| Title: | Experimental Designs Package |
|---|---|
| Description: | Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>. |
| Authors: | Eric Batista Ferreira, Portya Piscitelli Cavalcanti, Denismar Alves Nogueira |
| Maintainer: | Eric Batista Ferreira <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.2.2 |
| Built: | 2025-03-01 03:49:40 UTC |
| Source: | https://github.com/cran/ExpDes |
anscombetukey Performs the test for homogeneity of
variances of Anscombe and Tukey (1963).
anscombetukey( resp, Trat, Bloco, glres, msres, sstrat, ssbloco, residuals, fitted.values )anscombetukey( resp, Trat, Bloco, glres, msres, sstrat, ssbloco, residuals, fitted.values )
resp |
Numeric or complex vector containing the response variable. |
Trat |
Numeric or complex vector containing the treatments. |
Bloco |
Numeric or complex vector containing the blocks. |
glres |
Residual degrees of freedom. |
msres |
Residual Mean Square. |
sstrat |
Residual Sum of Squares. |
ssbloco |
Sum of Squares for blocks. |
residuals |
Numeric or complex vector containing the residuals. |
fitted.values |
Numeric or complex vector containing the fitted values. |
Returns the p-value of Anscombe and Tukey's test of homogeneity of variances and its practical interpretation for 5% of significance.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Marcos Costa de Paula
Mateus Pimenta Siqueira Lima
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.
data(ex2) attach(ex2) rbd(trat, provador, aparencia, quali = TRUE, mcomp = "tukey", hvar='anscombetukey', sigT = 0.05, sigF = 0.05)data(ex2) attach(ex2) rbd(trat, provador, aparencia, quali = TRUE, mcomp = "tukey", hvar='anscombetukey', sigT = 0.05, sigF = 0.05)
bartlett Performs the test for homogeneity of
variances of Bartlett (1937).
bartlett(trat, resp, t, r)bartlett(trat, resp, t, r)
trat |
Numeric or complex vector containing the treatments. |
resp |
Numeric or complex vector containing the response variable. |
t |
Number of treatments. |
r |
Numeric or complex vector containing the number of replications of each treatment. |
Returns the p-value of Bartlett's test of homogeneity of variances and its practical interpretation for 5% of significance.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Marcos Costa de Paula
Mateus Pimenta Siqueira Lima
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.
levene,
oneillmathews, samiuddin
data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar='bartlett', sigF = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar='bartlett', sigF = 0.05)
ccboot Performs the Ramos and Ferreira (2009)
multiple comparison bootstrap test.
ccboot( y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL, B = 1000 )ccboot( y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL, B = 1000 )
y |
Numeric or complex vector containing the response varible. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance of the test. |
group |
TRUE or FALSE |
main |
Title |
B |
Number of bootstrap resamples. |
Multiple means comparison for the bootstrap test.
Eric B Ferreira, [email protected]
Patricia de Siqueira Ramos
Daniel Furtado Ferreira
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.
data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp='ccboot', sigF = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp='ccboot', sigF = 0.05)
ccF Performs the Calinski and Corsten test based on
the F distribution.
ccF(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)ccF(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response varible. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance of the test. |
group |
TRUE or FALSE. |
main |
Title. |
Multiple means comparison for the Calinski and Corsten test.
Eric B Ferreira, [email protected]
Patricia de Siqueira Ramos
Daniel Furtado Ferreira
CALI\'NSKI, T.; CORSTEN, L. C. A. Clustering means in ANOVA by Simultaneous Testing. Biometrics. v. 41, p. 39-48, 1985.
data(ex2) attach(ex2) rbd(trat, provador, aparencia, quali = TRUE, mcomp='ccf', sigT = 0.05, sigF = 0.05)data(ex2) attach(ex2) rbd(trat, provador, aparencia, quali = TRUE, mcomp='ccf', sigT = 0.05, sigF = 0.05)
crd Analyses balanced experiments in Completely
Randomized Design under one single factor, considering a
fixed model.
