Modelos de Regressão e aplicações no ambiente R

13 a 17 de Abril de 2015 - Manaus - AM
Prof. Dr. Walmes M. Zeviani
Fundação Oswaldo Cruz - FIOCRUZ
Lab. de Estatística e Geoinformação - LEG
Departamento de Estatística - UFPR


Leitura de dados e análise gráfica exploratória


Análise para um par de variáveis

##-----------------------------------------------------------------------------
## Definições da sessão

pkg <- c("latticeExtra", "doBy", "plyr", "reshape", "reshape2",
         "alr3", "nlrwr", "faraway", "car")
sapply(pkg, require, character.only=TRUE) 
##-----------------------------------------------------------------------------
## ALWINS (pg 708): O número de vitórias de um time de basebol está
## relacionado ao batting?

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/ALWINS.DAT"
download.file(url=url, destfile=basename(url))

da <- read.fwf(url, widths=c(12,11,4),
               colClasses=c("character",NA,NA))
str(da)
## 'data.frame':    14 obs. of  3 variables:
##  $ V1: chr  "New York    " "Toronto     " "Baltimore   " "Boston      " ...
##  $ V2: num  103 78 67 93 55 74 55 81 62 94 ...
##  $ V3: num  0.275 0.261 0.246 0.277 0.253 0.249 0.248 0.268 0.256 0.272 ...
names(da) <- c("team", "gamesWon","battingAve")
da
##            team gamesWon battingAve
## 1  New York          103      0.275
## 2  Toronto            78      0.261
## 3  Baltimore          67      0.246
## 4  Boston             93      0.277
## 5  Tampa Bay          55      0.253
## 6  Cleveland          74      0.249
## 7  Detroit            55      0.248
## 8  Chicago            81      0.268
## 9  Kansas City        62      0.256
## 10 Minnesota          94      0.272
## 11 Anaheim            99      0.282
## 12 Texas              72      0.269
## 13 Seattle            93      0.275
## 14 Oakland           103      0.261
## Remover espaços de strings: gdata::trim(), stringr::str_trim().
da$team <- gsub(x=da$team, pattern="\\s+$", replacement="")
da
##           team gamesWon battingAve
## 1     New York      103      0.275
## 2      Toronto       78      0.261
## 3    Baltimore       67      0.246
## 4       Boston       93      0.277
## 5    Tampa Bay       55      0.253
## 6    Cleveland       74      0.249
## 7      Detroit       55      0.248
## 8      Chicago       81      0.268
## 9  Kansas City       62      0.256
## 10   Minnesota       94      0.272
## 11     Anaheim       99      0.282
## 12       Texas       72      0.269
## 13     Seattle       93      0.275
## 14     Oakland      103      0.261
plot(gamesWon~battingAve, data=da,
     xlab="Batting Average", ylab="Vitórias")

