dbc.ad: Analysis: Randomized block design with an additional...

In AgroR: Experimental Statistics and Graphics for Agricultural Sciences

Analysis: Randomized block design with an additional treatment for quantitative factor

Description

Statistical analysis of experiments conducted in a randomized block design with an additional treatment and balanced design with a factor considering the fixed model.

Usage

dbc.ad(
  trat,
  block,
  response,
  responsead,
  grau = 1,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  theme = theme_classic(),
  ylab = "response",
  xlab = "independent",
  family = "sans",
  posi = "top",
  pointsize = 4.5,
  linesize = 0.8,
  width.bar = NA,
  point = "mean_sd"
)

Arguments

trat	Numerical or complex vector with treatments
block	Numerical or complex vector with blocks
response	Numerical vector containing the response of the experiment.
responsead	Numerical vector with additional treatment responses
grau	Degree of polynomial in case of quantitative factor (default is 1)
norm	Error normality test (default is Shapiro-Wilk)
homog	Homogeneity test of variances (default is Bartlett)
alpha.f	Level of significance of the F test (default is 0.05)
theme	ggplot2 theme (default is theme_classic())
ylab	Variable response name (Accepts the expression() function)
xlab	Treatments name (Accepts the expression() function)
family	Font family
posi	Legend position
pointsize	Point size
linesize	line size (Trendline and Error Bar)
width.bar	width of the error bars of a regression graph.
point	Defines whether to plot mean ("mean"), mean with standard deviation ("mean_sd" - default) or mean with standard error (default - "mean_se"). For quali=FALSE or quali=TRUE.

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, adjustment of regression models up to grade 3 polynomial. The function also returns a standardized residual plot.

Note

In some experiments, the researcher may study a quantitative factor, such as fertilizer doses, and present a control, such as a reference fertilizer, treated as a qualitative control. In these cases, there is a difference between considering only the residue in the unfolding of the polynomial, removing or not the qualitative treatment, or since a treatment is excluded from the analysis. In this approach, the residue used is also considering the qualitative treatment, a method similar to the factorial scheme with additional control.

Author(s)

Gabriel Danilo Shimizu, [e-mail]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

doses = c(rep(c(1:5),e=3))
resp = c(3, 4, 3, 5, 5, 6, 7, 7, 8, 4, 4, 5, 2, 2, 3)
bloco = rep(c("B1","B2","B3","B4","B5"),3)
dbc.ad(doses, bloco, resp, responsead=rnorm(3,6,0.1),grau=2)

AgroR documentation built on May 29, 2024, 4:18 a.m.