crd( treat, resp, quali = TRUE, mcomp = "tukey", nl = FALSE, hvar = "bartlett", sigT = 0.05, sigF = 0.05, unfold = NULL )crd( treat, resp, quali = TRUE, mcomp = "tukey", nl = FALSE, hvar = "bartlett", sigT = 0.05, sigF = 0.05, unfold = NULL )
treat |
Numeric or complex vector containing the treatments. |
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-Knot ('sk'), the Calinski and Corsten test ('ccF') and bootstrap multiple comparison's test ('ccboot'). |
nl |
Logic. If FALSE (default) linear regression models are adjusted. IF TRUE, non-linear regression models are adjusted. |
hvar |
Allows choosing the test for homogeneity of variances; the default is the test of Bartlett, however there are other options: test of Levene ('levene'), test of Samiuddin ('samiuddin'), test of ONeill and Mathews ('oneillmathews') and the Layard test ('layard'). |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
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.
fat2.crd, fat3.crd,
split2.crd, fat2.ad.crd and
fat3.ad.crd.
data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, sigF = 0.05, unfold=NULL)data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, sigF = 0.05, unfold=NULL)
duncan Performs the test of Duncan for multiple
comparison of means.
duncan(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)duncan(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response variable. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance level. |
group |
TRUE or FALSE. |
main |
Title. |
Returns the multiple comparison of means according to the test of Duncan.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
Additional treatment response variable (height of corn plants) of the experiment on stink bugs.
data(est21Ad)data(est21Ad)
Numeric vector.
Eric Batista Ferreira, [email protected]
Experiment about vines (not published) where one studied the effects of different fertilizers and harvest dates on the pH of grapes.
data(ex)data(ex)
A data frame with 24 observations on the following 4 variables.
trata factor with levels A B
dosea numeric vector
repa numeric vector
respa numeric vector
Eric Batista Ferreira, [email protected]
Experiment aiming to evaluate the influence of the yacon flour consumption on the glicemic index.
data(ex1)data(ex1)
A data frame with 24 observations on the following 2 variables.
trata numeric vector
iga numeric vector
Eric Batista Ferreira, [email protected]
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.
Sensory evaluation of food bars where panelists (blocks) evaluated their appearance.
data(ex2)data(ex2)
A data frame with 350 observations on the following 3 variables.
provadora numeric vector
trata factor with levels A
B C D E
aparenciaa numeric vector
Eric Batista Ferreira, [email protected]
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.
Data from an experiment aiming to select forage for minimizing the intake problem of feeding cattle in the sub-region of Paiaguas.
data(ex3)data(ex3)
A data frame with 49 observations on the following 4 variables.
trata factor with levels A B
C D E F G
linhaa numeric vector
colunaa numeric vector
respa numeric vector
Eric Batista Ferreira, [email protected]
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.
Field experiment to test the composting of coffee husk with or without cattle manure at different revolving intervals.
data(ex4)data(ex4)
A data frame with 24 observations on the following 11 variables.
revola numeric vector
estercoa factor with levels c
s
repa numeric vector
ca numeric vector
na numeric vector
ka numeric vector
pa numeric vector
zna numeric vector
ba numeric vector
caa numeric vector
cna numeric vector
Eric Batista Ferreira, [email protected]
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.
Data adapted from a sensorial experiment where panelists of different genders evaluated the taste of food bars.
data(ex5)data(ex5)
A data frame with 160 observations on the following 4 variables.
trata factor with levels 10g
15g 15t 20t
generoa factor with levels F
M
blocoa numeric vector
sabora numeric vector
Eric Batista Ferreira, [email protected]
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.
Data simulated from a standard normal distribution for an experiment in triple factorial scheme.
data(ex6)data(ex6)
A data frame with 24 observations on the following 5 variables.
fatorAa numeric vector
fatorBa numeric vector
fatorCa numeric vector
repa numeric vector
respa numeric vector
Eric Batista Ferreira, [email protected]
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.
data(ex7)data(ex7)
Data frame with 80 observations on the following 4 variables.
periodoa factor with levels 0-7DAE
0-14DAE 0-21DAE 7-14DAE
7-21DAE
nivela numeric vector
blocoa numeric vector
est21a 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.
