Files resulted of 'check()'.

buildPkg.R

@@ -41,10 +41,25 @@ build(manual = TRUE, vignettes = FALSE)
 # build the binary version for windows (not used)
 # build_win()
 
+##----------------------------------------------------------------------
+## Test installation.
+
 ## Test install with install.packages
 pkg <- paste0("../legTools_", packageVersion("legTools"), ".tar.gz")
 install.packages(pkg, repos = NULL)
 
+## Test using devtools::install_git().
+libTest <- "~/R/"
+if (file.exists(libTest)){
+    file.remove(libTest)
+}
+dir.create(path=libTest)
+
+.libPaths(new=libTest)
+
+install_git(url="http://git.leg.ufpr.br/leg/legTools.git",
+            branch="issue#9")
+
 ##======================================================================
 ## Sending package tarballs and manual to remote server to be
 ## downloadable

man/kornYield.Rd → man/cornYield.Rd

 % Generated by roxygen2 (4.1.1): do not edit by hand
 % Please edit documentation in R/legTools.R
 \docType{data}
-\name{kornYield}
-\alias{kornYield}
-\title{Korn yield as function of fertilization with NPK}
+\name{cornYield}
+\alias{cornYield}
+\title{Corn yield as function of fertilization with NPK}
 \format{a \code{data.frame} with 32 records and 4 variables.}
 \source{
 Frederico, P. (2009). Curso de Estatística Experimental (15th
     ed.). Piracicaba, São Paulo: FEALQ. (page 115)
 }
 \usage{
-data(kornYield)
+data(cornYield)
 }
 \description{
 These data are from an \eqn{2^3} factorial experiment
     studing the effect of Nitrogen (N), Phosporus (P) and Potassium
-    (K) on korn yield in a randomized block design.
+    (K) on corn yield in a randomized block design.
 
 \itemize{
   \item \code{block} a factor with 4 levels.
   \item \code{N} low (-1) and high (+1) levels of nitrogen.
   \item \code{P} low (-1) and high (+1) levels of phosporus.
   \item \code{K} low (-1) and high (+1) levels of potassium.
-  \item \code{yield} korn yield (ton/ha).
+  \item \code{yield} corn yield (ton/ha).
 }
 }
 \examples{
 library(lattice)
 library(latticeExtra)
 
-data(kornYield)
-str(kornYield)
+data(cornYield)
+str(cornYield)
 
 xyplot(yield~N|P, groups=K,
-       data=kornYield, type=c("p", "a"),
+       data=cornYield, type=c("p", "a"),
        ylab=expression(Yield~(ton~ha^{-1})),
        xlab="Nutrient level")
 
 xyplot(yield~N, groups=interaction(P, K),
-       data=kornYield, type=c("p", "a"),
+       data=cornYield, type=c("p", "a"),
        auto.key=list(columns=2),
        ylab=expression(Yield~(ton~ha^{-1})),
        xlab="Nutrient level")
-
-m0 <- lm(yield~block+(N+P+K)^3, data=kornYield)
-par(mfrow=c(2,2)); plot(m0); layout(1)
-anova(m0)
-
-m1 <- update(m0, .~block+N+K)
-par(mfrow=c(2,2)); plot(m1); layout(1)
-
-anova(m0, m1)
-anova(m1)
-
-summary(m1)
-
-pred <- expand.grid(block="1",
-                    N=seq(-1, 1, by=0.1),
-                    K=seq(-1, 1, by=0.1))
-pred$mu <- predict(m1, newdata=pred)
-
-wireframe(mu~N+K, data=pred,
-          scales=list(arrows=FALSE),
-          zlab=list(expression(Yield~(ton~ha^{-1})), rot=90),
-          drape=TRUE, cuts=20,
-          col.regions=colorRampPalette(
-              color=brewer.pal(n=11, name="Spectral"))(21))
-
-levelplot(mu~N+K, data=pred, aspect=1,
-          main=expression(Yield~(ton~ha^{-1})),
-          col.regions=colorRampPalette(
-              color=brewer.pal(n=11, name="Spectral")))
 }
 \keyword{datasets}
 

man/kornYield2.Rd → man/cornYield2.Rd

 % Generated by roxygen2 (4.1.1): do not edit by hand
 % Please edit documentation in R/legTools.R
 \docType{data}
-\name{kornYield2}
-\alias{kornYield2}
+\name{cornYield2}
+\alias{cornYield2}
 \title{Axial factorial NPK experiment with added treatments}
 \format{a \code{data.frame} with 9 records and 5 variables.}
 \source{
@@ -17,7 +17,7 @@ data(sugarcaneYield4)
 }
 \description{
 These data are from an axial 3 factorial experiment
-    studing NPK in the yield of korn. Tow controls were added, one is
+    studing NPK in the yield of corn. Tow controls were added, one is
     zer control (no NPK) and the other is central factorial point
     plus presence of limestone.
 
