Statistical analysis of experiments conducted in a randomized block design using a generalized linear model. It performs the deviance analysis and the effect is tested by a chi-square test. Multiple comparisons are adjusted by Tukey.
DBC.glm(
trat,
block,
response,
glm.family = "binomial",
quali = TRUE,
alpha.f = 0.05,
alpha.t = 0.05,
geom = "bar",
theme = theme_classic(),
sup = NA,
ylab = "Response",
xlab = "",
fill = "lightblue",
angle = 0,
family = "sans",
textsize = 12,
labelsize = 5,
dec = 3,
addmean = TRUE,
errorbar = TRUE,
posi = "top",
point = "mean_sd",
angle.label = 0
)
Numerical or complex vector with treatments
Numerical or complex vector with blocks
Numerical vector containing the response of the experiment. Use cbind(resp, n-resp) for binomial or quasibinomial family.
distribution family considered (default is binomial)
Defines whether the factor is quantitative or qualitative (default is qualitative)
Level of significance of the F test (default is 0.05)
Significance level of the multiple comparison test (default is 0.05)
Graph type (columns, boxes or segments)
ggplot2 theme (default is theme_classic())
Number of units above the standard deviation or average bar on the graph
Variable response name (this argument uses the parse function)
Treatments name (this argument uses the parse function)
Defines chart color (to generate different colors for different treatments, define fill = "trat")
x-axis scale text rotation
Font family
Font size
Label size
Number of cells
Plot the average value on the graph (default is TRUE)
Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE
Legend position
Defines whether to plot mean ("mean"), mean with standard deviation ("mean_sd" - default) or mean with standard error (default - "mean_se").
label angle
Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com
Leandro Simoes Azeredo Goncalves
Rodrigo Yudi Palhaci Marubayashi
data("aristolochia")
attach(aristolochia)
# Assuming the same aristolochia data set, but considering randomized blocks
bloco=rep(paste("B",1:16),5)
resp=resp/2
DBC.glm(trat,bloco, cbind(resp,50-resp), glm.family="binomial")
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