Eric Batista Ferreira, [email protected]
Experiment in greenhouses to observe the performance of the obtained composting for fertilizing sorghum.
data(ex8)data(ex8)
A data frame with 24 observations on the following 5 variables.
inoculantea factor with levels
esterco mamona
biodiesela numeric vector
vasoa numeric vector
frescaa numeric vector
secaa numeric vector
Eric Batista Ferreira, [email protected]
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.
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.
data(ex9)data(ex9)
A data frame with 48 observations on the following 4 variables.
coberturaa factor with levels T1
T2 T3 T4 T5 T6
profa numeric vector
repa numeric vector
pHa numeric vector
Eric Batista Ferreira, [email protected]
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 mass for non-linear regression model fit
data(exnl)data(exnl)
A data frame with 30 observations of the following 3 variables.
trata numeric vector
repa numeric vector
respa numeric vector
Eric Batista Ferreira, [email protected]
fat2.ad.crd Analyses experiments in balanced
Completely Randomized Design in double factorial scheme
with an additional treatment, considering a fixed model.
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 )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 )
factor1 |
Numeric or complex vector containing the factor 1 levels. |
factor2 |
Numeric or complex vector containing the factor 2 levels. |
repet |
Numeric or complex vector containing the replications. |
resp |
Numeric or complex vector containing the response variable. |
respAd |
Numeric or complex vector containing the additional treatment. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to construct
regression plots and plotres for residuals
plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
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.
fat2.crd, fat2.rbd,
fat3.crd, fat3.rbd,
fat2.ad.rbd,
fat3.ad.crd and fat3.ad.rbd.
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)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)
fat2.ad.rbd Analyses experiments in balanced
Randomized Blocks Designs in double factorial scheme
with an additional treatment, considering a fixed model.
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 )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 )
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. |
respAd |
Numeric or complex vector containing the additional treatment. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.
fat2.crd, fat2.rbd,
fat3.crd, fat3.rbd,
fat2.ad.crd,
fat3.ad.crd and fat3.ad.rbd.
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)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)
fat2.ad2.crd Analyses experiments in balanced
Completely Randomized Design in double factorial scheme
with two additional treatments, considering a fixed model.
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 )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 )
factor1 |
Numeric or complex vector containing the factor 1 levels. |
factor2 |
Numeric or complex vector containing the factor 2 levels. |
repet |
Numeric or complex vector containing the replications. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to construct
regression plots and plotres for residuals
plots.
Portya Piscitelli Cavalcanti
Sônia Maria De Stefano Piedade
Eric B Ferreira, [email protected]
???
fat2.crd, fat2.rbd,
fat3.crd, fat3.rbd,
fat2.ad.crd, fat2.ad.rbd,
fat3.ad.crd and fat3.ad.rbd.
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)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)
fat2.ad2.rbd Analyses experiments in balanced
Randomized Blocks Design in double factorial scheme
with two additional treatments, considering a fixed model.
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 )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 )
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to construct
regression plots and plotres for residuals
plots.
Portya Piscitelli Cavalcanti
Sônia Maria De Stefano Piedade
Eric B Ferreira, [email protected]
???
fat2.crd, fat2.rbd,
fat3.crd, fat3.rbd,
fat2.ad.crd, fat2.ad.rbd,
fat3.ad.crd and fat3.ad.rbd.
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)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)
fat2.crd Analyses experiments in balanced
Completely Randomized Design in double factorial
scheme, considering a fixed model.
fat2.crd( factor1, factor2, resp, quali = c(TRUE, TRUE), mcomp = "tukey", fac.names = c("F1", "F2"), sigT = 0.05, sigF = 0.05, unfold = NULL )fat2.crd( factor1, factor2, resp, quali = c(TRUE, TRUE), mcomp = "tukey", fac.names = c("F1", "F2"), sigT = 0.05, sigF = 0.05, unfold = NULL )
factor1 |
Numeric or complex vector containing the factor 1 levels. |
factor2 |
Numeric or complex vector containing the factor 2 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 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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.
crd, fat3.crd,
split2.crd, fat2.ad.crd and
fat3.ad.crd.
