Walmes Zeviani
Milson Evaldo Serafim
Tabela de conteúdo
Material complementar ao artigo com título ??? submetido à revista ???, volume ???, número ???. A citação será incluída assim que for determinado o volume e paginação.
%% Citação LaTex.
@article{SerafimAbacaxiSolo2014,
author = {},
title = {Atributos químicos do solo sob cultivo de abacaxi em
função da aplicação de gesso e cobertura do solo},
doi = {},
journal = {},
month = {},
pages = {},
publisher = {},
url = {},
year = {2014}
}
Donwnload:
##-----------------------------------------------------------------------------
## Definições da sessão, pacotes a serem usados.
## Instruções para instalação do wzRfun em:
## browseURL("https://github.com/walmes/wzRfun")
pkg <- c("lattice", "latticeExtra",
"reshape", "plyr", "doBy", "multcomp",
"wzRfun")
sapply(pkg, library, character.only=TRUE, logical.return=TRUE)
## lattice latticeExtra reshape plyr doBy multcomp
## TRUE TRUE TRUE TRUE TRUE TRUE
## wzRfun
## TRUE
##-----------------------------------------------------------------------------
## Configurações de cores da lattice.
colp <- brewer.pal(11, "Spectral")
colp <- colorRampPalette(colp, space="rgb")
mycol <- brewer.pal(8, "Dark2")
ps <- list(
box.dot=list(col="black", pch="|"),
box.rectangle=list(col="black", fill=c("gray70")),
box.umbrella=list(col="black", lty=1),
dot.symbol=list(col="black"),
dot.line=list(col="gray90", lty=1),
plot.symbol=list(col="black", cex=0.8),
plot.line=list(col="black", lty=1.2),
plot.polygon=list(col="gray80"),
superpose.line=list(col=mycol),
superpose.symbol=list(col=mycol),
superpose.polygon=list(col=mycol),
regions=list(col=colp(100)),
strip.background=list(col=c("gray90","gray50")),
strip.shingle=list(col=c("gray50","gray90"))
)
## trellis.par.set(ps)
rm(list=c("colp", "mycol", "ps"))
##-----------------------------------------------------------------------------
## Gráficos monocromáticos.
## names(lattice.options())
trellis.device(color=FALSE)
##-----------------------------------------------------------------------------
## Informações sobre as versões dos pacotes.
sessionInfo()
## R version 3.1.1 (2014-07-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=pt_BR.UTF-8
## [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=pt_BR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=pt_BR.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] splines stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] wzRfun_0.1 multcomp_1.3-3 TH.data_1.0-3 mvtnorm_0.9-9997
## [5] doBy_4.5-10 MASS_7.3-33 survival_2.37-7 plyr_1.8.1
## [9] reshape_0.8.5 latticeExtra_0.6-26 RColorBrewer_1.0-5 lattice_0.20-29
## [13] knitr_1.6
##
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.5 formatR_0.10 grid_3.1.1 lme4_1.1-6
## [5] Matrix_1.1-4 methods_3.1.1 minqa_1.2.3 nlme_3.1-117
## [9] Rcpp_0.11.2 RcppEigen_0.3.2.0.2 sandwich_2.3-0 stringr_0.6.2
## [13] tools_3.1.1 zoo_1.7-10
## obs: Para instalar um pacote faça:
## install.packages("nome_do_pacote", dependencies=TRUE)
##-----------------------------------------------------------------------------
## Como citar o R.
citation()
##
## To cite R in publications use:
##
## R Core Team (2014). R: A language and environment for statistical computing. R
## Foundation for Statistical Computing, Vienna, Austria. URL
## http://www.R-project.org/.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {R: A Language and Environment for Statistical Computing},
## author = {{R Core Team}},
## organization = {R Foundation for Statistical Computing},
## address = {Vienna, Austria},
## year = {2014},
## url = {http://www.R-project.org/},
## }
##
## We have invested a lot of time and effort in creating R, please cite it when
## using it for data analysis. See also 'citation("pkgname")' for citing R
## packages.
##-----------------------------------------------------------------------------
## Ler.
## list.files(pattern="\\.txt$")
url <- "http://www.leg.ufpr.br/~walmes/data/abacaxisolo.txt"
da <- read.table(url, header=TRUE, sep="\t")
str(da)
## 'data.frame': 64 obs. of 12 variables:
## $ cober: Factor w/ 2 levels "com cobertura",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ ges : Factor w/ 2 levels "com gesso","sem gesso": 2 2 2 2 2 2 2 2 2 2 ...
