9850777 1.0000000
## T2020007-T2019011 2.327554e-01 -0.6203754 1.0858862 1.0000000
## T2020009-T2019011 -1.390440e-01 -1.7351057 1.4570176 1.0000000
## T2019013-T2019012 1.458850e-02 -0.7484749 0.7776519 1.0000000
## T2019015-T2019012 1.129559e-01 -0.7818152 1.0077271 1.0000000
## T2019016-T2019012 1.179691e-01 -0.7041283 0.9400665 1.0000000
## T2019018-T2019012 6.170340e-04 -0.8941541 0.8953882 1.0000000
## T2019020-T2019012 -1.392575e-01 -1.0930868 0.8145717 1.0000000
## T2019022-T2019012 -8.524931e-02 -1.0390786 0.8685799 1.0000000
## T2019023-T2019012 -1.813180e-01 -1.0760892 0.7134532 1.0000000
## T2020001-T2019012 -1.527424e-04 -0.7789511 0.7786456 1.0000000
## T2020002-T2019012 -1.655445e-01 -1.7616061 1.4305172 1.0000000
## T2020003-T2019012 1.652120e-03 -0.8204453 0.8237496 1.0000000
## T2020004-T2019012 -1.581808e-01 -0.9562116 0.6398500 1.0000000
## T2020005-T2019012 -1.451061e-01 -0.9672036 0.6769913 1.0000000
## T2020006-T2019012 -5.358333e-02 -0.8323817 0.7252150 1.0000000
## T2020007-T2019012 -2.710728e-02 -0.8802381 0.8260235 1.0000000
## T2020009-T2019012 -3.989067e-01 -1.9949683 1.1971549 1.0000000
## T2019015-T2019013 9.836744e-02 -0.7109835 0.9077184 1.0000000
## T2019016-T2019013 1.033806e-01 -0.6248211 0.8315823 1.0000000
## T2019018-T2019013 -1.397147e-02 -0.8233224 0.7953795 1.0000000
## T2019020-T2019013 -1.538460e-01 -1.0280450 0.7203529 1.0000000
## T2019022-T2019013 -9.983782e-02 -0.9740368 0.7743611 1.0000000
## T2019023-T2019013 -1.959065e-01 -1.0052575 0.6134445 1.0000000
## T2020001-T2019013 -1.474125e-02 -0.6936819 0.6641994 1.0000000
## T2020002-T2019013 -1.801330e-01 -1.7299221 1.3696561 1.0000000
## T2020003-T2019013 -1.293639e-02 -0.7411381 0.7152653 1.0000000
## T2020004-T2019013 -1.727693e-01 -0.8736878 0.5281492 1.0000000
## T2020005-T2019013 -1.596946e-01 -0.8878963 0.5685071 1.0000000
## T2020006-T2019013 -6.817184e-02 -0.7471125 0.6107688 1.0000000
## T2020007-T2019013 -4.169579e-02 -0.8047592 0.7213676 1.0000000
## T2020009-T2019013 -4.134952e-01 -1.9632843 1.1362939 1.0000000
## T2019016-T2019015 5.013154e-03 -0.8602194 0.8702457 1.0000000
## T2019018-T2019015 -1.123389e-01 -1.0468969 0.8222191 1.0000000
## T2019020-T2019015 -2.522135e-01 -1.2434619 0.7390350 1.0000000
## T2019022-T2019015 -1.982053e-01 -1.1894537 0.7930432 1.0000000
## T2019023-T2019015 -2.942739e-01 -1.2288319 0.6402841 1.0000000
## T2020001-T2019015 -1.131087e-01 -0.9373114 0.7110940 1.0000000
## T2020002-T2019015 -2.785004e-01 -1.8972023 1.3402015 1.0000000
## T2020003-T2019015 -1.113038e-01 -0.9765364 0.7539288 1.0000000
## T2020004-T2019015 -2.711368e-01 -1.1135360 0.5712624 1.0000000
## T2020005-T2019015 -2.580621e-01 -1.1232946 0.6071705 1.0000000
## T2020006-T2019015 -1.665393e-01 -0.9907420 0.6576634 1.0000000
## T2020007-T2019015 -1.400632e-01 -1.0348344 0.7547079 1.0000000
## T2020009-T2019015 -5.118627e-01 -2.1305646 1.1068393 1.0000000
## T2019018-T2019016 -1.173521e-01 -0.9825846 0.7478805 1.0000000
## T2019020-T2019016 -2.572266e-01 -1.1834028 0.6689495 1.0000000
## T2019022-T2019016 -2.032184e-01 -1.1293946 0.7229577 1.0000000
## T2019023-T2019016 -2.992871e-01 -1.1645197 0.5659455 1.0000000
## T2020001-T2019016 -1.181218e-01 -0.8627955 0.6265518 1.0000000
## T2020002-T2019016 -2.835136e-01 -1.8632049 1.2961778 1.0000000
## T2020003-T2019016 -1.163170e-01 -0.9061626 0.6735287 1.0000000
## T2020004-T2019016 -2.761499e-01 -1.0409147 0.4886148 