## Cores do R.
colors()
##   [1] "white"                "aliceblue"            "antiquewhite"        
##   [4] "antiquewhite1"        "antiquewhite2"        "antiquewhite3"       
##   [7] "antiquewhite4"        "aquamarine"           "aquamarine1"         
##  [10] "aquamarine2"          "aquamarine3"          "aquamarine4"         
##  [13] "azure"                "azure1"               "azure2"              
##  [16] "azure3"               "azure4"               "beige"               
##  [19] "bisque"               "bisque1"              "bisque2"             
##  [22] "bisque3"              "bisque4"              "black"               
##  [25] "blanchedalmond"       "blue"                 "blue1"               
##  [28] "blue2"                "blue3"                "blue4"               
##  [31] "blueviolet"           "brown"                "brown1"              
##  [34] "brown2"               "brown3"               "brown4"              
##  [37] "burlywood"            "burlywood1"           "burlywood2"          
##  [40] "burlywood3"           "burlywood4"           "cadetblue"           
##  [43] "cadetblue1"           "cadetblue2"           "cadetblue3"          
##  [46] "cadetblue4"           "chartreuse"           "chartreuse1"         
##  [49] "chartreuse2"          "chartreuse3"          "chartreuse4"         
##  [52] "chocolate"            "chocolate1"           "chocolate2"          
##  [55] "chocolate3"           "chocolate4"           "coral"               
##  [58] "coral1"               "coral2"               "coral3"              
##  [61] "coral4"               "cornflowerblue"       "cornsilk"            
##  [64] "cornsilk1"            "cornsilk2"            "cornsilk3"           
##  [67] "cornsilk4"            "cyan"                 "cyan1"               
##  [70] "cyan2"                "cyan3"                "cyan4"               
##  [73] "darkblue"             "darkcyan"             "darkgoldenrod"       
##  [76] "darkgoldenrod1"       "darkgoldenrod2"       "darkgoldenrod3"      
##  [79] "darkgoldenrod4"       "darkgray"             "darkgreen"           
##  [82] "darkgrey"             "darkkhaki"            "darkmagenta"         
##  [85] "darkolivegreen"       "darkolivegreen1"      "darkolivegreen2"     
##  [88] "darkolivegreen3"      "darkolivegreen4"      "darkorange"          
##  [91] "darkorange1"          "darkorange2"          "darkorange3"         
##  [94] "darkorange4"          "darkorchid"           "darkorchid1"         
##  [97] "darkorchid2"          "darkorchid3"          "darkorchid4"         
## [100] "darkred"              "darksalmon"           "darkseagreen"        
## [103] "darkseagreen1"        "darkseagreen2"        "darkseagreen3"       
## [106] "darkseagreen4"        "darkslateblue"        "darkslategray"       
## [109] "darkslategray1"       "darkslategray2"       "darkslategray3"      
## [112] "darkslategray4"       "darkslategrey"        "darkturquoise"       
## [115] "darkviolet"           "deeppink"             "deeppink1"           
## [118] "deeppink2"            "deeppink3"            "deeppink4"           
## [121] "deepskyblue"          "deepskyblue1"         "deepskyblue2"        
## [124] "deepskyblue3"         "deepskyblue4"         "dimgray"             
## [127] "dimgrey"              "dodgerblue"           "dodgerblue1"         
## [130] "dodgerblue2"          "dodgerblue3"          "dodgerblue4"         
## [133] "firebrick"            "firebrick1"           "firebrick2"          
## [136] "firebrick3"           "firebrick4"           "floralwhite"         
## [139] "forestgreen"          "gainsboro"            "ghostwhite"          
## [142] "gold"                 "gold1"                "gold2"               
## [145] "gold3"                "gold4"                "goldenrod"           
## [148] "goldenrod1"           "goldenrod2"           "goldenrod3"          
## [151] "goldenrod4"           "gray"                 "gray0"               
## [154] "gray1"                "gray2"                "gray3"               
## [157] "gray4"                "gray5"                "gray6"               
## [160] "gray7"                "gray8"                "gray9"               
## [163] "gray10"               "gray11"               "gray12"              
## [166] "gray13"               "gray14"               "gray15"              
## [169] "gray16"               "gray17"               "gray18"              
## [172] "gray19"               "gray20"               "gray21"              
## [175] "gray22"               "gray23"               "gray24"              
## [178] "gray25"               "gray26"               "gray27"              
## [181] "gray28"               "gray29"               "gray30"              
## [184] "gray31"               "gray32"               "gray33"              
## [187] "gray34"               "gray35"               "gray36"              
## [190] "gray37"               "gray38"               "gray39"              
## [193] "gray40"               "gray41"               "gray42"              
## [196] "gray43"               "gray44"               "gray45"              
## [199] "gray46"               "gray47"               "gray48"              
## [202] "gray49"               "gray50"               "gray51"              
## [205] "gray52"               "gray53"               "gray54"              
## [208] "gray55"               "gray56"               "gray57"              
## [211] "gray58"               "gray59"               "gray60"              
## [214] "gray61"               "gray62"               "gray63"              
## [217] "gray64"               "gray65"               "gray66"              
## [220] "gray67"               "gray68"               "gray69"              
## [223] "gray70"               "gray71"               "gray72"              
## [226] "gray73"               "gray74"               "gray75"              
## [229] "gray76"               "gray77"               "gray78"              
## [232] "gray79"               "gray80"               "gray81"              
## [235] "gray82"               "gray83"               "gray84"              
## [238] "gray85"               "gray86"               "gray87"              
## [241] "gray88"               "gray89"               "gray90"              
## [244] "gray91"               "gray92"               "gray93"              
## [247] "gray94"               "gray95"               "gray96"              
## [250] "gray97"               "gray98"               "gray99"              
## [253] "gray100"              "green"                "green1"              
## [256] "green2"               "green3"               "green4"              
## [259] "greenyellow"          "grey"                 "grey0"               
## [262] "grey1"                "grey2"                "grey3"               
## [265] "grey4"                "grey5"                "grey6"               
## [268] "grey7"                "grey8"                "grey9"               
## [271] "grey10"               "grey11"               "grey12"              
## [274] "grey13"               "grey14"               "grey15"              
## [277] "grey16"               "grey17"               "grey18"              
## [280] "grey19"               "grey20"               "grey21"              
## [283] "grey22"               "grey23"               "grey24"              
## [286] "grey25"               "grey26"               "grey27"              
## [289] "grey28"               "grey29"               "grey30"              
## [292] "grey31"               "grey32"               "grey33"              
## [295] "grey34"               "grey35"               "grey36"              
## [298] "grey37"               "grey38"               "grey39"              
## [301] "grey40"               "grey41"               "grey42"              
## [304] "grey43"               "grey44"               "grey45"              
## [307] "grey46"               "grey47"               "grey48"              
## [310] "grey49"               "grey50"               "grey51"              
## [313] "grey52"               "grey53"               "grey54"              
## [316] "grey55"               "grey56"               "grey57"              
## [319] "grey58"               "grey59"               "grey60"              
## [322] "grey61"               "grey62"               "grey63"              
## [325] "grey64"               "grey65"               "grey66"              
## [328] "grey67"               "grey68"               "grey69"              
## [331] "grey70"               "grey71"               "grey72"              
## [334] "grey73"               "grey74"               "grey75"              
## [337] "grey76"               "grey77"               "grey78"              
## [340] "grey79"               "grey80"               "grey81"              
## [343] "grey82"               "grey83"               "grey84"              
## [346] "grey85"               "grey86"               "grey87"              
## [349] "grey88"               "grey89"               "grey90"              
## [352] "grey91"               "grey92"               "grey93"              
## [355] "grey94"               "grey95"               "grey96"              
## [358] "grey97"               "grey98"               "grey99"              
## [361] "grey100"              "honeydew"             "honeydew1"           
## [364] "honeydew2"            "honeydew3"            "honeydew4"           
## [367] "hotpink"              "hotpink1"             "hotpink2"            
## [370] "hotpink3"             "hotpink4"             "indianred"           
## [373] "indianred1"           "indianred2"           "indianred3"          
## [376] "indianred4"           "ivory"                "ivory1"              
## [379] "ivory2"               "ivory3"               "ivory4"              
## [382] "khaki"                "khaki1"               "khaki2"              
## [385] "khaki3"               "khaki4"               "lavender"            
## [388] "lavenderblush"        "lavenderblush1"       "lavenderblush2"      
## [391] "lavenderblush3"       "lavenderblush4"       "lawngreen"           
## [394] "lemonchiffon"         "lemonchiffon1"        "lemonchiffon2"       
## [397] "lemonchiffon3"        "lemonchiffon4"        "lightblue"           
## [400] "lightblue1"           "lightblue2"           "lightblue3"          
## [403] "lightblue4"           "lightcoral"           "lightcyan"           
## [406] "lightcyan1"           "lightcyan2"           "lightcyan3"          
## [409] "lightcyan4"           "lightgoldenrod"       "lightgoldenrod1"     
## [412] "lightgoldenrod2"      "lightgoldenrod3"      "lightgoldenrod4"     
## [415] "lightgoldenrodyellow" "lightgray"            "lightgreen"          
## [418] "lightgrey"            "lightpink"            "lightpink1"          
## [421] "lightpink2"           "lightpink3"           "lightpink4"          
## [424] "lightsalmon"          "lightsalmon1"         "lightsalmon2"        
## [427] "lightsalmon3"         "lightsalmon4"         "lightseagreen"       
## [430] "lightskyblue"         "lightskyblue1"        "lightskyblue2"       
## [433] "lightskyblue3"        "lightskyblue4"        "lightslateblue"      
## [436] "lightslategray"       "lightslategrey"       "lightsteelblue"      
## [439] "lightsteelblue1"      "lightsteelblue2"      "lightsteelblue3"     
## [442] "lightsteelblue4"      "lightyellow"          "lightyellow1"        
## [445] "lightyellow2"         "lightyellow3"         "lightyellow4"        
## [448] "limegreen"            "linen"                "magenta"             
## [451] "magenta1"             "magenta2"             "magenta3"            
## [454] "magenta4"             "maroon"               "maroon1"             
## [457] "maroon2"              "maroon3"              "maroon4"             
## [460] "mediumaquamarine"     "mediumblue"           "mediumorchid"        
## [463] "mediumorchid1"        "mediumorchid2"        "mediumorchid3"       
## [466] "mediumorchid4"        "mediumpurple"         "mediumpurple1"       
## [469] "mediumpurple2"        "mediumpurple3"        "mediumpurple4"       
## [472] "mediumseagreen"       "mediumslateblue"      "mediumspringgreen"   
## [475] "mediumturquoise"      "mediumvioletred"      "midnightblue"        
## [478] "mintcream"            "mistyrose"            "mistyrose1"          
## [481] "mistyrose2"           "mistyrose3"           "mistyrose4"          
## [484] "moccasin"             "navajowhite"          "navajowhite1"        
## [487] "navajowhite2"         "navajowhite3"         "navajowhite4"        
## [490] "navy"                 "navyblue"             "oldlace"             
## [493] "olivedrab"            "olivedrab1"           "olivedrab2"          
## [496] "olivedrab3"           "olivedrab4"           "orange"              
## [499] "orange1"              "orange2"              "orange3"             
## [502] "orange4"              "orangered"            "orangered1"          
## [505] "orangered2"           "orangered3"           "orangered4"          
## [508] "orchid"               "orchid1"              "orchid2"             
## [511] "orchid3"              "orchid4"              "palegoldenrod"       
## [514] "palegreen"            "palegreen1"           "palegreen2"          
## [517] "palegreen3"           "palegreen4"           "paleturquoise"       
## [520] "paleturquoise1"       "paleturquoise2"       "paleturquoise3"      
## [523] "paleturquoise4"       "palevioletred"        "palevioletred1"      
## [526] "palevioletred2"       "palevioletred3"       "palevioletred4"      
## [529] "papayawhip"           "peachpuff"            "peachpuff1"          
## [532] "peachpuff2"           "peachpuff3"           "peachpuff4"          
## [535] "peru"                 "pink"                 "pink1"               
## [538] "pink2"                "pink3"                "pink4"               
## [541] "plum"                 "plum1"                "plum2"               
## [544] "plum3"                "plum4"                "powderblue"          
## [547] "purple"               "purple1"              "purple2"             
## [550] "purple3"              "purple4"              "red"                 
## [553] "red1"                 "red2"                 "red3"                
## [556] "red4"                 "rosybrown"            "rosybrown1"          
## [559] "rosybrown2"           "rosybrown3"           "rosybrown4"          
## [562] "royalblue"            "royalblue1"           "royalblue2"          
## [565] "royalblue3"           "royalblue4"           "saddlebrown"         
## [568] "salmon"               "salmon1"              "salmon2"             
## [571] "salmon3"              "salmon4"              "sandybrown"          
## [574] "seagreen"             "seagreen1"            "seagreen2"           
## [577] "seagreen3"            "seagreen4"            "seashell"            
## [580] "seashell1"            "seashell2"            "seashell3"           
## [583] "seashell4"            "sienna"               "sienna1"             
## [586] "sienna2"              "sienna3"              "sienna4"             
## [589] "skyblue"              "skyblue1"             "skyblue2"            
## [592] "skyblue3"             "skyblue4"             "slateblue"           
## [595] "slateblue1"           "slateblue2"           "slateblue3"          
## [598] "slateblue4"           "slategray"            "slategray1"          
## [601] "slategray2"           "slategray3"           "slategray4"          
## [604] "slategrey"            "snow"                 "snow1"               
## [607] "snow2"                "snow3"                "snow4"               
## [610] "springgreen"          "springgreen1"         "springgreen2"        
## [613] "springgreen3"         "springgreen4"         "steelblue"           
## [616] "steelblue1"           "steelblue2"           "steelblue3"          
## [619] "steelblue4"           "tan"                  "tan1"                
## [622] "tan2"                 "tan3"                 "tan4"                
## [625] "thistle"              "thistle1"             "thistle2"            
## [628] "thistle3"             "thistle4"             "tomato"              
## [631] "tomato1"              "tomato2"              "tomato3"             
## [634] "tomato4"              "turquoise"            "turquoise1"          
## [637] "turquoise2"           "turquoise3"           "turquoise4"          
## [640] "violet"               "violetred"            "violetred1"          
## [643] "violetred2"           "violetred3"           "violetred4"          
## [646] "wheat"                "wheat1"               "wheat2"              
## [649] "wheat3"               "wheat4"               "whitesmoke"          
## [652] "yellow"               "yellow1"              "yellow2"             
## [655] "yellow3"              "yellow4"              "yellowgreen"
plot(gamesWon~battingAve, data=da,
     xlab="Batting Average", ylab="Vitórias",
     col="tomato3",
     pch=8,
     cex=1.5,
     sub="Fonte: Bussiness and Economics",
     main="Diagrama de dispersão")
abline(h=seq(from=60, to=100, by=10), lty=2, col="gray")
abline(v=seq(from=0.245, to=0.280, by=0.005), lty=2, col="gray")
legend("bottomright", legend="Observações", pch=8)
legend(x=0.245, y=112.5, legend="Observações", pch=8, xpd=TRUE)