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)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)
fat2.rbd Analyses experiments in balanced
Randomized Blocks Designs in double factorial scheme,
considering a fixed model.
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 )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 )
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%. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.
fat3.rbd,
split2.rbd, strip,
fat2.ad.rbd and fat3.ad.rbd.
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)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)
fat3.ad.crd Analyses experiments in balanced
Completely Randomized Design in triple factorial
scheme with an additional treatment, considering a
fixed model.
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 )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 )
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. |
repet |
Numeric or complex vector containing the replications. |
resp |
Numeric or complex vector containing the response variable. |
respAd |
Numeric or complex vector containing the additional treatment. |
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%. |
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 '2.1', '2.2' or '2.3', the double interactions are unfolded; if '3', the triple interaction is unfolded. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.
fat2.crd,
fat2.rbd, fat3.crd,
fat3.rbd, fat2.ad.crd,
fat2.ad.rbd, fat3.ad.crd
and fat3.ad.rbd.
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)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)
fat3.ad.rbd Analyses experiments in balanced
Randomized Blocks Designs in triple factorial scheme with
an additional treatment, considering a fixed model.
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 )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 )
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. |
block |
Numeric or complex vector containing the blocks. |
resp |
Numeric or complex vector containing the response variable. |
respAd |
Numeric or complex vector containing the additional treatment. |
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%. |
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 '2.1', '2.2' or '2.3', the double interactions are unfolded; if '3', the triple interaction is unfolded. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
HEALY, M. J. R. The analysis of a factorial experiment with additional treatments. Journal of Agricultural Science, Cambridge, v. 47, p. 205-206. 1956.
fat2.crd,
fat2.rbd, fat3.crd,
fat3.rbd, fat2.ad.crd,
fat2.ad.rbd, fat3.ad.crd
and fat3.ad.crd.
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)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)
fat3.crd Analyses experiments in balanced Completely
Randomized Design in triple factorial scheme, considering
a fixed model.
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 )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 )
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%. |
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 '2.1', '2.2' or '2.3', the double interactions are unfolded; if '3', the triple interaction is unfolded. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.
fat2.crd,
fat2.rbd, fat3.rbd,
fat2.ad.crd, fat2.ad.rbd,
fat3.ad.crd and fat3.ad.rbd.
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)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)
fat3.rbd Analyses experiments in balanced Randomized
Blocks Designs in triple factorial scheme, considering a
fixed model.
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 )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 )
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. |
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, 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%. |
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 '2.1', '2.2' or '2.3', the double interactions are unfolded; if '3', the triple interaction is unfolded. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.
fat2.crd,
fat2.rbd, fat3.crd,
fat2.ad.crd, fat2.ad.rbd,
fat3.ad.crd and fat3.ad.crd.
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)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)
ginv Computes the Moore-Penrose generalized inverse
of a matrix X.
ginv(X, tol = sqrt(.Machine$double.eps))ginv(X, tol = sqrt(.Machine$double.eps))
X |
Matrix for which the Moore-Penrose inverse is required. |
tol |
A relative tolerance to detect zero singular values. |
A MP generalized inverse matrix for X.
Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer. p.100.
graphics Plots from regression models fitted in ANOVA.
graphics( a, degree = 1, mod = TRUE, main = " ", sub = " ", xlab = "Levels (X)", ylab = "Response var (Y)", pch = 19, xlim = NULL, ylim = NULL, bty = "o" )graphics( a, degree = 1, mod = TRUE, main = " ", sub = " ", xlab = "Levels (X)", ylab = "Response var (Y)", pch = 19, xlim = NULL, ylim = NULL, bty = "o" )
a |
Output from anova (performed in ExpDes). |
degree |
For polynomial models, 1 (linear model) is the default, 2 (quadratic model), 3 (cubic model), "pot" (Power model), "log" (Logistic model), "gom" (Gompertz model) and "exp" (Exponential model). |
mod |
Logic. Print the model expression and its R2 on the top of the graphic. The default is TRUE. |
main |
Title of the plot. Empty is the default. |
sub |
Subtitle of the plot. Empty is the default. |
xlab |
Name for axis X. |
ylab |
Name for axis Y. |
pch |
Caracter type to be used on the observed values. |
xlim |
Limits for axis X. |
ylim |
Limits for axis Y. |
bty |
Type of box the plot is fitted in. |
Eric B Ferreira, [email protected]
STEEL, R. G. D.; TORRIE, J. H. Principles and procedures in Statistics: a biometrical approach. McGraw-Hill, New York, NY. 1980.
data(ex1) attach(ex1) a<-crd(trat, ig, quali=FALSE, nl=FALSE) graphics(a, degree=1) graphics(a, degree=2) graphics(a, degree=3)data(ex1) attach(ex1) a<-crd(trat, ig, quali=FALSE, nl=FALSE) graphics(a, degree=1) graphics(a, degree=2) graphics(a, degree=3)
han Performs the test for homogeneity of variances of
Han (1969).
han(resp, trat, block)han(resp, trat, block)
resp |
Numeric or complex vector containing the response variable. |
trat |
Numeric or complex vector containing the treatments. |
block |
Numeric or complex vector containing the blocks. |
Returns the p-value of Han's test of homogeneity of variances and its practical interpretation for 5% of significance.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima
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.
data(ex2) attach(ex2) rbd(trat, provador, aparencia, hvar = "han")data(ex2) attach(ex2) rbd(trat, provador, aparencia, hvar = "han")
lastC A special function for the group of treatments
in the multiple comparison tests. Use order.group.
lastC(x)lastC(x)
x |
letters |
x character.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)
x<-c("a","ab","b","c","cd") lastC(x) # "a" "b" "b" "c" "d"x<-c("a","ab","b","c","cd") lastC(x) # "a" "b" "b" "c" "d"
lastd Analyses experiments in balanced Latin Square
Design, considering a fixed model.
latsd( treat, row, column, resp, quali = TRUE, mcomp = "tukey", sigT = 0.05, sigF = 0.05, unfold = NULL )latsd( treat, row, column, resp, quali = TRUE, mcomp = "tukey", sigT = 0.05, sigF = 0.05, unfold = NULL )
treat |
Numeric or complex vector containing the treatments. |
row |
Numeric or complex vector containing the rows. |
column |
Numeric or complex vector containing the columns. |
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'). |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
Eric B Ferreira,
[email protected]
@author Denismar Alves Nogueira
@author Portya Piscitelli Cavalcanti
@note The graphics can be used to construct
regression plots and plotres for residuals
plots.
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.
data(ex3) attach(ex3) latsd(trat, linha, coluna, resp, quali = TRUE, mcomp = "snk", sigT = 0.05, sigF = 0.05, unfold=NULL)data(ex3) attach(ex3) latsd(trat, linha, coluna, resp, quali = TRUE, mcomp = "snk", sigT = 0.05, sigF = 0.05, unfold=NULL)
layard Performs the test for homogeneity of variances
of Layard for Jackknife (1973).
layard(trat, resp, t, r)layard(trat, resp, t, r)
trat |
Numeric or complex vector containing treatments. |
resp |
Numeric or complex vector containing the response variable. |
t |
Number of treatments. |
r |
Numeric or complex vector containing the number of replications of each treatment. |
Returns the p-value of the Layard test of homogeneity of variances and its practical interpretation for the significance level of 5%.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima
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.
bartlett, samiuddin,
levene, oneillmathews.
data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "layard")data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "layard")
levene Performs the test for homogeneity of variances
of Levene (1960).
