Test for homogeneity of variances of Anscombe and Tukey

Description

anscombetukey Performs the test for homogeneity of variances of Anscombe and Tukey (1963).

Usage

anscombetukey(
  resp,
  Trat,
  Bloco,
  glres,
  msres,
  sstrat,
  ssbloco,
  residuals,
  fitted.values
)

Arguments

Value

Returns the p-value of Anscombe and Tukey's test of homogeneity of variances and its practical interpretation for 5% of significance.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Marcos Costa de Paula

Mateus Pimenta Siqueira Lima

References

ANSCOMBE, F. J.; TUKEY, J. W. The examination and analysis of residuals. Technometrics, 5:141-160, 1963.

RIBEIRO, R. Proposta e comparacao do desempenho de testes para homogeneidade de variancia de modelos de classificacao one-way e two-way. Iniciacao Cientifica. (Iniciacao Cientifica) - Universidade Federal de Alfenas. 2012.

See Also

han, oneillmathews.

Examples

data(ex2)
attach(ex2)
rbd(trat, provador, aparencia, quali = TRUE, mcomp = "tukey",
hvar='anscombetukey', sigT = 0.05, sigF = 0.05)

Test for Homogeneity of Variances: Bartlett

Description

bartlett Performs the test for homogeneity of variances of Bartlett (1937).

Usage

bartlett(trat, resp, t, r)

Arguments

Value

Returns the p-value of Bartlett's test of homogeneity of variances and its practical interpretation for 5% of significance.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Marcos Costa de Paula

Mateus Pimenta Siqueira Lima

References

BARTLETT, M. S. Properties of sufficiency and statistical tests. Proceedings of the Royal Statistical Society - Serie A, 60:268-282, 1937.

NOGUEIRA, D, P.; PEREIRA, G, M. Desempenho de testes para homogeneidade de vari?ncias em delineamentos inteiramente casualizados. Sigmae, Alfenas, v.2, n.1, p. 7-22. 2013.

See Also

levene, oneillmathews, samiuddin

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = FALSE, hvar='bartlett', sigF = 0.05)

Multiple comparison: Calinski and Corsten

Description

ccF Performs the Calinski and Corsten test based on the F distribution.

Usage

ccF(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Value

Multiple means comparison for the Calinski and Corsten test.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Patricia de Siqueira Ramos

Daniel Furtado Ferreira

References

CALI\'NSKI, T.; CORSTEN, L. C. A. Clustering means in ANOVA by Simultaneous Testing. Biometrics. v. 41, p. 39-48, 1985.

Examples

data(ex2)
attach(ex2)
rbd(trat, provador, aparencia, quali = TRUE, mcomp='ccf',
sigT = 0.05, sigF = 0.05)

Multiple comparison: Bootstrap

Description

ccboot Performs the Ramos and Ferreira (2009) multiple comparison bootstrap test.

Usage

ccboot(
  y,
  trt,
  DFerror,
  SSerror,
  alpha = 0.05,
  group = TRUE,
  main = NULL,
  B = 1000
)

Arguments

Value

Multiple means comparison for the bootstrap test.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Patricia de Siqueira Ramos

Daniel Furtado Ferreira

References

RAMOS, P. S., FERREIRA, D. F. Agrupamento de medias via bootstrap de populacoes normais e nao-normais, Revista Ceres, v.56, p.140-149, 2009.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = TRUE, mcomp='ccboot', sigF = 0.05)

One factor Completely Randomized Design

Description

crd Analyses balanced experiments in Completely Randomized Design under one single factor, considering a fixed model.

Usage

crd(
  treat,
  resp,
  quali = TRUE,
  mcomp = "tukey",
  nl = FALSE,
  hvar = "bartlett",
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

Details

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

Value

The output contains the ANOVA of the 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).

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

FERREIRA, E. B.; CAVALCANTI, P. P. Funcao em codigo R para analisar experimentos em DIC simples, em uma so rodada. In: REUNIAO ANUAL DA REGIAO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 54./SIMPOSIO DE ESTATISTICA APLICADA A EXPERIMENTACAO AGRONOMICA, 13., 2009, Sao Carlos. Programas e resumos... Sao Carlos, SP: UFSCar, 2009. p. 1-5.