@@ -26,7 +26,7 @@ These data are from an axial 3 factorial experiment
   \item \code{P} content of phosphorus in the fertilizer.
   \item \code{K} content of potassium in the fertilizer.
   \item \code{limestone} presence (1) or absence of limestone (0).
-  \item \code{acid} mean of korn yield in 16 locations (ton/ha).
+  \item \code{acid} mean of corn yield in 16 locations (ton/ha).
 }
 }
 \details{
@@ -38,15 +38,15 @@ The experiment was caried out in 16 different locations but
 library(lattice)
 library(latticeExtra)
 
-data(kornYield2)
-str(kornYield2)
+data(cornYield2)
+str(cornYield2)
 
 ## Axial triple factorial with 2 controls.
-ftable(xtabs(~N+P+K, data=kornYield2))
+ftable(xtabs(~N+P+K, data=cornYield2))
 
 xyplot(yield~N+P+K,
        groups=as.integer(limestone==1 | (N+P+K)==0),
-       data=kornYield2, type=c("p", "a"),
+       data=cornYield2, type=c("p", "a"),
        auto.key=TRUE,
        ylab=expression(Yield~(ton~ha^{-1})),
        xlab="Nutrient content")

man/filterCake.Rd

@@ -40,23 +40,6 @@ xyplot(y~cake, groups=mineral,
        data=filterCake, type=c("p", "a"),
        ylab="y",
        xlab="Filter cake level")
-
-m0 <- lm(y~block+(cake+mineral)^2, data=filterCake)
-par(mfrow=c(2,2)); plot(m0); layout(1)
-anova(m0)
-
-summary(m0)
-
-filterCake$Mineral <- factor(filterCake$mineral,
-                             labels=c("absent", "present"))
-
-m1 <- aov(y~block+Mineral/cake, data=filterCake)
-anova(m1)
-
-## Split SS to see effect of cake in each level of mineral.
-summary(m1, split=list("Mineral:cake"=list("absent"=1, "present"=2)))
-
-summary.lm(m1)
 }
 \keyword{datasets}
 

man/polyGui.Rd

@@ -20,10 +20,11 @@ None is returned by the function.
 }
 \description{
 This function opens an interface to control the
-polynomial degree in linear regression. It shows the observed values
-and the corresponding fitted curve superimposed with confidence bands
-(for the fitted values) and also show the residuals plot. It assumes
-that \code{gWidgets} and \code{gWidgetstcltk} packages are available.
+    polynomial degree in linear regression. It shows the observed
+    values and the corresponding fitted curve superimposed with
+    confidence bands (for the fitted values) and also show the
+    residuals plot. It assumes that \code{gWidgets} and
+    \code{gWidgetstcltk} packages are available.
 }
 \examples{
 \donttest{

man/sugarcaneYield4.Rd

@@ -32,7 +32,6 @@ There is a missprint in the book for the 9th entry, which
 \examples{
 library(lattice)
 library(latticeExtra)
-library(multcomp)
 
 data(sugarcaneYield4)
 str(sugarcaneYield4)
@@ -44,55 +43,6 @@ xyplot(yield~N|P, groups=K,
        data=sugarcaneYield4, type=c("p", "a"),
        ylab=expression(Yield~(ton~ha^{-1})),
        xlab="Nitrogen level level")
-
-## Sums in each cell combination.
-addmargins(with(sugarcaneYield4, tapply(yield, list(P, N), FUN=sum)))
-addmargins(with(sugarcaneYield4, tapply(yield, list(K, N), FUN=sum)))
-addmargins(with(sugarcaneYield4, tapply(yield, list(K, P), FUN=sum)))
-
-sugarcaneYield4 <- transform(sugarcaneYield4,
-                             blockr=interaction(block, rept),
-                             nitro=factor(N),
-                             phosp=factor(P),
-                             potas=factor(K))
-str(sugarcaneYield4)
-
-m0 <- lm(yield~blockr+(nitro+phosp+potas)^3, data=sugarcaneYield4)
-par(mfrow=c(2,2)); plot(m0); layout(1)
-anova(m0)
-
-m1 <- update(m0, .~blockr+(nitro+phosp)^2)
-par(mfrow=c(2,2)); plot(m1); layout(1)
-
-anova(m0, m1)
-anova(m1)
-
-m2 <- aov(yield~blockr+nitro/phosp, data=sugarcaneYield4)
-anova(m2)
-
-PinN <- sapply(paste0("nitro", levels(sugarcaneYield4$nitro)),
-               FUN=grep, x=names(coef(m2))[m2$assign==3L],
-               simplify=FALSE)
-
-summary(m2, split=list("nitro:phosp"=PinN))
-
-X <- model.matrix(m1)
-X
-
-aggregate(X~nitro+phosp, data=sugarcaneYield4, FUN=mean)
-
-## It is better use multcomp::LSmatrix().
-L <- aggregate(X~nitro+phosp, data=sugarcaneYield4, FUN=mean)
-rownames(L) <- with(L, paste0("N", nitro, ":P", phosp))
-L <- as.matrix(L[, colnames(X)])
-str(L)
-
-## Least squares means for N:P combinations.
-L\%*\%coef(m1)
-
-g1 <- glht(m1, linfct=L)
-
-confint(g1, calpha=univariate_calpha())
 }
 \keyword{datasets}
 