## locator()

xyplot(gamesWon~battingAve, data=da,
       xlab="Batting Average", ylab="Vitórias")

xyplot(gamesWon~battingAve, data=da,
       xlab="Batting Average", ylab="Vitórias",
       type=c("p","g"),
       col="seagreen",
       pch=19,
       cex=1.2,
       sub="Fonte: Bussiness and Economics",
       main="Diagrama de dispersão")

xyplot(gamesWon~battingAve, data=da,
       type=c("p","smooth","g"),
       xlab="Batting Average", ylab="Vitórias")

##-----------------------------------------------------------------------------
## DIAMONDS (pg 707): Dados sobre 308 diamantes à venda. Verifique a
## relação entre preço (dolares) e tamanho (number of carats).

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/DIAMONDS.DAT"
## da <- read.table(url, header=FALSE)
## str(da)

## Lê apenas as colunas de interesse.
da <- read.table(url, header=FALSE,
                 colClasses=c("numeric","NULL","NULL","NULL","integer"))
names(da) <- c("carat", "price")
str(da)
## 'data.frame':    308 obs. of  2 variables:
##  $ carat: num  0.3 0.3 0.3 0.3 0.31 0.31 0.31 0.31 0.31 0.31 ...
##  $ price: int  1302 1510 1510 1260 1641 1555 1427 1427 1126 1126 ...
xlab <- "Tamanho do diamante"
ylab <- "Preço (U$)"

xyplot(price~carat, data=da, xlab=xlab, ylab=ylab)

xyplot(price~carat, data=da, xlab=xlab, ylab=ylab, type=c("p","smooth"))

## Usando o update().
p0 <- xyplot(price~carat, data=da)
update(p0, type=c("p","smooth"))

## Escala log10 para y.
xyplot(price~carat, data=da, xlab=xlab, ylab=ylab,
       type=c("p","smooth", "g"),
       scales=list(y=list(log=10)))

## Opções: yscale.components.{log,log10.3,logpower}.
xyplot(price~carat, data=da, xlab=xlab, ylab=ylab,
       type=c("p","smooth", "g"),
       scales=list(y=list(log=10)),
       yscale.components=yscale.components.log10.3)

Exercícios

##-----------------------------------------------------------------------------
## OJUICE (pg 709): O índice de docura do suco de laranja está
## relacionado com a concentração de pectina?

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/OJUICE.DAT"
da <- read.fwf(url, widths=c(-12,12,3))
names(da) <- c("SweetnessIndex", "pectin")
str(da)

xyplot(SweetnessIndex~pectin, data=da, type=c("p","smooth","g"))

xyplot(SweetnessIndex~log(pectin), data=da, type=c("p","smooth","g"))
xyplot(SweetnessIndex~sqrt(pectin), data=da, type=c("p","smooth","g"))
xyplot(SweetnessIndex~pectin^(1/3), data=da, type=c("p","smooth","g"))

xyplot(log(SweetnessIndex)~log(pectin), data=da, type=c("p","smooth","g"))


##-----------------------------------------------------------------------------
## GASOIL (pg 710): Dados sobre o preço da gasolina (cents/gallon) e do
## barril de óleo cru (refiner acquisition cost, U$/bbl) para o período
## de 1980-2001. Explora e relação entre preço da gasolina (y) e do óleo
## cru (x).