## $ vari : Factor w/ 4 levels "Havai","IAC Fantástico",..: 4 4 4 4 1 1 1 1 2 2 ...
## $ blo : int 1 2 3 4 1 2 3 4 1 2 ...
## $ ph : num 4.8 5.6 5 5 4.5 4.8 4.8 5.1 4.7 4.4 ...
## $ p : num 3.16 3.74 14.81 33.97 3.53 ...
## $ k : num 0.57 0.54 0.31 0.68 0.65 0.68 0.39 0.72 0.84 0.52 ...
## $ ca : num 1.1 1.5 1.5 1.3 1.2 1.1 1.2 1.1 1.1 1.1 ...
## $ mg : num 1.3 1.1 1 0.8 1 1.2 1.4 1.5 1.3 1.2 ...
## $ mo : num 13.8 14.8 16 14.1 20.7 ...
## $ ctc : num 2.97 3.24 2.91 2.98 2.95 3.18 3.09 3.42 3.34 2.92 ...
## $ v : num 56.2 57.5 57.5 57.2 57.9 ...
head(da)
## cober ges vari blo ph p k ca mg mo ctc v
## 1 sem cobertura sem gesso Pérola 1 4.8 3.16 0.57 1.1 1.3 13.82 2.97 56.18
## 2 sem cobertura sem gesso Pérola 2 5.6 3.74 0.54 1.5 1.1 14.76 3.24 57.48
## 3 sem cobertura sem gesso Pérola 3 5.0 14.81 0.31 1.5 1.0 16.01 2.91 57.52
## 4 sem cobertura sem gesso Pérola 4 5.0 33.97 0.68 1.3 0.8 14.13 2.98 57.25
## 5 sem cobertura sem gesso Havai 1 4.5 3.53 0.65 1.2 1.0 20.72 2.95 57.86
## 6 sem cobertura sem gesso Havai 2 4.8 33.97 0.68 1.1 1.2 14.44 3.18 58.94
##-----------------------------------------------------------------------------
## Editar.
da$blo <- factor(da$blo)
##-----------------------------------------------------------------------------
## Resumo descritivo.
with(da, ftable(vari~cober+ges))
## vari Havai IAC Fantástico Imperial Pérola
## cober ges
## com cobertura com gesso 4 4 4 4
## sem gesso 4 4 4 4
## sem cobertura com gesso 4 4 4 4
## sem gesso 4 4 4 4
##-----------------------------------------------------------------------------
## Presença de missings.
colSums(apply(da[,-c(1:4)], 2, is.na))
## ph p k ca mg mo ctc v
## 0 0 0 0 5 0 0 0
## lapply(da[,-c(1:4)],
## function(i){
## with(da, xtabs(!is.na(i)~vari+interaction(ges, cober)))
## })
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(ph~cober|vari, groups=ges, data=da,
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(ph~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: ph
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.039 0.013 0.19 0.9032
## vari 3 0.439 0.146 2.12 0.1110
## ges 1 0.214 0.214 3.10 0.0852 .
## cober 1 0.544 0.544 7.87 0.0074 **
## vari:ges 3 0.297 0.099 1.43 0.2460
## vari:cober 3 0.159 0.053 0.77 0.5178
## ges:cober 1 0.056 0.056 0.82 0.3710
## vari:ges:cober 3 0.137 0.046 0.66 0.5811
## Residuals 45 3.108 0.069
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari+ges+cober)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: ph ~ blo + vari + ges + cober
## Model 2: ph ~ blo + vari * ges * cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 55 3.76
## 2 45 3.11 10 0.649 0.94 0.51
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect="cober")
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc
## cober estimate se df t.stat p.value lwr upr
## 1 com cobertura 4.922 0.0462 55 106.5 2.106e-65 4.829 5.014
## 2 sem cobertura 4.738 0.0462 55 102.5 1.702e-64 4.645 4.830
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect="ges")
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg
## ges estimate se df t.stat p.value lwr upr
## 1 com gesso 4.772 0.0462 55 103.3 1.146e-64 4.679 4.864
## 2 sem gesso 4.888 0.0462 55 105.8 3.091e-65 4.795 4.980
## LSmeans(m0, effect="vari")
lsmv <- LSmeans(m1, effect="vari")
lsmv <- cbind(lsmv$grid, lsmv$coef)
lsmv <- equallevels(lsmv, da)
lsmv
