fat2.ad2.rbd {ExpDes}

Double factorial scheme plus two additional treatments in RBD

Description

fat2.ad2.rbd Analyses experiments in balanced Randomized Blocks Design in double factorial scheme with two additional treatments, considering a fixed model.

Usage

fat2.ad2.rbd(
  factor1,
  factor2,
  block,
  resp,
  respAd1,
  respAd2,
  quali = c(TRUE, TRUE),
  mcomp = "tukey",
  fac.names = c("F1", "F2"),
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

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.

respAd1

Numeric or complex vector containing the additional treatment 1.

respAd2

Numeric or complex vector containing the additional treatment 2.

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%.

unfold

Says what must be done after the ANOVA. If NULL (default), recommended tests are performed; if '0', just ANOVA is performed; if '1', the simple effects are tested; if '2', the double interaction is unfolded.

Details

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

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).

Note

The graphics can be used to construct regression plots and plotres for residuals plots.

Author(s)

Portya Piscitelli Cavalcanti

Sônia Maria De Stefano Piedade

Eric B Ferreira, [e-mail]

References

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See Also

fat2.crd, fat2.rbd, fat3.crd, fat3.rbd, fat2.ad.crd, fat2.ad.rbd, fat3.ad.crd and fat3.ad.rbd.

Examples

factor1<-c(rep(1,6),rep(2,6))
factor2<-c(rep(1,3),rep(2,3),rep(1,3),rep(2,3))
block<-rep(1:3,4)
resp<-c(10.0,10.8,9.8,10.3,11.3,10.3,9.7,10.1,10.2,9.4,11.6,9.1)
respAd1<-c(10.6,10.6,10.4)
respAd2<-c(5.7,6,7.4)
data.frame(factor1,factor2,block,resp)
fat2.ad2.rbd(factor1, factor2, block, resp, respAd1, respAd2,
quali=c(TRUE, FALSE), mcomp = "tukey", fac.names =
c("XXXX", "YYYY"), sigT = 0.05, sigF = 0.05, unfold=NULL)