ExpDes (version 1.2.0)

split2.rbd: Split-plots in RBD

Description

Analyses experiments in Split-plot scheme in balanced Randomized Blocks Design, considering a fixed model.

Usage

split2.rbd(factor1, factor2, block, resp, quali = c(TRUE, TRUE), mcomp = "tukey", 
fac.names = c("F1", "F2"), sigT = 0.05, sigF = 0.05)

Arguments

factor1

Numeric or complex vector containing the factor 1 levels.

factor2

Numeric or complex vector containing the factor 2 levels.

block

Numeric or complex vector containing the blocks.

resp

Numeric or complex vector containing the response variable.

quali

Logic. If TRUE (default), the treatments are assumed qualitative, if FALSE, quantitatives.

mcomp

Allows choosing the multiple comparison test; the default is the test of Tukey, however, the options are: the LSD test ('lsd'), the LSD test with Bonferroni protection ('lsdb'), the test of Duncan ('duncan'), the test of Student-Newman-Keuls ('snk'), the test of Scott-Knott ('sk'), the Calinski and Corsten test ('ccf') and bootstrap multiple comparison's test ('ccboot').

fac.names

Allows labeling the factors 1 and 2.

sigT

The signficance to be used for the multiple comparison test; the default is 5%.

sigF

The signficance to be used for the F test of ANOVA; the default is 5%.

Value

The output contains the ANOVA of the referred RBD, the Shapiro-Wilk normality test for the residuals of the model, the fitted regression models (when the treatments are quantitative) and/or the multiple comparison tests (when the treatments are qualitative).

Details

The arguments sigT and mcomp will be used only when the treatment are qualitative.

References

BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.

See Also

split2.crd and strip

Examples

# NOT RUN {
data(ex)
attach(ex)
split2.rbd(trat, dose, rep, resp, quali = c(TRUE, FALSE), mcomp = "tukey", 
 fac.names = c("Treatament", "Dose"), sigT = 0.05, sigF = 0.05)
# }