Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test that compares means and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values.

curve_mean(x, y, data, paired = F, method = "default", replicates = 1000, steps = 10000, cores = getOption("mc.cores", 1L), table = TRUE)

x | Variable that contains the data for the first group being compared. |
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y | Variable that contains the data for the second group being compared. |

data | Data frame from which the variables are being extracted from. |

paired | Indicates whether the statistical test is a paired difference test. By default, it is set to "F",which means the function will be an unpaired statistical test comparing two independent groups.Inserting "paired" will change the test to a paired difference test. |

method | By default this is turned off (set to "default"), but allows for bootstrapping if "boot" is inserted into the function call. |

replicates | Indicates how many bootstrap replicates are to be performed. The default is currently 20000 but more may be desirable, especially to make the functions more smooth. |

steps | Indicates how many consonance intervals are to be calculated at various levels. For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe. |

cores | Select the number of cores to use in order to compute the intervals The default is 1 core. |

table | Indicates whether or not a table output with some relevant statistics should be generated. The default is TRUE and generates a table which is included in the list object. |

A list with 3 items where the dataframe of values is in the first object, the values needed to calculate the density function in the second, and the table for the values in the third if table = TRUE.

# Simulate random data GroupA <- runif(100, min = 0, max = 100) GroupB <- runif(100, min = 0, max = 100) RandomData <- data.frame(GroupA, GroupB) bob <- curve_mean(GroupA, GroupB, RandomData)