## vari estimate se df t.stat p.value lwr upr
## 1 Havai 4.850 0.06534 55 74.22 7.823e-57 4.719 4.981
## 2 IAC Fantástico 4.713 0.06534 55 72.12 3.745e-56 4.582 4.843
## 3 Imperial 4.813 0.06534 55 73.65 1.194e-56 4.682 4.943
## 4 Pérola 4.944 0.06534 55 75.66 2.757e-57 4.813 5.075
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("pH")
p1 <-
segplot(cober~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
p2 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmv, draw=FALSE,
xlab=xlab, ylab="Cultivares de abacaxi")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
plot(p1, split=c(1,1,2,1), more=TRUE)
plot(p2, split=c(2,1,2,1), more=FALSE)
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(log(p)~cober|vari, groups=ges, data=da,
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(p~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
MASS::boxcox(m0)
abline(v=1/3, col=2)
m0 <- lm(p^(1/3)~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: p^(1/3)
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 3.0 1.0 1.81 0.16
## vari 3 1.6 0.5 0.98 0.41
## ges 1 10.8 10.8 19.91 5.4e-05 ***
## cober 1 59.6 59.6 109.69 1.2e-13 ***
## vari:ges 3 1.4 0.5 0.85 0.48
## vari:cober 3 0.6 0.2 0.36 0.78
## ges:cober 1 0.5 0.5 0.99 0.33
## vari:ges:cober 3 1.7 0.6 1.04 0.38
## Residuals 45 24.4 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari+ges+cober)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: p^(1/3) ~ blo + vari + ges + cober
## Model 2: p^(1/3) ~ blo + vari * ges * cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 55 28.6
## 2 45 24.4 10 4.2 0.77 0.65
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect="cober")
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc[,c("estimate","lwr","upr")] <- lsmc[,c("estimate","lwr","upr")]^3
lsmc
## cober estimate se df t.stat p.value lwr upr
## 1 com cobertura 74.51 0.1276 55 32.99 6.356e-38 61.737 88.93
## 2 sem cobertura 11.83 0.1276 55 17.86 1.480e-24 8.275 16.27
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect="ges")
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg[,c("estimate","lwr","upr")] <- lsmg[,c("estimate","lwr","upr")]^3
lsmg
## ges estimate se df t.stat p.value lwr upr
## 1 com gesso 48.79 0.1276 55 28.64 9.799e-35 39.25 59.77
## 2 sem gesso 22.72 0.1276 55 22.20 3.964e-29 17.10 29.44
## LSmeans(m0, effect="vari")
lsmv <- LSmeans(m1, effect="vari")
lsmv <- cbind(lsmv$grid, lsmv$coef)
lsmv <- equallevels(lsmv, da)
lsmv[,c("estimate","lwr","upr")] <- lsmv[,c("estimate","lwr","upr")]^3
lsmv
## vari estimate se df t.stat p.value lwr upr
## 1 Havai 28.42 0.1804 55 16.92 1.863e-23 19.47 39.77
## 2 IAC Fantástico 40.10 0.1804 55 18.97 8.416e-26 28.68 54.19
## 3 Imperial 38.39 0.1804 55 18.70 1.684e-25 27.32 52.10
## 4 Pérola 30.51 0.1804 55 17.32 6.230e-24 21.10 42.37
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("Fósforo"~(mg~dm^{-3}))
p1 <-
segplot(cober~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
p2 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmv, draw=FALSE,
xlab=xlab, ylab="Cultivares de abacaxi")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
plot(p1, split=c(1,1,2,1), more=TRUE)
plot(p2, split=c(2,1,2,1), more=FALSE)
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(k~cober|vari, groups=ges, data=da,
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(k~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.096 0.0321 1.63 0.196