See Also

fat2.crd, fat3.crd, split2.crd, fat2.ad.crd and fat3.ad.crd.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = FALSE, sigF = 0.05, unfold=NULL)

Multiple comparison: Duncan test

Description

duncan Performs the test of Duncan for multiple comparison of means.

Usage

duncan(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Value

Returns the multiple comparison of means according to the test of Duncan.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

Stink bugs in corn: additional treatment.

Description

Additional treatment response variable (height of corn plants) of the experiment on stink bugs.

Usage

data(est21Ad)

Format

Numeric vector.

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Vines: Split-Plot in Randomized Blocks Design

Description

Experiment about vines (not published) where one studied the effects of different fertilizers and harvest dates on the pH of grapes.

Usage

data(ex)

Format

A data frame with 24 observations on the following 4 variables.

trat

a factor with levels A B

dose

a numeric vector

rep

a numeric vector

resp

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Yacon: CRD

Description

Experiment aiming to evaluate the influence of the yacon flour consumption on the glicemic index.

Usage

data(ex1)

Format

A data frame with 24 observations on the following 2 variables.

trat

a numeric vector

ig

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

RIBEIRO, J. de A. Estudos Quimicos e bioquimicos do Yacon (Samallanthus sonchifolius) in natura e Processado e Influencia do seu Consumo sobre Niveis Glicemicos e Lipideos Fecais de Ratos. 2008. 166p. Dissertation (Master in Food Science) - Universidade Federal de Lavras, UFLA, Lavras, 2008.

Food bars: RBD

Description

Sensory evaluation of food bars where panelists (blocks) evaluated their appearance.

Usage

data(ex2)

Format

A data frame with 350 observations on the following 3 variables.

provador

a numeric vector

trat

a factor with levels A B C D E

aparencia

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

PAIVA, A. P. de. Estudos Tecnologicos, Quimico, Fisico-quimico e Sensorial de Barras Alimenticias Elaboradas com Subprodutos e Residuos Agoindustriais. 2008. 131p. Dissertation (Master in Food Science) - Universidade Federal de Lavras, UFLA, Lavras, 2008.

Forage: LSD

Description

Data from an experiment aiming to select forage for minimizing the intake problem of feeding cattle in the sub-region of Paiaguas.

Usage

data(ex3)

Format

A data frame with 49 observations on the following 4 variables.

trat

a factor with levels A B C D E F G

linha

a numeric vector

coluna

a numeric vector

resp

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

COMASTRI FILHO, J. A. Avaliacao de especies de forrageiras nativas e exoticas na sub-regiao dos paiaguas no pantanal mato-grossense. Pesq. Agropec. Bras., Brasilia, v.29, n.6, p. 971-978, jun. 1994.

Composting: Doble Factorial scheme in CRD

Description

Field experiment to test the composting of coffee husk with or without cattle manure at different revolving intervals.

Usage

data(ex4)

Format

A data frame with 24 observations on the following 11 variables.

revol

a numeric vector

esterco

a factor with levels c s

rep

a numeric vector

c

a numeric vector

n

a numeric vector

k

a numeric vector

p

a numeric vector

zn

a numeric vector

b

a numeric vector

ca

a numeric vector

cn

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

REZENDE, F. A. de. Aproveitamento da Casca de Cafe e Borra da Purificacao de Gorduras e Oleos Residuarios em Compostagem. 2010. 74p. Thesis (Doctorate in Agronomy/Fitotecny) - Universidade Federal de Lavras, UFLA, Lavras, 2010.

Food bars: Double Factorial scheme in RBD

Description

Data adapted from a sensorial experiment where panelists of different genders evaluated the taste of food bars.

Usage

data(ex5)

Format

A data frame with 160 observations on the following 4 variables.

trat

a factor with levels 10g 15g 15t 20t

genero

a factor with levels F M

bloco

a numeric vector

sabor

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

MOREIRA, D. K. T. Extrudados Expandidos de Arroz, Soja e Gergelim para Uso em Barras Alimenticias. 2010. 166p. Dissertation (Master in Food Science) - Universidade Federal de Lavras, UFLA, Lavras, 2010.

