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LSD - Test Function - RDocumentation

The LSD.test function in the agricolae package is used for multiple comparisons of treatments using the Least Significant Difference method, with p-values adjusted through various methods. It requires inputs such as the model, treatment vector, degrees of freedom, and mean square error, and it outputs statistics, parameters, means, comparisons, and treatment groups. The function is typically applied after conducting an analysis of variance.

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70 views3 pages

LSD - Test Function - RDocumentation

The LSD.test function in the agricolae package is used for multiple comparisons of treatments using the Least Significant Difference method, with p-values adjusted through various methods. It requires inputs such as the model, treatment vector, degrees of freedom, and mean square error, and it outputs statistics, parameters, means, comparisons, and treatment groups. The function is typically applied after conducting an analysis of variance.

Uploaded by

Fernando Huayta
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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4/7/22, 0:18 LSD.

test function - RDocumentation

RDocumentation

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agricolae (version 1.3-5)

LSD.test: Multiple comparisons, "Least significant


difference" and Adjust P-values

Description

Multiple comparisons of treatments by means of LSD and a grouping of treatments. The level by alpha default is 0.05.
Returns p-values adjusted using one of several methods

Usage

LSD.test(y, trt, DFerror, MSerror, alpha = 0.05, p.adj=c("none","holm","hommel",

"hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)

Arguments

y
model(aov or lm) or answer of the experimental unit

trt
Constant( only y=model) or vector treatment applied to each experimental unit

DFerror
Degrees of freedom of the experimental error

MSerror
Means square error of the experimental

alpha
Level of risk for the test

p.adj
Method for adjusting p values (see p.adjust)

group
TRUE or FALSE

main
title of the study

console
logical, print output
https://www.rdocumentation.org/packages/agricolae/versions/1.3-5/topics/LSD.test 1/3
4/7/22, 0:18 LSD.test function - RDocumentation

Value

statistics
Statistics of the model

parameters
Design parameters

means
Statistical summary of the study variable

comparison
Comparison between treatments

groups
Formation of treatment groups

Details

For equal or different repetition.


For the adjustment methods, see the function p.adjusted.
p-adj ="none" is t-student.

It is necessary first makes a analysis of variance.


if model=y, then to apply the instruction: LSD.test(model, "trt", alpha
= 0.05, p.adj=c("none","holm","hommel", "hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main =
NULL,console=FALSE)
where the model class is aov or lm.

References

Steel, R.; Torri,J; Dickey, D.(1997)


Principles and Procedures of Statistics
A Biometrical Approach. pp178.

See Also

`BIB.test`, `DAU.test`, `duncan.test`,


`durbin.test`, `friedman`, `HSD.test`,
`kruskal`, `Median.test`, `PBIB.test`,
`REGW.test`, `scheffe.test`, `SNK.test`,
`waerden.test`, `waller.test`, `plot.group`

Examples Run this code

# NOT RUN {

library(agricolae)

data(sweetpotato)

model<-aov(yield~virus, data=sweetpotato)

out <- LSD.test(model,"virus", p.adj="bonferroni")

#stargraph
https://www.rdocumentation.org/packages/agricolae/versions/1.3-5/topics/LSD.test 2/3
4/7/22, 0:18 LSD.test function - RDocumentation
sta g ap
# Variation range: max and min

plot(out)

#endgraph

# Old version LSD.test()

df<-df.residual(model)

MSerror<-deviance(model)/df

out <- with(sweetpotato,LSD.test(yield,virus,df,MSerror))

#stargraph
# Variation interquartil range: Q75 and Q25

plot(out,variation="IQR")

#endgraph

out<-LSD.test(model,"virus",p.adj="hommel",console=TRUE)

plot(out,variation="SD") # variation standard deviation

# }

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