ExpDes (version 1.2.0)

fat3.crd: Triple factorial scheme in CRD

Description

Analyses experiments in balanced Completely Randomized Design in triple factorial scheme, considering a fixed model.

Usage

fat3.crd(factor1, factor2, factor3, resp, quali = c(TRUE, TRUE, TRUE), 
mcomp = "tukey", fac.names = c("F1", "F2", "F3"), 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.

factor3

Numeric or complex vector containing the factor 3 levels.

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, 2 and 3.

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 CRD, 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

For more examples, see: fat2.crd, fat2.rbd, fat3.crd, fat3.rbd, fat2.ad.crd, fat2.ad.rbd, fat3.ad.crd and fat3.ad.rbd.

Examples

# NOT RUN {
data(ex6)
attach(ex6)
fat3.crd(fatorA, fatorB, fatorC, resp, quali = c(TRUE, TRUE, TRUE), mcomp = "lsdb", 
fac.names = c("Factor A", "Factor B", "Factor C"), sigT = 0.05, sigF = 0.05)
# }