man/vinasseFert.Rd

@@ -40,18 +40,6 @@ xyplot(y~vinasse, groups=mineral,
        data=vinasseFert, type=c("p", "a"),
        ylab="y",
        xlab="Vinasse level")
-
-m0 <- lm(y~block+(vinasse+mineral)^2, data=vinasseFert)
-par(mfrow=c(2,2)); plot(m0); layout(1)
-anova(m0)
-
-m1 <- update(m0, .~block+vinasse)
-par(mfrow=c(2,2)); plot(m1); layout(1)
-
-anova(m0, m1)
-anova(m1)
-
-summary(m1)
 }
 \keyword{datasets}
 

man/wgpigs.Rd → man/wgPigs.Rd

 % Generated by roxygen2 (4.1.1): do not edit by hand
 % Please edit documentation in R/legTools.R
 \docType{data}
-\name{wgpigs}
-\alias{wgpigs}
+\name{wgPigs}
+\alias{wgPigs}
 \title{Feeding type in pig weight gain}
 \format{a \code{data.frame} with 20 records and 2 variables.}
 \source{
@@ -10,7 +10,7 @@ Frederico, P. (2009). Curso de Estatística Experimental (15th
     ed.). Piracicaba, São Paulo: FEALQ. (page 62)
 }
 \usage{
-data(wgpigs)
+data(wgPigs)
 }
 \description{
 This is an artifial dataset corresponding a experiment
@@ -29,9 +29,9 @@ This is an artifial dataset corresponding a experiment
 }
 \examples{
 library(lattice)
-data(wgpigs)
+data(wgPigs)
 
-xyplot(wg~ft, data=wgpigs,
+xyplot(wg~ft, data=wgPigs,
        ylab="Weight gain (kg)",
        xlab="Feeding type")
 }

man/wgpigs2.Rd → man/wgPigs2.Rd

 % Generated by roxygen2 (4.1.1): do not edit by hand
 % Please edit documentation in R/legTools.R
 \docType{data}
-\name{wgpigs2}
-\alias{wgpigs2}
+\name{wgPigs2}
+\alias{wgPigs2}
 \title{Age of castration in pig weight gain}
 \format{a \code{data.frame} with 16 records and 4 variables.}
 \source{
@@ -10,7 +10,7 @@ Frederico, P. (2009). Curso de Estatística Experimental (15th
     ed.). Piracicaba, São Paulo: FEALQ. (page 110)
 }
 \usage{
-data(wgpigs2)
+data(wgPigs2)
 }
 \description{
 This is an artifial dataset corresponding a experiment
@@ -36,41 +36,12 @@ This is an artifial dataset corresponding a experiment
 \examples{
 library(lattice)
 
-data(wgpigs2)
-str(wgpigs2)
+data(wgPigs2)
+str(wgPigs2)
 
-xyplot(wg~age, data=wgpigs2, groups=litter,
+xyplot(wg~age, data=wgPigs2, groups=litter,
        ylab="Weight gain (kg)",
        xlab="Age at castration (days)")
-
-m0 <- lm(wg~litter+size+age, data=wgpigs2)
-par(mfrow=c(2,2)); plot(m0); layout(1)
-anova(m0)
-
-summary(m0)
-
-library(multcomp)
-summary(glht(m0, linfct=mcp(age="Dunnet")),
-        test=adjusted(type="single-step"))
-
-m1 <- glm(wg~litter+size+age, data=wgpigs2, family=Gamma)
-m2 <- glm(wg~litter+size+age, data=wgpigs2,
-          family=Gamma(link="log"))
-m3 <- glm(wg~litter+size+age, data=wgpigs2,
-          family=Gamma(link="identity"))
-
-rbind(logLik(m0),
-      logLik(m1),
-      logLik(m2),
-      logLik(m3))
-
-par(mfrow=c(2,2)); plot(m1); layout(1)
-anova(m1, test="F")
-anova(m2, test="F")
-anova(m3, test="F")
-
-summary(glht(m3, linfct=mcp(age="Dunnet")),
-        test=adjusted(type="single-step"))
 }
 \keyword{datasets}