Fictional data 1

Description

Data simulated from a standard normal distribution for an experiment in triple factorial scheme.

Usage

data(ex6)

Format

A data frame with 24 observations on the following 5 variables.

fatorA

a numeric vector

fatorB

a numeric vector

fatorC

a numeric vector

rep

a numeric vector

resp

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Height of corn plants 21 days after emergence.

Description

We evaluated the height of corn plants 21 days after emergence under infestation of stink bugs (Dichelops) at different times of coexistence (period) and infestation levels (level). Additional treatment is period zero and level zero.

Usage

data(ex7)

Format

Data frame with 80 observations on the following 4 variables.

periodo

a factor with levels 0-7DAE 0-14DAE 0-21DAE 7-14DAE 7-21DAE

nivel

a numeric vector

bloco

a numeric vector

est21

a numeric vector

@references RODRIGUES, R. B. Danos do percevejo-barriga-verde Dichelops melacanthus (Dallas, 1851) (Hemiptera: Pentatomidae) na cultura do milho. 2011. 105f. Dissertacao (Mestrado em Agronomia - Universidade Federal de Santa Maria, Santa Maria, 2011.

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Composting: double factorial scheme plus one additional treatment in CRD.

Description

Experiment in greenhouses to observe the performance of the obtained composting for fertilizing sorghum.

Usage

data(ex8)

Format

A data frame with 24 observations on the following 5 variables.

inoculante

a factor with levels esterco mamona

biodiesel

a numeric vector

vaso

a numeric vector

fresca

a numeric vector

seca

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

REZENDE, F. A. de. Aproveitamento da Casca de Cafe e Borra da Purificacao de Gorduras e Oleos Residuarios em Compostagem. 2010. 74p. Thesis (Doctorate in Agronomy/Fitotecny) - Universidade Federal de Lavras, UFLA, Lavras, 2010.

Vegetated: Split-plot in CRD

Description

Subset of data from an experiment that studied the effect on soil pH of cover crops subjected to trampling by cattle predominantly under continuous grazing system, analyzed at different depths.

Usage

data(ex9)

Format

A data frame with 48 observations on the following 4 variables.

cobertura

a factor with levels T1 T2 T3 T4 T5 T6

prof

a numeric vector

rep

a numeric vector

pH

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

References

GUERRA, A. R. Atributos de Solo sob Coberturas Vegetais em Sistema Silvipastoril em Lavras - MG. 2010. 141p. Dissertation (Master in Forest Engineering) - Universidade Federal de Lavras, UFLA, Lavras, 2010.

Example of fictitious data set

Description

Example of fictitious data mass for non-linear regression model fit

Usage

data(exnl)

Format

A data frame with 30 observations of the following 3 variables.

trat

a numeric vector

rep

a numeric vector

resp

a numeric vector

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Double factorial scheme plus one additional treatment in CRD

Description

fat2.ad.crd Analyses experiments in balanced Completely Randomized Design in double factorial scheme with an additional treatment, considering a fixed model.

Usage

fat2.ad.crd(
  factor1,
  factor2,
  repet,
  resp,
  respAd,
  quali = c(TRUE, TRUE),
  mcomp = "tukey",
  fac.names = c("F1", "F2"),
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.

FERREIRA, E. B.; CAVALCANTI, P. P.; NOGUEIRA D. A. Funcao para analisar experimentos em fatorial duplo com um tratamento adicional, em uma so rodada.In: CONGRESSO DE POS-GRADUACAO DA UNIVERSIDADE FEDERAL DE LAVRAS, 19., 2010, Lavras. Resumos... Lavras: UFLA, 2010.

See Also

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

Examples

data(ex8)
attach(ex8)
data(secaAd)
fat2.ad.crd(inoculante, biodiesel, vaso, seca, secaAd,
quali = c(TRUE,FALSE), mcomp = "tukey", fac.names =
c("Inoculant", "Biodiesel"), sigT = 0.05, sigF = 0.05,
unfold=NULL)

Double factorial scheme plus one additional treatment in RBD

Description

fat2.ad.rbd Analyses experiments in balanced Randomized Blocks Designs in double factorial scheme with an additional treatment, considering a fixed model.

