Description Usage Arguments Details Value Examples

`MRtest`

applies the Skott-Knott midrange, Skott-Knott range,
Student-Newman-Keuls midrange and Tukey midrange tests. These are new
tests for multiple comparisons proposed by the authors (2015), that are in publication
fase.

1 2 3 |

`y` |
Model (aov or lm), numeric vector containing the response variable or the mean of the treatments. |

`trt` |
Constant (y = model) or a vector containing the treatments. |

`dferror` |
Degrees of freedom of the Mean Square Error. |

`mserror` |
Mean Square Error. |

`replication` |
Number de repetitions of the treatments in the experiment.
For unbalanced data should be informed the harmonic mean of repetitions.
This argument should also be informed if |

`alpha` |
Significant level. The default is |

`main` |
Title of the analysis. |

`MCP` |
Allows choosing the multiple comparison test;
the |

`ismean` |
Logic. If |

The `MCP`

argument allows you to choose various tests
of multiple comparisons at once. For example,
`MCP = c("SKM", "SKR")`

, and so on.

`MRtest`

returns the print of a list of results. First,
the summary of `y`

. Second, the statistics
of the test chosen. And finally, the mean group results for each test.
If `MRtest`

function is stored
in an object, the results will be printed and
also stored in the object.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | ```
# Simulated data (completely randomized design)
# 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: SKM test",
MCP = c("SKM"))
# 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: SKM test",
MCP = c("SKM"))
# 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: SKR test
MRtest(y = res, trt = "treat", alpha = 0.05,
main = "Multiple Comparison Procedure: SKR test",
MCP = c("SKR"))
# 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: SKM test",
MCP = c("SKM"),
ismean = TRUE)
``` |

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