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diff --git a/data/potatoYield2.RData b/data/potatoYield2.RData
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diff --git a/man/castorbeansYield.Rd b/man/castorbeansYield.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..9aeb846063aa4bf7552b81b2b1f9220a6c95b93a
--- /dev/null
+++ b/man/castorbeansYield.Rd
@@ -0,0 +1,61 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/legTools.R
+\docType{data}
+\name{castorbeansYield}
+\alias{castorbeansYield}
+\title{Castor beans variety competition experiments in some locations}
+\format{a \code{data.frame} with 45 records and 3 variables.}
+\source{
+Pimentel Gomes, F. (2009). Curso de Estatística Experimental
+ (15th ed.). Piracicaba, São Paulo: FEALQ. (page 149)
+
+Souza, O. Ferreira de.; Canecchio, F. V. (1952). Melhoramento de
+ mamoeira, VII. Bragantia 12:301-307.
+}
+\usage{
+data(castorbeansYield)
+}
+\description{
+These data are from a set of experiments evaluating
+ varieties of castor beans in terms of yield (kg/ha) for some
+ locations (counties).
+
+\itemize{
+
+\item \code{variety} a categorical unordered factor with 8 levels,
+ varieties and lines of castor beans.
+
+\item \code{loc} a categorical unordered factor with 5 levels, the
+ locations (counties) experimental stations.
+
+\item \code{meanYield} is the mean of yield for a variety in each
+ location. So, this the mean across all plots of the same variety
+ in each experiment.
+
+}
+}
+\details{
+The data in the book was not complete because doesn't report
+ individual plot values but the mean for a variety in each single
+ experiment. Neither mention which experimental design was used in
+ each station. The book report the Mean Square Error estimates for
+ each experiment. These values as provided as an attribute of the
+ object, \code{attr(peanut, "MSE")} and they comes from the ANOVA
+ table corresponding to an appropriate model for each
+ location. With these MSE is possible use them in a such a way
+ that a partial ANOVA table can be obtained to test the effect of
+ location, variety and its interaction.
+}
+\examples{
+require(lattice)
+
+data(castorbeansYield)
+str(castorbeansYield)
+
+xyplot(meanYield~variety, data=castorbeansYield,
+ groups=loc, type="o",
+ ylab=expression(Yield~(t~ha^{-1})),
+ xlab="Variety")
+}
+\keyword{datasets}
+
diff --git a/man/peanutYield.Rd b/man/peanutYield.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..5feba59ba058a6446f2d0f755a249e6d68b91648
--- /dev/null
+++ b/man/peanutYield.Rd
@@ -0,0 +1,63 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/legTools.R
+\docType{data}
+\name{peanutYield}
+\alias{peanutYield}
+\title{Peanut variety competition experiments in some locations and
+ years}
+\format{a \code{data.frame} with 36 records and 4 variables.}
+\source{
+Pimentel Gomes, F. (2009). Curso de Estatística Experimental
+ (15th ed.). Piracicaba, São Paulo: FEALQ. (page 150)
+
+Souza, O. Ferreira de.; Abramides, Eduardo. (1952). Ensaios de
+ variedades de amendoim. Bragantia 12:349-358.
+}
+\usage{
+data(peanutYield)
+}
+\description{
+These data are from a set of experiments evaluating
+ varieties of peanut in terms of yield (kg/ha) for some locations
+ and years.
+
+\itemize{
+
+\item \code{variety} a categorical unordered factor with 4 levels,
+ peanut varieties.
+
+\item \code{loc} a categorical unordered factor with 3 levels, the
+ locations (counties) of the experimental stations.
+
+\item \code{year} a categorical factor, the crop year.
+
+\item \code{meanYield} is the adjusted mean of yield for a variety in
+ each location and year.
+
+}
+}
+\details{
+The data in the book was not complete because doesn't report
+ individual plot values but the adjusted mean for a variety in
+ each single experiment. Neither mention which experimental design
+ was used in each station. The book report the Mean Square Error
+ estimates for each experiment. These values as provided as an
+ attribute of the object, \code{attr(peanut, "MSE")} and they
+ comes from the ANOVA table corresponding to an appropriate model
+ for each location. With these MSE is possible use them in a such
+ a way that a partial ANOVA table can be obtained to test the
+ effect of location, variety and its interaction.
