Macrophomina - Simple bar plots
Juan Pablo Edwards Molina
## trat rota bloco inc pl pmg ndv rend pl10000
## 1 1 Msil-Fj-Sj 1 32 174747.5 173.6 58.4 1611.9 17.47475
## 2 1 Msil-Fj-Sj 2 34 185858.6 152.9 52.2 1677.0 18.58586
## 3 1 Msil-Fj-Sj 3 20 147474.7 158.4 52.0 1494.0 14.74747
## 4 1 Msil-Fj-Sj 4 44 147474.7 151.5 60.2 1596.4 14.74747
## 5 1 Msil-Fj-Sj 5 16 190909.1 145.5 35.4 1594.2 19.09091
## 6 2 Mg-Nb-Sj 1 48 237373.7 150.3 36.2 1464.2 23.73737Exploração geral dos dados
## $stats
## [,1] [,2] [,3] [,4]
## [1,] 15 36 16 28
## [2,] 18 36 20 34
## [3,] 22 38 32 38
## [4,] 30 38 34 50
## [5,] 34 38 44 56
## attr(,"class")
## Mg-Av-Sj
## "integer"
##
## $n
## [1] 5 5 5 5
##
## $conf
## [,1] [,2] [,3] [,4]
## [1,] 13.52083 36.58681 22.10764 26.69444
## [2,] 30.47917 39.41319 41.89236 49.30556
##
## $out
## [1] 48 28
##
## $group
## [1] 2 2
##
## $names
## [1] "Mg-Av-Sj" "Mg-Nb-Sj" "Msil-Fj-Sj" "Ms-Mg-Sj"
##
## $stats
## [,1] [,2] [,3] [,4]
## [1,] 19.39394 23.73737 14.74747 19.79798
## [2,] 20.20202 23.73737 14.74747 20.40404
## [3,] 23.43434 25.25253 17.47475 20.60606
## [4,] 24.64646 26.56566 18.58586 22.32323
## [5,] 31.01010 28.08081 19.09091 24.14141
##
## $n
## [1] 5 5 5 5
##
## $conf
## [,1] [,2] [,3] [,4]
## [1,] 20.29391 23.25407 14.76255 19.24997
## [2,] 26.57477 27.25099 20.18695 21.96215
##
## $out
## numeric(0)
##
## $group
## numeric(0)
##
## $names
## [1] "Mg-Av-Sj" "Mg-Nb-Sj" "Msil-Fj-Sj" "Ms-Mg-Sj"
##
## $stats
## [,1] [,2] [,3] [,4]
## [1,] 160.8 147.6 145.5 134.0
## [2,] 171.4 148.9 151.5 139.6
## [3,] 173.4 150.3 152.9 146.1
## [4,] 180.0 151.4 158.4 146.6
## [5,] 183.0 151.4 158.4 149.7
##
## $n
## [1] 5 5 5 5
##
## $conf
## [,1] [,2] [,3] [,4]
## [1,] 167.3233 148.5335 148.0245 141.1538
## [2,] 179.4767 152.0665 157.7755 151.0462
##
## $out
## [1] 155.6 173.6
##
## $group
## [1] 2 3
##
## $names
## [1] "Mg-Av-Sj" "Mg-Nb-Sj" "Msil-Fj-Sj" "Ms-Mg-Sj"
##
## $stats
## [,1] [,2] [,3] [,4]
## [1,] 38.8 31.6 52.0 32.2
## [2,] 43.8 36.2 52.0 36.4
## [3,] 46.6 41.6 52.2 45.8
## [4,] 49.6 51.4 58.4 55.4
## [5,] 49.8 56.0 60.2 55.8
##
## $n
## [1] 5 5 5 5
##
## $conf
## [,1] [,2] [,3] [,4]
## [1,] 42.50173 30.85972 47.67778 32.37465
## [2,] 50.69827 52.34028 56.72222 59.22535
##
## $out
## [1] 35.4
##
## $group
## [1] 3
##
## $names
## [1] "Mg-Av-Sj" "Mg-Nb-Sj" "Msil-Fj-Sj" "Ms-Mg-Sj"
##
## $stats
## [,1] [,2] [,3] [,4]
## [1,] 2219.7 1445.9 1594.2 1505.8
## [2,] 2219.7 1464.2 1594.2 1513.1
## [3,] 2223.6 1538.8 1596.4 1513.1
## [4,] 2255.8 1671.9 1611.9 1559.8
## [5,] 2255.8 1755.9 1611.9 1617.5
##
## $n
## [1] 5 5 5 5
##
## $conf
## [,1] [,2] [,3] [,4]
## [1,] 2198.092 1392.04 1583.893 1480.102
## [2,] 2249.108 1685.56 1608.907 1546.098
##
## $out
## [1] 2445.9 1982.1 1677.0 1494.0
##
## $group
## [1] 1 1 3 3
##
## $names
## [1] "Mg-Av-Sj" "Mg-Nb-Sj" "Msil-Fj-Sj" "Ms-Mg-Sj"
Análise individualizado por Variavel:
1- Incidência de podridão carvão (Macrophomina phaseolina)