## vari 3 0.060 0.0199 1.01 0.397
## ges 1 0.050 0.0501 2.54 0.118
## cober 1 0.024 0.0244 1.24 0.271
## vari:ges 3 0.008 0.0028 0.14 0.935
## vari:cober 3 0.152 0.0506 2.57 0.066 .
## ges:cober 1 0.031 0.0311 1.58 0.216
## vari:ges:cober 3 0.004 0.0013 0.06 0.979
## Residuals 45 0.886 0.0197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari*cober+ges)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: k ~ blo + vari + cober + ges + vari:cober
## Model 2: k ~ blo + vari * ges * cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 52 0.929
## 2 45 0.886 7 0.0432 0.31 0.94
anova(m1)
## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.096 0.0321 1.80 0.159
## vari 3 0.060 0.0199 1.11 0.352
## cober 1 0.024 0.0244 1.37 0.248
## ges 1 0.050 0.0501 2.80 0.100
## vari:cober 3 0.152 0.0506 2.83 0.047 *
## Residuals 52 0.929 0.0179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect="cober")
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc
## cober estimate se df t.stat p.value lwr upr
## 1 com cobertura 0.5594 0.02363 52 23.67 1.650e-29 0.512 0.6068
## 2 sem cobertura 0.5984 0.02363 52 25.32 6.494e-31 0.551 0.6459
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect="ges")
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg
## ges estimate se df t.stat p.value lwr upr
## 1 com gesso 0.5509 0.02363 52 23.31 3.401e-29 0.5035 0.5984
## 2 sem gesso 0.6069 0.02363 52 25.68 3.306e-31 0.5595 0.6543
## LSmeans(m0, effect="vari")
lsmv <- LSmeans(m1, effect="vari")
lsmv <- cbind(lsmv$grid, lsmv$coef)
lsmv <- equallevels(lsmv, da)
lsmv
## vari estimate se df t.stat p.value lwr upr
## 1 Havai 0.5356 0.03342 52 16.03 9.208e-22 0.4686 0.6027
## 2 IAC Fantástico 0.6213 0.03342 52 18.59 1.273e-24 0.5542 0.6883
## 3 Imperial 0.5850 0.03342 52 17.50 1.900e-23 0.5179 0.6521
## 4 Pérola 0.5738 0.03342 52 17.17 4.506e-23 0.5067 0.6408
##-----------------------------------------------------------------------------
## Olhando para interação vari:cober.
lsmcv <- LSmeans(m1, effect=c("cober","vari"))
lsmcv <- cbind(lsmcv$grid, lsmcv$coef)
lsmcv <- equallevels(lsmcv, da)
lsmcv
## cober vari estimate se df t.stat p.value lwr upr
## 1 com cobertura Havai 0.4525 0.04727 52 9.573 4.587e-13 0.3577 0.5473
## 2 sem cobertura Havai 0.6188 0.04727 52 13.090 4.356e-18 0.5239 0.7136
## 3 com cobertura IAC Fantástico 0.6125 0.04727 52 12.958 6.540e-18 0.5177 0.7073
## 4 sem cobertura IAC Fantástico 0.6300 0.04727 52 13.328 2.108e-18 0.5352 0.7248
## 5 com cobertura Imperial 0.5475 0.04727 52 11.583 5.113e-16 0.4527 0.6423
## 6 sem cobertura Imperial 0.6225 0.04727 52 13.170 3.417e-18 0.5277 0.7173
## 7 com cobertura Pérola 0.6250 0.04727 52 13.223 2.908e-18 0.5302 0.7198
## 8 sem cobertura Pérola 0.5225 0.04727 52 11.054 2.916e-15 0.4277 0.6173
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("Potássio"~(cmol[c]~dm^{-3}))
p1 <-
segplot(cober~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
p2 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmv, draw=FALSE,
xlab=xlab, ylab="Cultivares de abacaxi")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
l <- levels(lsmcv$cober)
key <- list(##corner=c(0.97,0.97),
columns=2,
type="o", divide=1,
lines=list(lty=1, pch=1:length(l)),
text=list(l))
p2 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmcv, draw=FALSE, key=key,
groups=lsmcv$cober, f=0.1, pch=as.integer(lsmcv$cober),
xlab=xlab, ylab="Cultivares de abacaxi",
panel=panel.segplotBy)
plot(p2)
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(ca~cober|vari, groups=ges, data=da,
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(ca~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
MASS::boxcox(m0)
abline(v=1/3, col=2)
m0 <- lm(ca^(1/3)~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: ca^(1/3)
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.010 0.003 0.56 0.643
## vari 3 0.040 0.013 2.32 0.088 .
## ges 1 0.004 0.004 0.66 0.422
## cober 1 0.948 0.948 164.94 <2e-16 ***
## vari:ges 3 0.034 0.011 1.98 0.130
## vari:cober 3 0.028 0.009 1.64 0.193
## ges:cober 1 0.035 0.035 6.08 0.018 *
## vari:ges:cober 3 0.027 0.009 1.58 0.207
## Residuals 45 0.259 0.006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Quadro de anova particionado.
m0 <- aov(ca^(1/3)~blo+vari*ges+ges/cober+vari:ges:cober, data=da)
c0 <- coef(m0)
a <- m0$assign
c0 <- c0[a==5]
s <- paste0("ges", levels(da$ges), ":cober")
s <- sapply(s, grep, x=names(c0), simplify=FALSE)
names(s) <- levels(da$ges)
summary(m0, split=list("ges:cober"=s))
