Last chance! 50% off unlimited learning
Sale ends in
Performs one or multiple mean comparisons.
compare_means(formula, data, method = "wilcox.test", paired = FALSE,
group.by = NULL, ref.group = NULL, symnum.args = list(),
p.adjust.method = "holm", ...)a formula of the form x ~ group where x is a
numeric variable giving the data values and group is a factor with
one or multiple levels giving the corresponding groups. For example,
formula = TP53 ~ cancer_group.
It's also possible to perform the test for multiple response variables at
the same time. For example, formula = c(TP53, PTEN) ~ cancer_group.
a data.frame containing the variables in the formula.
the type of test. Default is wilcox.test. Allowed values include:
t.test (parametric) and
wilcox.test (non-parametric). Perform comparison
between two groups of samples. If the grouping variable contains more than
two levels, then a pairwise comparison is performed.
anova (parametric) and
kruskal.test (non-parametric). Perform one-way ANOVA
test comparing multiple groups.
a logical indicating whether you want a paired test. Used only
in t.test and in wilcox.test.
a character vector containing the name of grouping variables.
a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group).
ref.group can be also ".all.". In this case, each of the
grouping variable levels is compared to all (i.e. basemean).
a list of arguments to pass to the function
symnum for symbolic number coding of p-values. For
example, symnum.args <- list(list(cutpoints = c(0, 0.0001, 0.001,
0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))).
In other words, we use the following convention for symbols indicating statistical significance:
ns: p > 0.05
*: p <= 0.05
**: p <= 0.01
***: p <= 0.001
****: p <= 0.0001
method for adjusting p values (see
p.adjust). Has impact only in a situation, where
multiple pairwise tests are performed; or when there are multiple grouping
variables. Allowed values include "holm", "hochberg", "hommel",
"bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p
value (not recommended), use p.adjust.method = "none".
Note that, when the formula contains multiple variables, the p-value
adjustment is done independently for each variable.
Other arguments to be passed to the test function.
return a data frame with the following columns:
.y.: the y variable used in the test.
group1,group2: the compared groups in the pairwise tests.
Available only when method = "t.test" or method = "wilcox.test".
p: the p-value.
p.adj: the adjusted p-value. Default for p.adjust.method = "holm".
p.format: the formatted p-value.
p.signif: the significance level.
method: the statistical test used to compare groups.
# NOT RUN {
# Load data
#:::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth
# One-sample test
#:::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ 1, df, mu = 0)
# Two-samples unpaired test
#:::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ supp, df)
# Two-samples paired test
#:::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ supp, df, paired = TRUE)
# Compare supp levels after grouping the data by "dose"
#::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ supp, df, group.by = "dose")
# pairwise comparisons
#::::::::::::::::::::::::::::::::::::::::
# As dose contains more thant two levels ==>
# pairwise test is automatically performed.
compare_means(len ~ dose, df)
# Comparison against reference group
#::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ dose, df, ref.group = "0.5")
# Comparison against all
#::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ dose, df, ref.group = ".all.")
# Anova and kruskal.test
#::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ dose, df, method = "anova")
compare_means(len ~ dose, df, method = "kruskal.test")
# }
Run the code above in your browser using DataLab