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/GASOIL.DAT"
da <- read.fwf(url, widths=c(16,8,6))
names(da) <- c("year", "gasoline", "crudeOil")
str(da)

xyplot(gasoline~crudeOil, data=da, type=c("p","smooth","g"))
xyplot(gasoline~year, data=da, type=c("p","smooth","g"))
xyplot(crudeOil~year, data=da, type=c("p","smooth","g"))

xyplot(gasoline+crudeOil~year, data=da, type=c("p","smooth","g"),
       auto.key=TRUE)

## Dois eixos y.
p1 <- xyplot(gasoline~year, data=da, type=c("p","smooth"))
p2 <- xyplot(crudeOil~year, data=da, type=c("p","smooth"))
doubleYScale(p1, p2)

##-----------------------------------------------------------------------------
## FERTRATE (pg 735): Dados sobre a taxa de fertilidade (y) e
## prevalencia de contraceptivos (x) para 27 Estados dos EUA.

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/FERTRATE.DAT"
da <- read.fwf(url, widths=c(36,3))
names(da) <- c("contrPrev", "FertRate")
str(da)

xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))

##-----------------------------------------------------------------------------
## Exportando.

getwd()

jpeg(filename="figura1.jpeg", width=800, height=600, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()

jpeg(filename="figura1.jpeg", width=1600, height=1200, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()

png(filename="figura1.png", width=1600, height=1200, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()

pdf(file="figura1.pdf", w=7, h=5)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()

tiff(filename="figura1.tiff", width=1600, height=1200, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()

Mais de um par de variáveis

##-----------------------------------------------------------------------------
## COLLGPA.

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/COLLGPA.DAT"
da <- read.fwf(url, widths=c(4,3,4))
names(da) <- c("verbal", "math", "gpa")
str(da)
## 'data.frame':    40 obs. of  3 variables:
##  $ verbal: num  81 68 57 100 54 82 75 58 55 49 ...
##  $ math  : num  87 99 86 49 83 86 74 98 54 81 ...
##  $ gpa   : num  3.49 2.89 2.73 1.54 2.56 3.43 3.59 2.86 1.46 2.11 ...
##-----------------------------------------------------------------------------
## FTC.

url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXAMPLES/FTC.DAT"
da <- read.fwf(url, widths=c(8,8,8,8))
names(da) <- c("tar","nicotine","weight","carbonMono")
str(da)
## 'data.frame':    25 obs. of  4 variables:
##  $ tar       : num  14.1 16 29.8 8 4.1 15 8.8 12.4 16.6 14.9 ...
##  $ nicotine  : num  0.86 1.06 2.03 0.67 0.4 1.04 0.76 0.95 1.12 1.02 ...
##  $ weight    : num  0.985 1.094 1.165 0.928 0.946 ...
##  $ carbonMono: num  13.6 16.6 23.5 10.2 5.4 15 9 12.3 16.3 15.4 ...
##-----------------------------------------------------------------------------
## rock.
##-----------------------------------------------------------------------------
## Informações da sessão.

Sys.time()
## [1] "2015-04-13 11:59:10 BRT"
sessionInfo()
## R version 3.1.2 (2014-10-31)
## Platform: i686-pc-linux-gnu (32-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] methods   splines   stats     graphics  grDevices utils     datasets  base     
## 
## other attached packages:
##  [1] faraway_1.0.6       nlrwr_1.1-0         sandwich_2.3-2      NRAIA_0.9-8        
##  [5] nlstools_1.0-0      nls2_0.2            proto_0.3-10        NISTnls_0.9-13     
##  [9] lmtest_0.9-33       zoo_1.7-11          HydroMe_2.0         minpack.lm_1.1-8   
## [13] nlme_3.1-119        drc_2.3-96          plotrix_3.5-10      magic_1.5-6        
## [17] abind_1.4-0         MASS_7.3-37         gtools_3.4.1        alr3_2.0.5         
## [21] car_2.0-22          reshape2_1.4        reshape_0.8.5       plyr_1.8.1         
## [25] doBy_4.5-12         survival_2.37-7     latticeExtra_0.6-26 lattice_0.20-29    
## [29] RColorBrewer_1.0-5  rmarkdown_0.3.3     knitr_1.8          
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.4    evaluate_0.5.5  formatR_1.0     grid_3.1.2      htmltools_0.2.6
##  [6] Matrix_1.1-5    nnet_7.3-8      Rcpp_0.11.3     stringr_0.6.2   tools_3.1.2    
## [11] yaml_2.1.13