## Analysis of Variance Table
##
## Response: inc
## Df Sum Sq Mean Sq F value Pr(>F)
## trat 3 937.35 312.450 3.2965 0.05785 .
## bloco 4 364.20 91.050 0.9606 0.46371
## Residuals 12 1137.40 94.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $statistics
## Mean CV MSerror HSD
## 32.95 29.54681 94.78333 18.28065
##
## $parameters
## Df ntr StudentizedRange
## 12 4 4.19866
##
## $means
## inc std r Min Max
## 1 29.2 11.278298 5 16 44
## 2 37.6 7.127412 5 28 48
## 3 41.2 11.541230 5 28 56
## 4 23.8 8.012490 5 15 34
##
## $comparison
## NULL
##
## $groups
## trt means M
## 1 3 41.2 a
## 2 2 37.6 a
## 3 1 29.2 a
## 4 4 23.8 a
## $statistics
## Mean CV MSerror HSD
## 32.95 29.54681 94.78333 15.76429
##
## $parameters
## Df ntr StudentizedRange
## 12 4 3.620708
##
## $means
## inc std r Min Max
## 1 29.2 11.278298 5 16 44
## 2 37.6 7.127412 5 28 48
## 3 41.2 11.541230 5 28 56
## 4 23.8 8.012490 5 15 34
##
## $comparison
## NULL
##
## $groups
## trt means M
## 1 3 41.2 a
## 2 2 37.6 ab
## 3 1 29.2 ab
## 4 4 23.8 b## $statistic
## W
## 0.93516##
## Bartlett test of homogeneity of variances
##
## data: inc by trat
## Bartlett's K-squared = 1.2291, df = 3, p-value = 0.746
2- Numero de plantas
## Analysis of Variance Table
##
## Response: pl
## Df Sum Sq Mean Sq F value Pr(>F)
## trat 3 2.0531e+10 6843520685 7.6728 0.003989 **
## bloco 4 2.1990e+09 549740511 0.6164 0.659192
## Residuals 12 1.0703e+10 891915930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $statistics
## Mean CV MSerror HSD
## 218989.9 13.6376 891915930 56077.38
##
## $parameters
## Df ntr StudentizedRange
## 12 4 4.19866
##
## $means
## pl std r Min Max
## 1 169292.9 20757.55 5 147474.7 190909.1
## 2 254747.5 18753.66 5 237373.7 280808.1
## 3 214545.4 17707.03 5 197979.8 241414.1
## 4 237373.7 46145.14 5 193939.4 310101.0
##
## $comparison
## NULL
##
## $groups
## trt means M
## 1 2 254747.5 a
## 2 4 237373.7 a
## 3 3 214545.4 ab
## 4 1 169292.9 b## $statistic
## W
## 0.9624905##
## Bartlett test of homogeneity of variances
##
## data: pl by trat
## Bartlett's K-squared = 5.1807, df = 3, p-value = 0.159
3- Peso mil graos
## Analysis of Variance Table
##
## Response: pmg
## Df Sum Sq Mean Sq F value Pr(>F)
## trat 3 2527.20 842.40 16.5037 0.0001475 ***
## bloco 4 337.67 84.42 1.6539 0.2247499
## Residuals 12 612.51 51.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $statistics
## Mean CV MSerror HSD
## 156.015 4.579324 51.04292 13.41509
##
## $parameters
## Df ntr StudentizedRange
## 12 4 4.19866
##
## $means
## pmg std r Min Max
## 1 156.38 10.664286 5 145.5 173.6
## 2 150.76 3.061536 5 147.6 155.6
## 3 143.20 6.320997 5 134.0 149.7
## 4 173.72 8.630875 5 160.8 183.0
##
## $comparison
## NULL
##
## $groups
## trt means M
## 1 4 173.72 a
## 2 1 156.38 b
## 3 2 150.76 b
## 4 3 143.20 b##
## Shapiro-Wilk normality test
##
## data: residuals(m_pmg)
## W = 0.9541, p-value = 0.4333##
## Bartlett test of homogeneity of variances
##
## data: pmg by trat
## Bartlett's K-squared = 4.9493, df = 3, p-value = 0.1755
4- Numero de vagens