Usage

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

Arguments

Details

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

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).

Note

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

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.

See Also

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

Examples

data(ex7)
attach(ex7)
data(est21Ad)
fat2.ad.rbd(periodo, nivel, bloco, est21, est21Ad,
quali=c(TRUE, FALSE), mcomp = "tukey", fac.names =
c("Period", "Level"), sigT = 0.05, sigF = 0.05,
unfold=NULL)

Double factorial scheme plus two additional treatments in CRD

Description

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

Usage

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

Arguments

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, eric.ferreira@unifal-mg.edu.br

References

???

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))
repet<-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,repet,resp)
fat2.ad2.crd(factor1, factor2, repet, resp, respAd1, respAd2,
quali=c(TRUE, FALSE), mcomp = "tukey", fac.names =
c("XXXX", "YYYY"), sigT = 0.05, sigF = 0.05, unfold=NULL)

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

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, eric.ferreira@unifal-mg.edu.br

References

???

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)

Double factorial scheme in CRD

Description

fat2.crd Analyses experiments in balanced Completely Randomized Design in double factorial scheme, considering a fixed model.

Usage

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

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

See Also

crd, fat3.crd, split2.crd, fat2.ad.crd and fat3.ad.crd.

Examples

data(ex4)
attach(ex4)
fat2.crd(revol, esterco, zn, quali = c(FALSE,TRUE),
mcomp = "tukey", fac.names = c("Revolving","Manure"),
sigT = 0.05, sigF = 0.05, unfold=NULL)

Double factorial scheme in RBD

Description

fat2.rbd Analyses experiments in balanced Randomized Blocks Designs in double factorial scheme, considering a fixed model.

Usage

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

Arguments

Details

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

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).

Note

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

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

See Also

fat3.rbd, split2.rbd, strip, fat2.ad.rbd and fat3.ad.rbd.

Examples

data(ex5)
attach(ex5)
fat2.rbd(trat, genero, bloco, sabor ,quali =
c(TRUE,TRUE), mcomp = "lsd", fac.names = c("Samples",
"Gender"), sigT = 0.05, sigF = 0.05, unfold=NULL)

Triple factorial scheme plus an additional treatment in CRD

Description

fat3.ad.crd Analyses experiments in balanced Completely Randomized Design in triple factorial scheme with an additional treatment, considering a fixed model.

Usage

fat3.ad.crd(
  factor1,
  factor2,
  factor3,
  repet,
  resp,
  respAd,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  fac.names = c("F1", "F2", "F3"),
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.

See Also

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

Examples

data(ex6)
attach(ex6)
data(respAd)
fat3.ad.crd(fatorA, fatorB, fatorC, rep, resp, respAd,
quali = c(TRUE, TRUE, TRUE), mcomp = "duncan",
fac.names = c("Factor A", "Factor B", "Factor C"),
sigT = 0.05, sigF = 0.05, unfold=NULL)

Triple factorial scheme plus an additional treatment in RBD

Description

fat3.ad.rbd Analyses experiments in balanced Randomized Blocks Designs in triple factorial scheme with an additional treatment, considering a fixed model.

Usage

fat3.ad.rbd(
  factor1,
  factor2,
  factor3,
  block,
  resp,
  respAd,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  fac.names = c("F1", "F2", "F3"),
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.

See Also

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

Examples

data(ex6)
attach(ex6)
data(respAd)
fat3.ad.rbd(fatorA, fatorB, fatorC, rep, resp, respAd,
quali = c(TRUE, TRUE, TRUE), mcomp = "snk", fac.names =
c("Factor A", "Factor B", "Factor C"), sigT = 0.05,
sigF = 0.05, unfold=NULL)

Triple factorial scheme in CRD

Description

fat3.crd 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,
  unfold = NULL
)

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

See Also

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

Examples

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)

Triple factorial scheme in RBD

Description

fat3.rbd Analyses experiments in balanced Randomized Blocks Designs in triple factorial scheme, considering a fixed model.