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.010 0.003 0.56 0.643
## vari 3 0.040 0.013 2.32 0.088 .
## ges 1 0.004 0.004 0.66 0.422
## vari:ges 3 0.034 0.011 1.98 0.130
## ges:cober 2 0.983 0.491 85.51 4.7e-16 ***
## ges:cober: com gesso 1 0.673 0.673 117.17 4.1e-14 ***
## ges:cober: sem gesso 1 0.310 0.310 53.85 3.2e-09 ***
## vari:ges:cober 6 0.056 0.009 1.61 0.166
## Residuals 45 0.259 0.006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari+ges*cober)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: ca^(1/3) ~ blo + vari + ges + cober + ges:cober
## Model 2: ca^(1/3) ~ blo + vari * ges + ges/cober + vari:ges:cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 54 0.348
## 2 45 0.259 9 0.0898 1.74 0.11
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect=c("cober","ges"))
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc[,c("estimate","lwr","upr")] <- lsmc[,c("estimate","lwr","upr")]^3
lsmc
## cober ges estimate se df t.stat p.value lwr upr
## 1 com cobertura com gesso 2.181 0.02008 54 64.58 8.253e-53 1.9838 2.390
## 2 sem cobertura com gesso 1.020 0.02008 54 50.13 5.749e-47 0.9025 1.147
## 3 com cobertura sem gesso 1.882 0.02008 54 61.48 1.127e-51 1.7040 2.072
## 4 sem cobertura sem gesso 1.118 0.02008 54 51.69 1.134e-47 0.9932 1.254
## LSmeans(m0, effect="vari")
lsmv <- LSmeans(m1, effect="vari")
lsmv <- cbind(lsmv$grid, lsmv$coef)
lsmv <- equallevels(lsmv, da)
lsmv[,c("estimate","lwr","upr")] <- lsmv[,c("estimate","lwr","upr")]^3
lsmv
## vari estimate se df t.stat p.value lwr upr
## 1 Havai 1.660 0.02008 54 58.97 1.043e-50 1.497 1.836
## 2 IAC Fantástico 1.388 0.02008 54 55.55 2.490e-49 1.243 1.544
## 3 Imperial 1.465 0.02008 54 56.56 9.560e-50 1.315 1.627
## 4 Pérola 1.484 0.02008 54 56.80 7.623e-50 1.332 1.647
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("Cálcio"~(cmol[c]~dm^{-3}))
p1 <-
segplot(cober~lwr+upr|ges, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
l <- levels(lsmc$cober)
key <- list(##corner=c(0.97,0.97),
columns=2,
type="o", divide=1,
lines=list(lty=1, pch=1:length(l)),
text=list(l))
p2 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE, key=key,
groups=lsmc$cober, f=0.1, pch=as.integer(lsmc$cober),
xlab=xlab, ylab="Aplicação de gesso",
panel=panel.segplotBy)
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmv, draw=FALSE,
xlab=xlab, ylab="Cultivares de abacaxi")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
p2
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(mg~cober|vari, groups=ges, data=da[!is.na(da$mg),],
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(mg~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: mg
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.18 0.059 0.59 0.62433
## vari 3 0.14 0.045 0.45 0.71693
## ges 1 0.32 0.322 3.21 0.08067 .
## cober 1 1.36 1.365 13.64 0.00066 ***
## vari:ges 3 0.30 0.101 1.01 0.39663
## vari:cober 3 0.89 0.297 2.96 0.04351 *
## ges:cober 1 0.11 0.114 1.14 0.29232
## vari:ges:cober 3 0.65 0.216 2.16 0.10753
## Residuals 40 4.00 0.100
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Quadro de anova particionado.
