pvaluescmink | R Documentation |
Using the Minkowski dissimilarity/distance for the dissimilarities/distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.
pvaluescmink(
dataset,
formula,
pvalue.method = "permutation",
p = 3,
plot = TRUE,
seed = NULL,
min_group_size = 3
)
dataset |
A dataframe. |
formula |
A variable or variables (two or more) with factors which you want to calculate the Minkowski dissimilarities/distances matrix or matrices (two or more). |
pvalue.method |
A p_value method used to calculate the matrix or matrices (two or more), the default value is "permutation". Another method is "bootstrap". |
p |
Order of the Minkowski dissimilarities/distances. The default value is 3. |
plot |
if TRUE, plot the p_values heatmap or heatmaps (two or more). The default value is TRUE. |
seed |
Optionally, set a seed for "bootstrap" and "permutation". |
min_group_size |
Minimum group size to maintain. The default value is 3, therefore groups, inside variables, with less than 3 observations will be discarded. |
A list containing a matrix of p_values and, optionally, the plot.
When p < 1, the Minkowski distance is a "dissimilarity" measure. When p >= 1, the triangle inequality property is satisfied and we say "Minkowski distance".
# Example with iris dataset
# data(iris)
# Calculate p_values of "Species" variable in iris dataset
pvaluescmink(iris,~Species, p = 3, pvalue.method = "permutation")
# Example with mtcars dataset
data(mtcars)
# Calculate p_values of "am" variable in mtcars dataset
pvaluescmink(mtcars,~am, p = 3,
pvalue.method = "permutation", seed = 100)
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