Usage

fat3.rbd(
  factor1,
  factor2,
  factor3,
  block,
  resp,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  fac.names = c("F1", "F2", "F3"),
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

See Also

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

Examples

data(ex6)
attach(ex6)
fat3.rbd(fatorA, fatorB, fatorC, rep, resp, quali = c(TRUE,
TRUE, TRUE), mcomp = "tukey", fac.names = c("Factor A",
"Factor B", "Factor C"), sigT = 0.05, sigF = 0.05,
unfold=NULL)

Generalized inverse

Description

ginv Computes the Moore-Penrose generalized inverse of a matrix X.

Usage

ginv(X, tol = sqrt(.Machine$double.eps))

Arguments

Value

A MP generalized inverse matrix for X.

References

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer. p.100.

See Also

solve, svd, eigen

Regression model plots

Description

graphics Plots from regression models fitted in ANOVA.

Usage

graphics(
  a,
  degree = 1,
  mod = TRUE,
  main = " ",
  sub = " ",
  xlab = "Levels (X)",
  ylab = "Response var (Y)",
  pch = 19,
  xlim = NULL,
  ylim = NULL,
  bty = "o"
)

Arguments

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

References

STEEL, R. G. D.; TORRIE, J. H. Principles and procedures in Statistics: a biometrical approach. McGraw-Hill, New York, NY. 1980.

See Also

reg.poly, plotres.

Examples

data(ex1)
attach(ex1)
a<-crd(trat, ig, quali=FALSE, nl=FALSE)
graphics(a, degree=1)
graphics(a, degree=2)
graphics(a, degree=3)

Test for homogeneity of variances of Han

Description

han Performs the test for homogeneity of variances of Han (1969).

Usage

han(resp, trat, block)

Arguments

Value

Returns the p-value of Han's test of homogeneity of variances and its practical interpretation for 5% of significance.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima

References

HAN, C. P. Testing the homogeneity of variances in a two-way classification. Biometrics, 25:153-158, Mar. 1969.

RIBEIRO, R. Proposta e comparacao do desempenho de testes para homogeneidade de variancia de modelos de classicacao one-way e two-way. Iniciacao Cientifica. (Iniciacao Cientifica) - Universidade Federal de Alfenas. 2012.

See Also

anscombetukey, oneillmathews.

Examples

data(ex2)
attach(ex2)
rbd(trat, provador, aparencia, hvar = "han")

Setting the last character of a chain

Description

lastC A special function for the group of treatments in the multiple comparison tests. Use order.group.

Usage

lastC(x)

Arguments

Value

x character.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)

See Also

order.group.

Examples

x<-c("a","ab","b","c","cd")
lastC(x)
# "a" "b" "b" "c" "d"

Latin Square Design

Description

lastd Analyses experiments in balanced Latin Square Design, considering a fixed model.

Usage

latsd(
  treat,
  row,
  column,
  resp,
  quali = TRUE,
  mcomp = "tukey",
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

Details

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

Value

The output contains the ANOVA of the LSD, 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).

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti @note The graphics can be used to construct regression plots and plotres for residuals plots.

References

GOMES, F. P. Curso de Estatistica Experimental. 10a ed. Piracicaba: ESALQ/USP. 1982. 430.

FERREIRA, E. B.; CAVALCANTI, P. P.; NOGUEIRA D. A. Funcao em codigo R para analisar experimentos em DQL simples, em uma so rodada. In: CONGRESSO DE POS-GRADUACAO DA UNIVERSIDADE FEDERAL DE LAVRAS, 18., 2009, Lavras. Annals... Lavras: UFLA, 2009.

See Also

crd, rbd.

Examples

data(ex3)
attach(ex3)
latsd(trat, linha, coluna, resp, quali = TRUE, mcomp = "snk",
sigT = 0.05, sigF = 0.05, unfold=NULL)

Test for homogeneity of variances of Layard

Description

layard Performs the test for homogeneity of variances of Layard for Jackknife (1973).