1.0000000
## T2020005-T2019016 -2.630752e-01 -1.0529209 0.5267704 1.0000000
## T2020006-T2019016 -1.715524e-01 -0.9162261 0.5731212 1.0000000
## T2020007-T2019016 -1.450764e-01 -0.9671738 0.6770211 1.0000000
## T2020009-T2019016 -5.168758e-01 -2.0965671 1.0628155 1.0000000
## T2019020-T2019018 -1.398746e-01 -1.1311230 0.8513739 1.0000000
## T2019022-T2019018 -8.586635e-02 -1.0771148 0.9053821 1.0000000
## T2019023-T2019018 -1.819350e-01 -1.1164930 0.7526230 1.0000000
## T2020001-T2019018 -7.697764e-04 -0.8249725 0.8234329 1.0000000
## T2020002-T2019018 -1.661615e-01 -1.7848634 1.4525404 1.0000000
## T2020003-T2019018 1.035086e-03 -0.8641975 0.8662677 1.0000000
## T2020004-T2019018 -1.587979e-01 -1.0011971 0.6836013 1.0000000
## T2020005-T2019018 -1.457232e-01 -1.0109557 0.7195094 1.0000000
## T2020006-T2019018 -5.420037e-02 -0.8784030 0.7700023 1.0000000
## T2020007-T2019018 -2.772432e-02 -0.9224955 0.8670468 1.0000000
## T2020009-T2019018 -3.995238e-01 -2.0182257 1.2191782 1.0000000
## T2019022-T2019020 5.400821e-02 -0.9908594 1.0988758 1.0000000
## T2019023-T2019020 -4.206047e-02 -1.0333089 0.9491880 1.0000000
## T2020001-T2019020 1.391048e-01 -0.7488619 1.0270715 1.0000000
## T2020002-T2019020 -2.628695e-02 -1.6783677 1.6257938 1.0000000
## T2020003-T2019020 1.409096e-01 -0.7852665 1.0670858 1.0000000
## T2020004-T2019020 -1.892330e-02 -0.9238052 0.8859586 1.0000000
## T2020005-T2019020 -5.848593e-03 -0.9320247 0.9203275 1.0000000
## T2020006-T2019020 8.567419e-02 -0.8022925 0.9736409 1.0000000
## T2020007-T2019020 1.121502e-01 -0.8416790 1.0659795 1.0000000
## T2020009-T2019020 -2.596492e-01 -1.9117299 1.3924315 1.0000000
## T2019023-T2019022 -9.606868e-02 -1.0873171 0.8951798 1.0000000
## T2020001-T2019022 8.509657e-02 -0.8028701 0.9730633 1.0000000
## T2020002-T2019022 -8.029516e-02 -1.7323759 1.5717856 1.0000000
## T2020003-T2019022 8.690143e-02 -0.8392747 1.0130776 1.0000000
## T2020004-T2019022 -7.293152e-02 -0.9778134 0.8319504 1.0000000
## T2020005-T2019022 -5.985680e-02 -0.9860329 0.8663193 1.0000000
## T2020006-T2019022 3.166598e-02 -0.8563007 0.9196327 1.0000000
## T2020007-T2019022 5.814203e-02 -0.8956872 1.0119713 1.0000000
## T2020009-T2019022 -3.136574e-01 -1.9657381 1.3384233 1.0000000
## T2020001-T2019023 1.811652e-01 -0.6430374 1.0053679 1.0000000
## T2020002-T2019023 1.577352e-02 -1.6029284 1.6344754 1.0000000
## T2020003-T2019023 1.829701e-01 -0.6822625 1.0482027 1.0000000
## T2020004-T2019023 2.313716e-02 -0.8192620 0.8655364 1.0000000
## T2020005-T2019023 3.621187e-02 -0.8290207 0.9014444 1.0000000
## T2020006-T2019023 1.277347e-01 -0.6964680 0.9519373 1.0000000
## T2020007-T2019023 1.542107e-01 -0.7405604 1.0489819 1.0000000
## T2020009-T2019023 -2.175887e-01 -1.8362907 1.4011132 1.0000000
## T2020002-T2020001 -1.653917e-01 -1.7229884 1.3922049 1.0000000
## T2020003-T2020001 1.804862e-03 -0.7428688 0.7464785 1.0000000
## T2020004-T2020001 -1.580281e-01 -0.8760447 0.5599885 1.0000000
## T2020005-T2020001 -1.449534e-01 -0.8896270 0.5997203 1.0000000
## T2020006-T2020001 -5.343059e-02 -0.7500090 0.6431478 1.0000000
## T2020007-T2020001 -2.695454e-02 -0.8057529 0.7518438 1.0000000
## T2020009-T2020001 -3.987540e-01 -1.9563506 1.1588427 1.0000000
## T2020003-T2020002 1.671966e-01 -1.4124947 1.7468879 1.0000000
## T2020004-T2020002 7.363646e-03 -1.5599378 1.5746650 1.0000000
## T2020005-T2020002 2.043836e-02 -1.5592530 1.6001297 1.0000000