m0 <- aov(mg~blo+vari*ges+vari/cober+vari:ges:cober, data=da)
c0 <- coef(m0)
a <- m0$assign
c0 <- c0[a==5]
s <- paste0("vari", levels(da$vari), ":cober")
s <- sapply(s, grep, x=names(c0), simplify=FALSE)
names(s) <- levels(da$vari)
summary(m0, split=list("vari:cober"=s))
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 0.18 0.059 0.59 0.62433
## vari 3 0.14 0.045 0.45 0.71693
## ges 1 0.32 0.322 3.21 0.08067 .
## vari:ges 3 0.30 0.099 0.99 0.40584
## vari:cober 4 2.26 0.565 5.65 0.00106 **
## vari:cober: Havai 1 1.51 1.511 15.09 0.00038 ***
## vari:cober: IAC Fantástico 1 0.68 0.681 6.80 0.01276 *
## vari:cober: Imperial 1 0.00 0.002 0.02 0.88948
## vari:cober: Pérola 1 0.07 0.067 0.67 0.41686
## vari:ges:cober 4 0.76 0.191 1.91 0.12819
## Residuals 40 4.00 0.100
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 5 observations deleted due to missingness
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari*cober+ges)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: mg ~ blo + vari + cober + ges + vari:cober
## Model 2: mg ~ blo + vari * ges + vari/cober + vari:ges:cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 47 5.2
## 2 40 4.0 7 1.2 1.71 0.13
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect=c("cober","vari"))
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc
## cober vari estimate se df t.stat p.value lwr upr
## 1 com cobertura Havai 0.6675 0.1375 47 4.854 1.376e-05 0.3909 0.9441
## 2 sem cobertura Havai 1.3000 0.1176 47 11.051 1.153e-14 1.0634 1.5366
## 3 com cobertura IAC Fantástico 0.9250 0.1176 47 7.864 4.074e-10 0.6884 1.1616
## 4 sem cobertura IAC Fantástico 1.3375 0.1176 47 11.370 4.326e-15 1.1009 1.5741
## 5 com cobertura Imperial 1.0092 0.1264 47 7.984 2.692e-10 0.7549 1.2635
## 6 sem cobertura Imperial 1.0375 0.1176 47 8.820 1.567e-11 0.8009 1.2741
## 7 com cobertura Pérola 1.0175 0.1375 47 7.399 2.034e-09 0.7409 1.2941
## 8 sem cobertura Pérola 1.1875 0.1176 47 10.095 2.358e-13 0.9509 1.4241
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect="ges")
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg
## ges estimate se df t.stat p.value lwr upr
## 1 com gesso 1.118 0.06443 47 17.35 4.528e-22 0.9885 1.248
## 2 sem gesso 1.002 0.05994 47 16.72 2.046e-21 0.8817 1.123
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("Magnésio"~(cmol[c]~dm^{-3}))
p1 <-
segplot(cober~lwr+upr|vari, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
l <- levels(lsmc$cober)
key <- list(##corner=c(0.97,0.97),
columns=2,
type="o", divide=1,
lines=list(lty=1, pch=1:length(l)),
text=list(l))
p2 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE, key=key,
groups=lsmc$cober, f=0.1, pch=as.integer(lsmc$cober),
xlab=xlab, ylab="Cultivares de abacaxi",
panel=panel.segplotBy)
p3 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
p2
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(mo~cober|vari, groups=ges, data=da[!is.na(da$mg),],
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(mo~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: mo
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 42.4 14.1 3.35 0.027 *
## vari 3 6.5 2.2 0.52 0.674
## ges 1 18.9 18.9 4.48 0.040 *
## cober 1 125.2 125.2 29.63 2.1e-06 ***
## vari:ges 3 43.2 14.4 3.41 0.025 *
## vari:cober 3 0.5 0.2 0.04 0.989
## ges:cober 1 10.4 10.4 2.47 0.123
## vari:ges:cober 3 20.4 6.8 1.61 0.200
## Residuals 45 190.1 4.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Quadro de anova particionado.
m0 <- aov(mo~blo+vari*cober+vari/ges+ges:cober+vari:ges:cober, data=da)
c0 <- coef(m0)
a <- m0$assign
c0 <- c0[a==5]
s <- paste0("vari", levels(da$vari), ":ges")
s <- sapply(s, grep, x=names(c0), simplify=FALSE)
names(s) <- levels(da$vari)
summary(m0, split=list("vari:ges"=s))