levene(trat, resp, t, r)levene(trat, resp, t, r)
trat |
Numeric or complex vector containing treatments. |
resp |
Numeric or complex vector containing the response variable. |
t |
Number of treatments. |
r |
Numeric or complex vector containing the number of replications of each treatment. |
Returns the p-value of Levene's test of homogeneity of variances and its practical interpretation for significance level of 5%.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima
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.
bartlett, samiuddin,
layard, oneillmathews.
data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "levene")data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "levene")
lsd Performs the t test (LSD) for multiple comparison
of means.
lsd(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)lsd(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response variable. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance level. |
group |
TRUE or FALSE. |
main |
Title. |
Returns the multiple comparison of means according to the LSD test.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
snk, duncan,
ccboot, lsdb,
scottknott, tukey,
ccF.
data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "lsd", sigT = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "lsd", sigT = 0.05)
lsdb Performs the t test (LSD) with Bonferroni's
protection, for multiple comparison of means.
lsdb(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)lsdb(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response variable. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance level. |
group |
TRUE or FALSE. |
main |
Title. |
Returns the multiple comparison of means according to the LSDB test.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
snk, duncan,
ccboot, lsd,
scottknott, tukey,
ccF.
data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "lsdb", sigT = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "lsdb", sigT = 0.05)
oneilldbc Performs the test for homogeneity of
variances of ONeill and Mathews (2002).
oneilldbc(resp, trat, block)oneilldbc(resp, trat, block)
resp |
Numeric or complex vector containing the response variable. |
trat |
Numeric or complex vector containing treatments. |
block |
Numeric or complex vector containing blocks. |
Returns the p-value of ONeill and Mathews' test of homogeneity of variances and its practical interpretation for significance level of 5%.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima
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.
data(ex2) attach(ex2) rbd(trat, provador, aparencia, hvar = "oneillmathews")data(ex2) attach(ex2) rbd(trat, provador, aparencia, hvar = "oneillmathews")
oneillmathews Performs the test for homogeneity of
variances of ONeill and Mathews (2000).
oneillmathews(trat, resp, t, r)oneillmathews(trat, resp, t, r)
trat |
Numeric or complex vector containing treatments. |
resp |
Numeric or complex vector containing the response variable. |
t |
Number of treatments. |
r |
Numeric or complex vector containing the number of replications of each treatment. |
Returns the p-value of ONeill and Mathews' test of homogeneity of variances and its practical interpretation for significance level of 5%.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima
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.
bartlett, layard,
levene, samiuddin.
data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "oneillmathews", sigF = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "oneillmathews", sigF = 0.05)
order.group It orders the groups of means.
order.group(trt, means, N, MSerror, Tprob, std.err, parameter = 1)order.group(trt, means, N, MSerror, Tprob, std.err, parameter = 1)
trt |
Treatments. |
means |
Means of treatment. |
N |
Replications. |
MSerror |
Mean square error. |
Tprob |
Minimum value for the comparison. |
std.err |
Standard error. |
parameter |
Constante 1 (Sd), 0.5 (Sx). |
trt Factor
means Numeric
N Numeric
MSerror Numeric
Tprob value between 0 and 1
std.err Numeric
parameter Constant
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)
order.stat.SNK Orders the groups of means according
to the test of SNK.
order.stat.SNK(treatment, means, minimum)order.stat.SNK(treatment, means, minimum)
treatment |
Treatment. |
means |
Means of treatment. |
minimum |
Minimum value for the comparison. |
trt Factor
means Numeric
minimum Numeric
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)
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).
plotres(x)plotres(x)
x |
Output from anova (performed in ExpDes). |
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @note The default produces four plots regarding the ANOVA assumptions.