Usage

layard(trat, resp, t, r)

Arguments

Value

Returns the p-value of the Layard test of homogeneity of variances and its practical interpretation for the significance level of 5%.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima

References

LAYARD, M. N. J. Robust large-sample tests for homogeneity of variances. Journal of the American Statistical Association, v.68, n.341, p.195-198, 1973.

NOGUEIRA, D, P.; PEREIRA, G, M. Desempenho de testes para homogeneidade de variancias em delineamentos inteiramente casualizados. Sigmae, Alfenas, v.2, n.1, p. 7-22. 2013.

See Also

bartlett, samiuddin, levene, oneillmathews.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = FALSE, hvar = "layard")

Test for homogeneity of variances of Levene

Description

levene Performs the test for homogeneity of variances of Levene (1960).

Usage

levene(trat, resp, t, r)

Arguments

Value

Returns the p-value of Levene's test of homogeneity of variances and its practical interpretation for significance level of 5%.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima

References

LEVENE, H. Robust tests for equality of variances. In: Olkin, I.; Ghurye, S.G.; Hoeffding, W.; Madow, W.G.; Mann, H.B. (eds.). Contribution to Probability and Statistics. Stanford, CA: Stanford University Press, pages 278-292, 1960.

NOGUEIRA, D, P.; PEREIRA, G, M. Desempenho de testes para homogeneidade de variancias em delineamentos inteiramente casualizados. Sigmae, Alfenas, v.2, n.1, p. 7-22. 2013.

See Also

bartlett, samiuddin, layard, oneillmathews.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = FALSE, hvar = "levene")

Multiple comparison: Least Significant Difference test

Description

lsd Performs the t test (LSD) for multiple comparison of means.

Usage

lsd(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Value

Returns the multiple comparison of means according to the LSD test.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

See Also

snk, duncan, ccboot, lsdb, scottknott, tukey, ccF.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = TRUE, mcomp = "lsd", sigT = 0.05)

Multiple comparison: Bonferroni's Least Significant Difference test

Description

lsdb Performs the t test (LSD) with Bonferroni's protection, for multiple comparison of means.

Usage

lsdb(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Value

Returns the multiple comparison of means according to the LSDB test.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

See Also

snk, duncan, ccboot, lsd, scottknott, tukey, ccF.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = TRUE, mcomp = "lsdb", sigT = 0.05)

Test for homogeneity of variances of ONeill and Mathews (RBD)

Description

oneilldbc Performs the test for homogeneity of variances of ONeill and Mathews (2002).

Usage

oneilldbc(resp, trat, block)

Arguments

Value

Returns the p-value of ONeill and Mathews' test of homogeneity of variances and its practical interpretation for significance level of 5%.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima

References

O'NEILL, M. E.; MATHEWS, K. L. Levene tests of homogeneity of variance for general block and treatment designs. Biometrics, 58:216-224, Mar. 2002.

RIBEIRO, R. Proposta e comparacao do desempenho de testes para homogeneidade de variancia de modelos de classificacao one-way e two-way. Iniciacao Cientifica. (Iniciacao Cientifica) - Universidade Federal de Alfenas. 2012.

See Also

anscombetukey, han.

Examples

data(ex2)
attach(ex2)
rbd(trat, provador, aparencia, hvar = "oneillmathews")

Test for homogeneity of variances of ONeill and Mathews (CRD)

Description

oneillmathews Performs the test for homogeneity of variances of ONeill and Mathews (2000).

Usage

oneillmathews(trat, resp, t, r)

Arguments

Value

Returns the p-value of ONeill and Mathews' test of homogeneity of variances and its practical interpretation for significance level of 5%.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima

References

O'NEILL, M. E.; MATHEWS, K. L. A weighted least squares approach to levene test of homogeneity of variance. Australian e New Zealand Journal Statistical, 42(1):81-100, 2000.

NOGUEIRA, D, P.; PEREIRA, G, M. Desempenho de testes para homogeneidade de variancias em delineamentos inteiramente casualizados. Sigmae, Alfenas, v.2, n.1, p. 7-22. 2013.

See Also

bartlett, layard, levene, samiuddin.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = FALSE, hvar = "oneillmathews",
sigF = 0.05)

Ordering the treatments according to the multiple comparison

Description

order.group It orders the groups of means.

