Atributos químicos do solo sob cultivo de abacaxi em função da aplicação de gesso e cobertura do solo

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

##-----------------------------------------------------------------------------
## 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.

Leitura e análise preliminar dos dados

##-----------------------------------------------------------------------------
## 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)))
##       })

pH

##-----------------------------------------------------------------------------
## Preliminar.

xyplot(ph~cober|vari, groups=ges, data=da,
       jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))

plot of chunk unnamed-chunk-5

##-----------------------------------------------------------------------------
## Modelo.

m0 <- lm(ph~blo+vari*ges*cober, data=da)

## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)

plot of chunk unnamed-chunk-6

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)

plot of chunk unnamed-chunk-7

##-----------------------------------------------------------------------------
## 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)

plot of chunk unnamed-chunk-8


Fósforo (P)

##-----------------------------------------------------------------------------
## Preliminar.

xyplot(log(p)~cober|vari, groups=ges, data=da,
       jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))

plot of chunk unnamed-chunk-9

##-----------------------------------------------------------------------------
## Modelo.

m0 <- lm(p~blo+vari*ges*cober, data=da)

## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)

plot of chunk unnamed-chunk-10

layout(1)

MASS::boxcox(m0)
abline(v=1/3, col=2)

plot of chunk unnamed-chunk-10

m0 <- lm(p^(1/3)~blo+vari*ges*cober, data=da)

## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)

plot of chunk unnamed-chunk-10

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)

plot of chunk unnamed-chunk-11

##-----------------------------------------------------------------------------
## 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)

plot of chunk unnamed-chunk-12


Potássio (K)

##-----------------------------------------------------------------------------
## Preliminar.

xyplot(k~cober|vari, groups=ges, data=da,
       jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))

plot of chunk unnamed-chunk-13

##-----------------------------------------------------------------------------
## Modelo.

m0 <- lm(k~blo+vari*ges*cober, data=da)

## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)

plot of chunk unnamed-chunk-14

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)

plot of chunk unnamed-chunk-15

##-----------------------------------------------------------------------------
## 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)

plot of chunk unnamed-chunk-16


Cálcio (Ca)

##-----------------------------------------------------------------------------
## Preliminar.

xyplot(ca~cober|vari, groups=ges, data=da,
       jitter.x=TRUE, type=c("p","a"), layout=c(NA,1))

plot of chunk unnamed-chunk-17

##-----------------------------------------------------------------------------
## Modelo.

m0 <- lm(ca~blo+vari*ges*cober, data=da)

## Diagnóstico dos resíduos.
par(mfrow=c(2,2))
plot(m0)