STEEL, R. G. D.; TORRIE, J. H. Principles and procedures in Statistics: a biometrical approach. McGraw-Hill, New York, NY. 1980.
data(ex1) attach(ex1) a<-crd(trat, ig) plotres(a)data(ex1) attach(ex1) a<-crd(trat, ig) plotres(a)
rbd Analyses experiments in balanced Randomized
Blocks Designs under one single factor, considering a fixed
model.
rbd( treat, block, resp, quali = TRUE, mcomp = "tukey", nl = FALSE, hvar = "oneillmathews", sigT = 0.05, sigF = 0.05, unfold = NULL )rbd( treat, block, resp, quali = TRUE, mcomp = "tukey", nl = FALSE, hvar = "oneillmathews", sigT = 0.05, sigF = 0.05, unfold = NULL )
treat |
Numeric or complex vector containing the treatments. |
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-Knot ('sk'), the Calinski and Corsten test ('ccF') and bootstrap multiple comparison's test ('ccboot'). |
nl |
Logic. If FALSE (default) linear regression models are adjusted. IF TRUE, non-linear regression models are adjusted. |
hvar |
Allows choosing the test for homogeneity of variances; the default is the test of ONeill and Mathews ('oneillmathews'), however there are other options: test of Han ('han'), and the test of Anscombe and Tukey ('anscombetukey'). |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to construct
regression plots and plotres for residuals
plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
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.
fat2.rbd, fat3.rbd,
split2.rbd, strip,
fat2.ad.rbd and fat3.ad.rbd.
data(ex2) attach(ex2) rbd(trat, provador, aparencia, quali = TRUE, mcomp = "lsd", hvar = "oneillmathews", sigT = 0.05, sigF = 0.05, unfold=NULL)data(ex2) attach(ex2) rbd(trat, provador, aparencia, quali = TRUE, mcomp = "lsd", hvar = "oneillmathews", sigT = 0.05, sigF = 0.05, unfold=NULL)
reg.nl Adjusts non-linear regression models in Anova
(Models: Power, Exponential, Logistic, Gompertz).
reg.nl(resp, treat)reg.nl(resp, treat)
resp |
Numeric or complex vector containing the response variable. |
treat |
Numeric or complex vector containing the treatments. |
Returns coefficients, significance and ANOVA of the fitted regression models.
Eric B Ferreira, [email protected]
Luiz Alberto Beijo
DRAPER, N.R.; SMITH, H. Apllied regression analysis. 3ed. New York : John Wiley, 1998. 706p.
data(exnl) attach(exnl) x<-crd(trat, resp, quali = FALSE, nl = TRUE) graphics(x, degree = "log")data(exnl) attach(exnl) x<-crd(trat, resp, quali = FALSE, nl = TRUE) graphics(x, degree = "log")
reg.poly Fits sequential regression models until the
third power.
reg.poly(resp, treat, DFerror, SSerror, DFtreat, SStreat)reg.poly(resp, treat, DFerror, SSerror, DFtreat, SStreat)
resp |
Numeric or complex vector containing the response variable. |
treat |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
DFtreat |
Treatments' dregrees of freedom. |
SStreat |
Treatments' sum of squares. |
Returns coefficients, significance and ANOVA of the fitted regression models.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
GOMES, F. P. Curso de Estatistica Experimental. 10a ed. Piracicaba: ESALQ/USP. 1982. 430.
Response variable form the additional treatment.
data(respAd)data(respAd)
Numeric vector.
Eric Batista Ferreira, [email protected]
samiuddin Performs the test for homogeneity of
variances of Samiuddin (1976).
samiuddin(trat, resp, t, r)samiuddin(trat, resp, t, r)
trat |
Numeric or complex vector containing treatments. |
resp |
Numeric or complex vector containing the response variable. |
t |
Number of treatments. |
r |
Numeric or complex vector containing the number of replications of each treatment. |
Returns the p-value of Samiuddin's test of homogeneity of variances and its practical interpretation for significance level of 5%.