## T2020006-T2020002 1.119611e-01 -1.4456355 1.6695578 1.0000000
## T2020007-T2020002 1.384372e-01 -1.4576244 1.7344988 1.0000000
## T2020009-T2020002 -2.333622e-01 -2.3230975 1.8563730 1.0000000
## T2020004-T2020003 -1.598330e-01 -0.9245977 0.6049318 1.0000000
## T2020005-T2020003 -1.467582e-01 -0.9366039 0.6430874 1.0000000
## T2020006-T2020003 -5.523545e-02 -0.7999091 0.6894382 1.0000000
## T2020007-T2020003 -2.875940e-02 -0.8508568 0.7933380 1.0000000
## T2020009-T2020003 -4.005588e-01 -1.9802502 1.1791325 1.0000000
## T2020005-T2020004 1.307471e-02 -0.7516901 0.7778395 1.0000000
## T2020006-T2020004 1.045975e-01 -0.6134191 0.8226141 1.0000000
## T2020007-T2020004 1.310735e-01 -0.6669573 0.9291044 1.0000000
## T2020009-T2020004 -2.407259e-01 -1.8080273 1.3265755 1.0000000
## T2020006-T2020005 9.152279e-02 -0.6531508 0.8361964 1.0000000
## T2020007-T2020005 1.179988e-01 -0.7040986 0.9400963 1.0000000
## T2020009-T2020005 -2.538006e-01 -1.8334919 1.3258907 1.0000000
## T2020007-T2020006 2.647605e-02 -0.7523223 0.8052744 1.0000000
## T2020009-T2020006 -3.453234e-01 -1.9029200 1.2122733 1.0000000
## T2020009-T2020007 -3.717994e-01 -1.9678611 1.2242622 1.0000000
tukey.anova2<- (TukeyHSD(anova2))
tukey.anova2
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = BWT ~ SEX + MPAR, data = data)
##
## $SEX
## diff lwr upr p adj
## 2-1 0.08303652 0.07206172 0.09401131 0
##
## $MPAR
## diff lwr upr p adj
## 1-0 -0.026103090 -0.052833357 0.0006271774 0.0618602
## 2-0 0.253082977 0.224923415 0.2812425384 0.0000000
## 3-0 0.277939127 0.244739234 0.3111390195 0.0000000
## 4-0 0.307590770 0.265193573 0.3499879661 0.0000000
## 5-0 0.258685039 0.203115653 0.3142544246 0.0000000
## 6-0 0.206586807 0.135344738 0.2778288760 0.0000000
## 7-0 0.147558136 0.060511541 0.2346047307 0.0000053
## 8-0 0.194849610 0.034384463 0.3553147565 0.0052125
## 2-1 0.279186066 0.243854879 0.3145172539 0.0000000
## 3-1 0.304042216 0.264576221 0.3435082116 0.0000000
## 4-1 0.333693859 0.286229619 0.3811581002 0.0000000
## 5-1 0.284788128 0.225262600 0.3443136569 0.0000000
## 6-1 0.232689897 0.158320788 0.3070590051 0.0000000
## 7-1 0.173661226 0.084037336 0.2632851149 0.0000001
## 8-1 0.220952699 0.059074982 0.3828304162 0.0007752
## 3-2 0.024856150 -0.015591576 0.0653038756 0.6093315
## 4-2 0.054507793 0.006224174 0.1027914122 0.0137089
## 5-2 0.005602062 -0.054578851 0.0657829748 0.9999986
## 6-2 -0.046496170 -0.121390882 0.0283985431 0.5956332
## 7-2 -0.105524841 -0.195585350 -0.0154643319 0.0085318
## 8-2 -0.058233367 -0.220353227 0.1038864928 0.9722775
## 4-3 0.029651643 -0.021734930 0.0810382158 0.6889759
## 5-3 -0.019254088 -0.081951889 0.0434437130 0.9898044
## 6-3 -0.071352320 -0.148284042 0.0055794033 0.0942814
## 7-3 -0.130380991 -0.222142460 -0.0386195214 0.0003592
## 8-3 -0.083089517 -0.246160425 0.0799813907 0.8156746
## 5-4 -0.048905731 -0.116922668 0.0191112053 0.3856606
## 6-4 -0.101003963 -0.182329109 -0.0196788167 0.0037412
## 7-4 -0.160032634 -0.255507534 -0.0645577333 0.0000072
## 8-4 -0.112741160 -0.277930174 0.0524478539 0.4613812
## 6-5 -0.052098231 -0.141003877 0.0368074138 0.6703377
## 7-5 -0.111126903 -0.213136172 -0.0091176333 0.0208354
## 8-5 -0.063835429 -0.232885258 0.1052144002 0.9624873
## 7-6 -0.059028671 -0.170354170 0.0522968279 0.7798664
## 8-6 -0.011737198 -0.186566601 0.1630922064 0.9999999
## 8-7 0.047291474 -0.134551859 0.2291348066 0.9967098
plot(tukey.anova2,las=1)