Usage

order.group(trt, means, N, MSerror, Tprob, std.err, parameter = 1)

Arguments

Value

• trt Factor

• means Numeric

• N Numeric

• MSerror Numeric

• Tprob value between 0 and 1

• std.err Numeric

• parameter Constant

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)

See Also

order.stat.SNK.

Grouping the treatments averages in a comparison with a minimum value

Description

order.stat.SNK Orders the groups of means according to the test of SNK.

Usage

order.stat.SNK(treatment, means, minimum)

Arguments

Value

• trt Factor

• means Numeric

• minimum Numeric

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)

See Also

order.group.

Residual plots

Description

plotres Residual plots for a output model. Four sets of plots are produced: (1) Histogram, (2) normal probability plot for the residual, (3) Standardized Residuals versus Fitted Values, and (4) box-plot (Standardized Residuals).

Usage

plotres(x)

Arguments

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @note The default produces four plots regarding the ANOVA assumptions.

References

STEEL, R. G. D.; TORRIE, J. H. Principles and procedures in Statistics: a biometrical approach. McGraw-Hill, New York, NY. 1980.

See Also

graphics.

Examples

data(ex1)
attach(ex1)
a<-crd(trat, ig)
plotres(a)

Randomized Blocks Design

Description

rbd Analyses experiments in balanced Randomized Blocks Designs under one single factor, considering a fixed model.

Usage

rbd(
  treat,
  block,
  resp,
  quali = TRUE,
  mcomp = "tukey",
  nl = FALSE,
  hvar = "oneillmathews",
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

Details

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

Value

The output contains the ANOVA of the 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).

Note

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

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

FERREIRA, E. B.; CAVALCANTI, P. P.; NOGUEIRA D. A. Funcao em codigo R para analisar experimentos em DBC simples, em uma so rodada. In: JORNADA CIENTIFICA DA UNIVERSIDADE FEDERAL DE ALFENAS-MG, 2., 2009, Alfenas. Annals... ALfenas: Unifal-MG, 2009.

See Also

fat2.rbd, fat3.rbd, split2.rbd, strip, fat2.ad.rbd and fat3.ad.rbd.

Examples

data(ex2)
attach(ex2)
rbd(trat, provador, aparencia, quali = TRUE, mcomp = "lsd",
hvar = "oneillmathews", sigT = 0.05, sigF = 0.05,
unfold=NULL)

Non-linear Regression

Description

reg.nl Adjusts non-linear regression models in Anova (Models: Power, Exponential, Logistic, Gompertz).

Usage

reg.nl(resp, treat)

Arguments

Value

Returns coefficients, significance and ANOVA of the fitted regression models.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Luiz Alberto Beijo

References

DRAPER, N.R.; SMITH, H. Apllied regression analysis. 3ed. New York : John Wiley, 1998. 706p.

See Also

graphics.

Examples

data(exnl)
attach(exnl)
x<-crd(trat, resp, quali = FALSE, nl = TRUE)
graphics(x, degree = "log")

Polinomial Regression

Description

reg.poly Fits sequential regression models until the third power.

Usage

reg.poly(resp, treat, DFerror, SSerror, DFtreat, SStreat)

Arguments

Value

Returns coefficients, significance and ANOVA of the fitted regression models.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

GOMES, F. P. Curso de Estatistica Experimental. 10a ed. Piracicaba: ESALQ/USP. 1982. 430.

See Also

graphics.

Fictional data: additional treatment

Description

Response variable form the additional treatment.

Usage

data(respAd)

Format

Numeric vector.

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Test for homogeneity of variances of Samiuddin

Description

samiuddin Performs the test for homogeneity of variances of Samiuddin (1976).

Usage

samiuddin(trat, resp, t, r)

Arguments

Value

Returns the p-value of Samiuddin's test of homogeneity of variances and its practical interpretation for significance level of 5%.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima

References

SAMIUDDIN, M. Bayesian test of homogeneity of variance. Journal of the American Statistical Association, 71(354):515-517, Jun. 1976.