Eric B Ferreira, [email protected] @author Denismar Alves Nogueira @author Marcos Costa de Paula @author Mateus Pimenta Siqueira Lima
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.
bartlett, layard,
levene, oneillmathews.
data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "samiuddin", sigF = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = FALSE, hvar = "samiuddin", sigF = 0.05)
scottknott Performs the test of Scott-Knott, for
multiple comparison of means.
scottknott(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)scottknott(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response variable. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance level. |
group |
TRUE or FALSE. |
main |
Title. |
Returns the multiple comparison of means according to the test of Scott-Knott.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti (Adapted from Laercio Junio da Silva - GPL(>=2))
RAMALHO, M. A. P.; FERREIRA, D. F.; OLIVEIRA, A. C. de. Experimentacao em Genetica e Melhoramento de Plantas. 2a ed. Lavras: UFLA. 2005. 300p.
snk, duncan,
lsd, lsdb, ccboot,
tukey, ccF.
data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "sk", sigT = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "sk", sigT = 0.05)
Response variable (dry biomass) of the additional treatment of the experiment about composting.
data(secaAd)data(secaAd)
Numeric vector.
Eric Batista Ferreira, [email protected]
snk Performs the test of SNK, for multiple
comparison of means.
snk(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)snk(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response variable. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance level. |
group |
TRUE or FALSE. |
main |
Title. |
Returns the multiple comparison of means according to the test of SNK.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
scottknott, duncan,
lsd, lsdb, ccboot,
tukey, ccF.
data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "snk", sigT = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "snk", sigT = 0.05)
split2.crd Analyses experiments in Split-plot scheme
in balanced Completely Randomized Design, considering a
fixed model.
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 )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 )
factor1 |
Numeric or complex vector containing the factor 1 levels. |
factor2 |
Numeric or complex vector containing the factor 2 levels. |
repet |
Numeric or complex vector containing the replications. |
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%. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.
split2.rbd and strip.
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)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)
split2.rbd Analyses experiments in Split-plot scheme
in balanced Randomized Blocks Design, considering a
fixed model.
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 )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 )
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%. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti
BANZATTO, D. A.; KRONKA, S. N. Experimentacao Agricola. 4 ed. Jaboticabal: Funep. 2006. 237 p.
split2.crd and strip.
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)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 Analysis Strip-plot experiments.
strip( factor1, factor2, block, resp, quali = c(TRUE, TRUE), mcomp = "tukey", fac.names = c("F1", "F2"), sigT = 0.05, sigF = 0.05, unfold = NULL )strip( factor1, factor2, block, resp, quali = c(TRUE, TRUE), mcomp = "tukey", fac.names = c("F1", "F2"), sigT = 0.05, sigF = 0.05, unfold = NULL )
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%. |
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. |
The arguments sigT and mcomp will be used only when the treatment are qualitative.
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).
The graphics can be used to
construct regression plots and plotres
for residuals plots.
Eric B Ferreira, [email protected]
Laís Brambilla Storti Ferreira
split2.rbd and rbd.
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)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)
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.
tapply.stat(y, x, stat = "mean")tapply.stat(y, x, stat = "mean")
y |
Data.frame variables. |
x |
Data.frame factors. |
stat |
Method. |
y Numeric x Numeric stat method = "mean", ...
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)
tukey Performs the test of Tukey, for multiple
comparison of means.
tukey(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)tukey(y, trt, DFerror, SSerror, alpha = 0.05, group = TRUE, main = NULL)
y |
Numeric or complex vector containing the response variable. |
trt |
Numeric or complex vector containing the treatments. |
DFerror |
Error degrees of freedom. |
SSerror |
Error sum of squares. |
alpha |
Significance level. |
group |
TRUE or FALSE. |
main |
Title. |
It is necessary first makes a analysis of variance.
y Numeric trt factor DFerror Numeric MSerror Numeric alpha Numeric group Logic main Text
Eric B Ferreira, [email protected]
Denismar Alves Nogueira
Portya Piscitelli Cavalcanti (Adapted from Felipe de Mendiburu - GPL)
Principles and procedures of statistics a biometrical approach Steel and Torry and Dickey. Third Edition 1997
scottknott, duncan,
lsd, lsdb, ccboot,
snk, ccF.
data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "tukey", sigT = 0.05)data(ex1) attach(ex1) crd(trat, ig, quali = TRUE, mcomp = "tukey", sigT = 0.05)