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 42.4 14.1 3.35 0.0272 *
## vari 3 6.5 2.2 0.52 0.6738
## cober 1 125.2 125.2 29.63 2.1e-06 ***
## vari:cober 3 0.5 0.2 0.04 0.9887
## vari:ges 4 62.2 15.5 3.68 0.0112 *
## vari:ges: Havai 1 36.9 36.9 8.74 0.0049 **
## vari:ges: IAC Fantástico 1 15.1 15.1 3.58 0.0650 .
## vari:ges: Imperial 1 7.8 7.8 1.84 0.1814
## vari:ges: Pérola 1 2.3 2.3 0.55 0.4619
## cober:ges 1 10.4 10.4 2.47 0.1230
## vari:cober:ges 3 20.4 6.8 1.61 0.2000
## Residuals 45 190.1 4.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari*ges+cober)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: mo ~ blo + vari + ges + cober + vari:ges
## Model 2: mo ~ blo + vari * cober + vari/ges + ges:cober + vari:ges:cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 52 221
## 2 45 190 7 31.4 1.06 0.4
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect=c("cober"))
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc
## cober estimate se df t.stat p.value lwr upr
## 1 com cobertura 17.2 0.3648 52 47.14 2.416e-44 16.47 17.93
## 2 sem cobertura 14.4 0.3648 52 39.48 1.923e-40 13.67 15.13
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect=c("ges","vari"))
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg
## ges vari estimate se df t.stat p.value lwr upr
## 1 com gesso Havai 14.01 0.7296 52 19.21 2.857e-25 12.55 15.48
## 2 sem gesso Havai 17.05 0.7296 52 23.37 3.002e-29 15.59 18.52
## 3 com gesso IAC Fantástico 15.30 0.7296 52 20.97 4.960e-27 13.84 16.76
## 4 sem gesso IAC Fantástico 17.24 0.7296 52 23.63 1.765e-29 15.78 18.71
## 5 com gesso Imperial 16.61 0.7296 52 22.77 1.045e-28 15.15 18.07
## 6 sem gesso Imperial 15.21 0.7296 52 20.85 6.429e-27 13.75 16.68
## 7 com gesso Pérola 15.10 0.7296 52 20.70 9.150e-27 13.64 16.56
## 8 sem gesso Pérola 15.86 0.7296 52 21.74 9.163e-28 14.40 17.33
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("Matéria orgânica"~(g~kg^{-1}))
p1 <-
segplot(cober~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
p2 <-
segplot(ges~lwr+upr|vari, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
l <- levels(lsmg$ges)
key <- list(##corner=c(0.97,0.97),
columns=2,
type="o", divide=1,
lines=list(lty=1, pch=1:length(l)),
text=list(l))
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE, key=key,
groups=lsmg$ges, f=0.1, pch=as.integer(lsmg$ges),
xlab=xlab, ylab="Cultivares de abacaxi",
panel=panel.segplotBy)
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
p3
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(ctc~cober|vari, groups=ges, data=da[!is.na(da$mg),],
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(ctc~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: ctc
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 1.68 0.560 2.80 0.05065 .
## vari 3 0.19 0.064 0.32 0.81060
## ges 1 0.14 0.142 0.71 0.40451
## cober 1 2.95 2.954 14.78 0.00038 ***
## vari:ges 3 0.19 0.062 0.31 0.81673
## vari:cober 3 0.64 0.212 1.06 0.37508
## ges:cober 1 0.33 0.329 1.65 0.20597
## vari:ges:cober 3 0.38 0.126 0.63 0.59847
## Residuals 45 9.00 0.200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari+ges+cober)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: ctc ~ blo + vari + ges + cober
## Model 2: ctc ~ blo + vari * ges * cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 55 10.5
## 2 45 9.0 10 1.53 0.77 0.66
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect=c("cober"))
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc
## cober estimate se df t.stat p.value lwr upr
## 1 com cobertura 3.472 0.07734 55 44.89 5.064e-45 3.317 3.627
## 2 sem cobertura 3.042 0.07734 55 39.34 5.840e-42 2.887 3.197
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect=c("ges"))
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg
## ges estimate se df t.stat p.value lwr upr
## 1 com gesso 3.304 0.07734 55 42.72 7.169e-44 3.149 3.459
## 2 sem gesso 3.210 0.07734 55 41.51 3.349e-43 3.055 3.365
## LSmeans(m0, effect="vari")
lsmv <- LSmeans(m1, effect=c("vari"))
lsmv <- cbind(lsmv$grid, lsmv$coef)
lsmv <- equallevels(lsmv, da)
lsmv
## vari estimate se df t.stat p.value lwr upr
## 1 Havai 3.292 0.1094 55 30.10 7.565e-36 3.073 3.511
## 2 IAC Fantástico 3.328 0.1094 55 30.42 4.325e-36 3.108 3.547
## 3 Imperial 3.216 0.1094 55 29.41 2.524e-35 2.997 3.435
## 4 Pérola 3.193 0.1094 55 29.19 3.703e-35 2.973 3.412
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("CTC efetiva"~(cmol[c]~dm^{-3}))
p1 <-
segplot(cober~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
p2 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmv, draw=FALSE,
xlab=xlab, ylab="Cultivares de abacaxi")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
