Session set
pkg <- c("agricolae", "knitr", "easyanova", "MASS")
sapply(pkg, library, character.only=TRUE, logical.return=TRUE)
## agricolae knitr easyanova MASS
## TRUE TRUE TRUE TRUE
Dataset
setwd("/home/epi/Dropbox/MyR/Análise otros/Gui")
list.files()
## [1] "gui_qca.csv"
# Dados planilha laboratorio
dat = read.csv("gui_qca.csv", dec=",", header=T, sep="\t", check.names=FALSE)
dat
## trt rep hesp hesp_ch dios nobi tange erio rutin
## 1 sad 1 4720.8 97.1 52.9 522.5 126.0 10820.3 0.7
## 2 sad 1 4684.3 199.6 54.7 640.2 111.3 10362.7 0.6
## 3 fm 2 4775.5 96.0 90.8 564.5 56.3 8651.2 1.1
## 4 fm 2 4209.9 119.4 97.7 434.2 84.2 7346.4 0.1
## 5 md 3 5505.4 137.8 122.9 602.4 135.0 9227.4 1.5
## 6 md 3 4337.6 107.6 NA 589.8 75.5 7465.0 0.7
for (i in 1:2) dat[,i] <- as.factor(dat[,i])
attach(dat)
Hesperidin
(mod_hesp<-ea1(dat[,c(1,3)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 2 183897.0 91948.52 0.3274 0.7437
## Residuals 3 842496.2 280832.08 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 md 4921.50 374.7213 a a a a a
## 2 sad 4702.55 374.7213 a a a a a
## 3 fm 4492.70 374.7213 a a a a a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 md - sad 218.95 0.9128 0.7072 0.7072 0.7072
## 2 md - fm 428.80 0.7240 0.7240 0.4747 0.4776
## 3 sad - fm 209.85 0.9194 0.7186 0.7186 0.7186
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.9868
## p.value Bartlett test 0.1381
## coefficient of variation (%) 11.2600
## first value most discrepant 6.0000
## second value most discrepant 5.0000
## third value most discrepant 3.0000
par(mfrow=c(1,2))
boxplot(hesp~trt, main="hesperedin")
Hesperidin chalcona
(mod_hesp.ch<-ea1(dat[,c(1,4)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 2 1690.230 845.115 0.4238 0.6885
## Residuals 3 5982.925 1994.308 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 sad 148.35 31.5777 a a a a a
## 2 md 122.70 31.5777 a a a a a
## 3 fm 107.70 31.5777 a a a a a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 sad - md 25.65 0.8424 0.6060 0.6060 0.6060
## 2 sad - fm 40.65 0.6717 0.6717 0.4270 0.4298
## 3 md - fm 15.00 0.9409 0.7591 0.7591 0.7591
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.9346
## p.value Bartlett test 0.4220
## coefficient of variation (%) 35.3700
## first value most discrepant 1.0000
## second value most discrepant 2.0000
## third value most discrepant 6.0000
boxplot(hesp_ch~trt, main="hesp_ch")
Diosmin
dat_dios = subset(dat, trt=="sad" | trt=="fm")
(mod_hesp.ch<-ea1(dat_dios[,c(1,5)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 1 1636.203 1636.2025 128.7082 0.0077
## Residuals 2 25.425 12.7125 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 fm 94.25 2.5212 a a a a a
## 2 sad 53.80 2.5212 b b b b a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 fm - sad 40.45 0.0075 0.0075 0.0075 0.0077
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.9992
## p.value Bartlett test 0.3284
## coefficient of variation (%) 4.8200
## first value most discrepant 3.0000
## second value most discrepant 4.0000
## third value most discrepant 1.0000
boxplot(dios~trt, main="dios")
Nobiletin
(mod_nobi<-ea1(dat[,c(1,6)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 2 10868.08 5434.042 1.0521 0.4506
## Residuals 3 15495.07 5165.023 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 md 596.10 50.8184 a a a a a
## 2 sad 581.35 50.8184 a a a a a
## 3 fm 499.35 50.8184 a a a a a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 md - sad 14.75 0.9772 0.8505 0.8505 0.8505
## 2 md - fm 96.75 0.4660 0.4660 0.2693 0.2709
## 3 sad - fm 82.00 0.5571 0.3367 0.3367 0.3367
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.3597
## p.value Bartlett test 0.3098
## coefficient of variation (%) 12.8600
## first value most discrepant 3.0000
## second value most discrepant 4.0000
## third value most discrepant 2.0000
boxplot(nobi~trt, main="nobi")
Tangeritin
(mod_tange<-ea1(dat[,c(1,7)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 2 2498.080 1249.0400 1.6526 0.3282
## Residuals 3 2267.375 755.7917 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 sad 118.65 19.4395 a a a a a
## 2 md 105.25 19.4395 a a a a a
## 3 fm 70.25 19.4395 a a a a a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 sad - md 13.4 0.8821 0.6594 0.6594 0.6594
## 2 sad - fm 48.4 0.3209 0.3209 0.1759 0.1765
## 3 md - fm 35.0 0.4971 0.2927 0.2927 0.2927
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.9899
## p.value Bartlett test 0.5442
## coefficient of variation (%) 28.0400
## first value most discrepant 5.0000
## second value most discrepant 6.0000
## third value most discrepant 3.0000
boxplot(tange~trt, main="tange")
Erio
(mod_erio<-ea1(dat[,c(1,8)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 2 7922768 3961384.0 4.7367 0.118
## Residuals 3 2508977 836325.8 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 sad 10591.5 646.6551 a a a a a
## 2 md 8346.2 646.6551 a a a a a
## 3 fm 7998.8 646.6551 a a a a a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 sad - md 2245.3 0.1746 0.0913 0.0913 0.0913
## 2 sad - fm 2592.7 0.1281 0.1281 0.0663 0.0659
## 3 md - fm 347.4 0.9254 0.7293 0.7293 0.7293
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.7000
## p.value Bartlett test 0.6072
## coefficient of variation (%) 10.1900
## first value most discrepant 5.0000
## second value most discrepant 6.0000
## third value most discrepant 4.0000
boxplot(erio~trt, main="erio")
Rutin
(mod_rutin<-ea1(dat[,c(1,9)], design=1, plot=3))
## $`Analysis of variance`
## df type I SS mean square F value p>F
## treatments 2 0.3033 0.1517 0.5515 0.6252
## Residuals 3 0.8250 0.2750 - -
##
## $Means
## treatment mean standard.error tukey snk duncan t scott_knott
## 1 md 1.10 0.3708 a a a a a
## 2 sad 0.65 0.3708 a a a a a
## 3 fm 0.60 0.3708 a a a a a
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 md - sad 0.45 0.6986 0.4539 0.4539 0.4539
## 2 md - fm 0.50 0.6495 0.6495 0.4080 0.4107
## 3 sad - fm 0.05 0.9950 0.9300 0.9300 0.9300
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.5873
## p.value Bartlett test 0.3238
## coefficient of variation (%) 66.9500
## first value most discrepant 3.0000
## second value most discrepant 4.0000
## third value most discrepant 5.0000
boxplot(rutin~trt, main="rutin")