+}
+\examples{
+require(lattice)
+
+data(peanutYield)
+str(peanutYield)
+
+xyplot(meanYield~variety|year, data=peanutYield,
+ groups=loc, type="o",
+ ylab=expression(Yield~(t~ha^{-1})),
+ xlab="Variety")
+}
+\keyword{datasets}
+
diff --git a/man/peanutYield2.Rd b/man/peanutYield2.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ebd061cded8fbde9843ec256504ca657d62e58ff
--- /dev/null
+++ b/man/peanutYield2.Rd
@@ -0,0 +1,60 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/legTools.R
+\docType{data}
+\name{peanutYield2}
+\alias{peanutYield2}
+\title{Peanut variety competition experiments in some locations}
+\format{a \code{data.frame} with 16 records and 3 variables.}
+\source{
+Pimentel Gomes, F. (2009). Curso de Estatística Experimental
+ (15th ed.). Piracicaba, São Paulo: FEALQ. (page 150)
+
+Souza, O. Ferreira de.; Abramides, Eduardo. (1952). Ensaios de
+ variedades de amendoim. Bragantia 12:349-358.
+}
+\usage{
+data(peanutYield2)
+}
+\description{
+These data are from a set of experiments evaluating
+ varieties of peanut in terms of yield (kg/ha) for some locations
+ in different years.
+
+\itemize{
+
+\item \code{variety} a categorical unordered factor with 4 levels,
+ peanut varieties.
+
+\item \code{loc} a categorical unordered factor with 4 levels, the
+ location:year of the experiment.
+
+\item \code{meanYield} is mean of yield for a variety in each
+ location:year.
+
+}
+}
+\details{
+The data in the book was not complete because doesn't report
+ individual plot values but the adjusted mean for a variety in
+ each single experiment. Neither mention which experimental design
+ was used in each station. The book report the Mean Square Error
+ estimates for each experiment. These values as provided as an
+ attribute of the object, \code{attr(peanut, "MSE")} and they
+ comes from the ANOVA table corresponding to an appropriate model
+ for each location. With these MSE is possible use them in a such
+ a way that a partial ANOVA table can be obtained to test the
+ effect of location, variety and its interaction.
+}
+\examples{
+require(lattice)
+
+data(peanutYield2)
+str(peanutYield2)
+
+xyplot(meanYield~variety, data=peanutYield2,
+ groups=loc, type="o",
+ ylab=expression(Yield~(t~ha^{-1})),
+ xlab="Variety")
+}
+\keyword{datasets}
+
diff --git a/man/potatoYield2.Rd b/man/potatoYield2.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..9dfc732ffe57a722453c1313792d0dd784a11d4b
--- /dev/null
+++ b/man/potatoYield2.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/legTools.R
+\docType{data}
+\name{potatoYield2}
+\alias{potatoYield2}
+\title{Potato variety competition experiments in several locations}
+\format{a \code{data.frame} with 56 records and 3 variables. There is
+ an attribute named \code{MSE}, a named vector containing the Mean
+ Squares Errors estimates for each experiment.}
+\source{
+Pimentel Gomes, F. (2009). Curso de Estatística Experimental
+ (15th ed.). Piracicaba, São Paulo: FEALQ. (page 147)
+}
+\usage{
+data(potatoYield2)
+}
+\description{
+These data are from a set of experiments done by the
+ engineer Oscar A. Garay at Balcare, Argentina. These experiments
+ were done in a randomized complete block design with 4 blocks and
+ at 7 locations on the potato production region at the Buenos
+ Aires province.
+
+\itemize{
+
+\item \code{variety} a categorical unordered factor with 8 levels,
+ varieties of potato.
+
+\item \code{loc} a categorical unordered factor with 7 levels, the
+ locations that represent farms or experimental stations.
+
+\item \code{sumYield} is the sum of yield for a variety in each
+ experiment. Then, this sum values across 4 blocks in each
+ experiment. To get the mean yield you should divide by 4. Yield
+ is t/ha.
+
+}
+}
+\details{
+The data in the book was not complete because doesn't report
+ individual plot values but, instead, the sum for a variety in
+ each experiment. To do a joint or global analysis, with all
+ locations, varieties and blocks, its necessary all individual
+ plot values. The book report the Mean Square Error estimates for
+ each experiment as an attribute of the object,
+ \code{attr(potatoYield2, "MSE")} and they comes from the ANOVA
+ table in which the model is \code{~block+variety} for each
+ location. The data set \link[legTools]{potatoYield} correspond
+ the location 3. With these MSE is possible use them in a such a
+ way that a partial ANOVA table can be obtained to test the effect
+ of location, variety and its interaction.
+}
+\examples{
+require(lattice)
+
+data(potatoYield2)
+str(potatoYield2)
+
+xyplot(sumYield/4~variety, data=potatoYield2,
+ groups=loc, type="o",
+ ylab=expression(Yield~(t~ha^{-1})),
+ xlab="Variety")
+}
+\keyword{datasets}
+