NOGUEIRA, D, P.; PEREIRA, G, M. Desempenho de testes para homogeneidade de variancias em delineamentos inteiramente casualizados. Sigmae, Alfenas, v.2, n.1, p. 7-22. 2013.

See Also

bartlett, layard, levene, oneillmathews.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = FALSE, hvar = "samiuddin", sigF = 0.05)

Multiple comparison: Scott-Knott test

Description

scottknott Performs the test of Scott-Knott, for multiple comparison of means.

Usage

scottknott(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Value

Returns the multiple comparison of means according to the test of Scott-Knott.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti (Adapted from Laercio Junio da Silva - GPL(>=2))

References

RAMALHO, M. A. P.; FERREIRA, D. F.; OLIVEIRA, A. C. de. Experimentacao em Genetica e Melhoramento de Plantas. 2a ed. Lavras: UFLA. 2005. 300p.

See Also

snk, duncan, lsd, lsdb, ccboot, tukey, ccF.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = TRUE, mcomp = "sk", sigT = 0.05)

Composting: additional treatment

Description

Response variable (dry biomass) of the additional treatment of the experiment about composting.

Usage

data(secaAd)

Format

Numeric vector.

Author(s)

Eric Batista Ferreira, eric.ferreira@unifal-mg.edu.br

Multiple comparison: Student-Newman-Keuls test

Description

snk Performs the test of SNK, for multiple comparison of means.

Usage

snk(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Value

Returns the multiple comparison of means according to the test of SNK.

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

See Also

scottknott, duncan, lsd, lsdb, ccboot, tukey, ccF.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = TRUE, mcomp = "snk", sigT = 0.05)

Split-plots in CRD

Description

split2.crd Analyses experiments in Split-plot scheme in balanced Completely Randomized Design, considering a fixed model.

Usage

split2.crd(
  factor1,
  factor2,
  repet,
  resp,
  quali = c(TRUE, TRUE),
  mcomp = "tukey",
  fac.names = c("F1", "F2"),
  sigT = 0.05,
  sigF = 0.05,
  unfold = NULL
)

Arguments

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)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

See Also

split2.rbd and strip.

Examples

data(ex9)
attach(ex9)
split2.crd(cobertura, prof, rep, pH, quali = c(TRUE, TRUE),
mcomp = "lsd", fac.names = c("Cover", "Depth"), sigT = 0.05,
sigF = 0.05, unfold=NULL)

Split-plots in RBD

Description

split2.rbd 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,
  unfold = NULL
)

Arguments

Details

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

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).

Note

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

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti

References

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

See Also

split2.crd and strip.

Examples

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, unfold=NULL)

Strip-plot experiments

Description

strip Analysis Strip-plot experiments.

Usage

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

Arguments

Details

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

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).

Note

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

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Laís Brambilla Storti Ferreira

See Also

split2.rbd and rbd.

Examples

data(ex5)
attach(ex5)
strip(trat, genero, bloco, sabor, quali = c(TRUE,TRUE),
mcomp = "tukey", fac.names = c("Amostras","Genero"),
sigT = 0.05, sigF = 0.05, unfold=NULL)

Statistics of data grouped by factors

Description

tapply.stat This process lies in finding statistics which consist of more than one variable, grouped or crossed by factors. The table must be organized by columns between variables and factors.

Usage

tapply.stat(y, x, stat = "mean")

Arguments

Value

y Numeric x Numeric stat method = "mean", ...

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)

Multiple comparison: Tukey's test

Description

tukey Performs the test of Tukey, for multiple comparison of means.

Usage

tukey(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)

Arguments

Details

It is necessary first makes a analysis of variance.

Value

y Numeric trt factor DFerror Numeric MSerror Numeric alpha Numeric group Logic main Text

Author(s)

Eric B Ferreira, eric.ferreira@unifal-mg.edu.br

Denismar Alves Nogueira

Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)

References

Principles and procedures of statistics a biometrical approach Steel and Torry and Dickey. Third Edition 1997

See Also

scottknott, duncan, lsd, lsdb, ccboot, snk, ccF.

Examples

data(ex1)
attach(ex1)
crd(trat, ig, quali = TRUE, mcomp = "tukey", sigT = 0.05)