p1
##-----------------------------------------------------------------------------
## Preliminar.
xyplot(v~cober|vari, groups=ges, data=da[!is.na(da$mg),],
jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))
##-----------------------------------------------------------------------------
## Modelo.
m0 <- lm(v~blo+vari*ges*cober, data=da)
## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)
layout(1)
## Quadro de anova.
anova(m0)
## Analysis of Variance Table
##
## Response: v
## Df Sum Sq Mean Sq F value Pr(>F)
## blo 3 118 39 3.29 0.029 *
## vari 3 65 22 1.80 0.160
## ges 1 406 406 34.01 5.5e-07 ***
## cober 1 589 589 49.31 9.5e-09 ***
## vari:ges 3 42 14 1.16 0.335
## vari:cober 3 23 8 0.64 0.594
## ges:cober 1 6 6 0.50 0.483
## vari:ges:cober 3 24 8 0.68 0.572
## Residuals 45 538 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##-----------------------------------------------------------------------------
## Modelo com interações abandonadas.
m1 <- update(m0, .~blo+vari+ges+cober)
anova(m1, m0)
## Analysis of Variance Table
##
## Model 1: v ~ blo + vari + ges + cober
## Model 2: v ~ blo + vari * ges * cober
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 55 633
## 2 45 538 10 94.8 0.79 0.64
##-----------------------------------------------------------------------------
## Comparações múltiplas.
## LSmeans(m0, effect="cober")
lsmc <- LSmeans(m1, effect=c("cober"))
lsmc <- cbind(lsmc$grid, lsmc$coef)
lsmc <- equallevels(lsmc, da)
lsmc
## cober estimate se df t.stat p.value lwr upr
## 1 com cobertura 67.99 0.5995 55 113.4 6.808e-67 66.79 69.19
## 2 sem cobertura 61.92 0.5995 55 103.3 1.137e-64 60.72 63.12
## LSmeans(m0, effect="ges")
lsmg <- LSmeans(m1, effect=c("ges"))
lsmg <- cbind(lsmg$grid, lsmg$coef)
lsmg <- equallevels(lsmg, da)
lsmg
## ges estimate se df t.stat p.value lwr upr
## 1 com gesso 67.48 0.5995 55 112.6 1.032e-66 66.27 68.68
## 2 sem gesso 62.44 0.5995 55 104.1 7.233e-65 61.23 63.64
## LSmeans(m0, effect="vari")
lsmv <- LSmeans(m1, effect=c("vari"))
lsmv <- cbind(lsmv$grid, lsmv$coef)
lsmv <- equallevels(lsmv, da)
lsmv
## vari estimate se df t.stat p.value lwr upr
## 1 Havai 64.81 0.8478 55 76.45 1.566e-57 63.11 66.51
## 2 IAC Fantástico 66.13 0.8478 55 78.00 5.235e-58 64.43 67.83
## 3 Imperial 65.47 0.8478 55 77.22 9.030e-58 63.77 67.17
## 4 Pérola 63.41 0.8478 55 74.80 5.147e-57 61.71 65.11
##-----------------------------------------------------------------------------
## Gráficos.
xlab <- expression("Saturação de bases (%)")
p1 <-
segplot(cober~lwr+upr, centers=estimate,
data=lsmc, draw=FALSE,
xlab=xlab, ylab="Cobertura do solo")
p2 <-
segplot(ges~lwr+upr, centers=estimate,
data=lsmg, draw=FALSE,
xlab=xlab, ylab="Aplicação de gesso")
p3 <-
segplot(vari~lwr+upr, centers=estimate,
data=lsmv, draw=FALSE,
xlab=xlab, ylab="Cultivares de abacaxi")
plot(p1, split=c(1,1,1,3), more=TRUE)
plot(p2, split=c(1,2,1,3), more=TRUE)
plot(p3, split=c(1,3,1,3), more=FALSE)
##-----------------------------------------------------------------------------
## Gráficos que serão colocados no artigo.
## fig.width=7, fig.height=3.5, dpi=124, units="cm"
## 10 cm, 1 cm = 2.42 pol, 300 pixels por inche.
## 300/2.42 ~= 124 pixel por cm.
plot(p1, split=c(1,1,2,1), more=TRUE)
plot(p2, split=c(2,1,2,1), more=FALSE)
