library(testthat)
library(midrangeMCP)
test_that("Test of MRtest function",
{
# Response variable
rv <- c(100.08, 105.66, 97.64, 100.11, 102.60, 121.29, 100.80,
99.11, 104.43, 122.18, 119.49, 124.37, 123.19, 134.16,
125.67, 128.88, 148.07, 134.27, 151.53, 127.31)
# Treatments
treat <- factor(rep(LETTERS[1:5], each = 4))
# Anova
res <- anova(aov(rv~treat))
DFerror <- res$Df[2]
MSerror <- res$`Mean Sq`[2]
# Loading the midrangeMCP package
library(midrangeMCP)
# applying the tests
results <- MRtest(y = rv,
trt = treat,
dferror = DFerror,
mserror = MSerror,
alpha = 0.05,
main = "Multiple Comparison Procedure: MGM test",
MCP = c("MGM"))
# Other option for the MCP argument is "all". All tests are used.
results$Groups # Results of the tests
results$Statistics # Main arguments of the tests
results$Summary # Summary of the response variable
# Using the y argument as aov or lm model
res <- aov(rv~treat)
MRtest(y = res, trt = "treat", alpha = 0.05,
main = "Multiple Comparison Procedure: MGM test",
MCP = c("MGM"))
# For unbalanced data: It will be used the harmonic mean of
# the number of experiment replicates
# Using the previous example
rv <- rv[-1]
treat <- treat[-1]
res <- lm(rv~treat) # Linear model
# Multiple comparison procedure: MGR test
MRtest(y = res, trt = "treat", alpha = 0.05,
main = "Multiple Comparison Procedure: MGR test",
MCP = c("MGR"))
# Assuming that the available data are the averages
# of the treatments and the analysis of variance
# Analysis of Variance Table
# Response: rv
# Df Sum Sq Mean Sq F value Pr(>F)
# treat 4 4135.2 1033.80 14.669 4.562e-05 ***
# Residuals 15 1057.1 70.47
mean.treat <- c(100.87, 105.95, 117.62, 127.97, 140.30)
treat <- factor(LETTERS[1:5])
DFerror <- 15
MSerror <- 70.47488
replic <- 4
MRtest(y = mean.treat,
trt = treat,
dferror = DFerror,
mserror = MSerror,
replication = replic,
alpha = 0.05,
main = "Multiple Comparison Procedure: MGM test",
MCP = c("MGM"),
ismean = TRUE)
})
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