## Analysis of Variance Table
##
## Response: ndv
## Df Sum Sq Mean Sq F value Pr(>F)
## trat 3 193.93 64.643 0.9528 0.4462
## bloco 4 533.31 133.327 1.9652 0.1642
## Residuals 12 814.13 67.844
## $statistics
## Mean CV MSerror HSD
## 46.46 17.72873 67.84433 15.46616
##
## $parameters
## Df ntr StudentizedRange
## 12 4 4.19866
##
## $means
## ndv std r Min Max
## 1 51.64 9.787134 5 35.4 60.2
## 2 43.36 10.211170 5 31.6 56.0
## 3 45.12 10.760669 5 32.2 55.8
## 4 45.72 4.583885 5 38.8 49.8
##
## $comparison
## NULL
##
## $groups
## trt means M
## 1 1 51.64 a
## 2 4 45.72 a
## 3 3 45.12 a
## 4 2 43.36 a##
## Shapiro-Wilk normality test
##
## data: residuals(m_ndv)
## W = 0.9742, p-value = 0.8393##
## Bartlett test of homogeneity of variances
##
## data: ndv by trat
## Bartlett's K-squared = 2.6355, df = 3, p-value = 0.4513
5- Rendimento
## Analysis of Variance Table
##
## Response: rend
## Df Sum Sq Mean Sq F value Pr(>F)
## trat 3 1614942 538314 33.7144 3.98e-06 ***
## bloco 4 15750 3938 0.2466 0.9062
## Residuals 12 191603 15967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $statistics
## Mean CV MSerror HSD
## 1734.33 7.285824 15966.91 237.2663
##
## $parameters
## Df ntr StudentizedRange
## 12 4 4.19866
##
## $means
## rend std r Min Max
## 1 1594.70 65.5987 5 1494.0 1677.0
## 2 1575.34 134.5063 5 1445.9 1755.9
## 3 1541.86 47.4285 5 1505.8 1617.5
## 4 2225.42 164.9053 5 1982.1 2445.9
##
## $comparison
## NULL
##
## $groups
## trt means M
## 1 4 2225.42 a
## 2 1 1594.70 b
## 3 2 1575.34 b
## 4 3 1541.86 b##
## Shapiro-Wilk normality test
##
## data: residuals(m_rend)
## W = 0.9521, p-value = 0.4006##
## Bartlett test of homogeneity of variances
##
## data: rend by trat
## Bartlett's K-squared = 6.4441, df = 3, p-value = 0.09189
Multivariada
variables = dat[,4:8]
Yp<-as.matrix(variables)
row.names(Yp)=dat$rota
grupo = dat$rota
cor(variables)## inc pl pmg ndv rend
## inc 1.00000000 0.05848082 -0.4029486 0.05738316 -0.46971926
## pl 0.05848082 1.00000000 0.1786000 -0.42727656 0.28624964
## pmg -0.40294862 0.17860002 1.0000000 0.11360893 0.80338730
## ndv 0.05738316 -0.42727656 0.1136089 1.00000000 -0.08121713
## rend -0.46971926 0.28624964 0.8033873 -0.08121713 1.00000000PCA.COR=princomp(~inc + pl + pmg + ndv + rend, cor=TRUE)
summary(PCA.COR)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.4855156 1.1871502 0.8988753 0.6364974 0.4132941
## Proportion of Variance 0.4413513 0.2818651 0.1615954 0.0810258 0.0341624
## Cumulative Proportion 0.4413513 0.7232164 0.8848118 0.9658376 1.0000000res.pca = PCA(variables, scale.unit = TRUE, ncp = 4,graph = TRUE)
mandat=manova(Yp~grupo); (test_Wilks<-summary(mandat,test="Wilks"))## Df Wilks approx F num Df den Df Pr(>F)
## grupo 3 0.022388 6.6163 15 33.528 2.876e-06 ***
## Residuals 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Si Pr<0.05, há diferencas entre as medias