curve_gen: Consonance Functions For Linear Models, Generalized Linear...

In Zadchow/concurve: Computes & Plots Compatibility (Confidence), Surprisal, & Likelihood Distributions

Consonance Functions For Linear Models, Generalized Linear Models, and Robust Linear Models

Description

Computes thousands of consonance (confidence) intervals for the chosen parameter in the selected model (linear models, general linear models, robust linear models, and generalized least squares and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values. Can also adjust for multiple comparisons. It is generally recommended to wrap this function using suppressMessages() due to the long list of profiling messages.

Usage

curve_gen(model, var, method = "lm", log = FALSE, penalty = NULL,
  m = NULL, steps = 1000, cores = getOption("mc.cores", 1L),
  table = TRUE)

Arguments

model	The statistical model of interest (ANOVA, regression, logistic regression) is to be indicated here.
var	The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced.
method	Chooses the method to be used to calculate the consonance intervals. There are currently five methods: "lm", rms::ols objects can be used with the "lm" option, "rlm", "glm" and "aov", and "gls". The "lm" method uses the profile likelihood method to compute intervals and can be used for models created by the 'lm' function. It is typically what most people are familiar with when computing intervals based on the calculated standard error. The ols function from the rms package can also be used for this option. The "rlm" method is designed for usage with the "rlm" function from the MASS package. The "glm" method allows this function to be used for specific scenarios like logistic regression and the 'glm' function. Similarly, the Glm function from the rms package can also be used for this option. The gls method allows objects from gls() or from Gls() from the rms package.
log	Determines whether the coefficients will be exponentiated or not. By default, it is off and set to FALSE or F, but changing this to TRUE or T, will exponentiate the results which may be useful if trying to view the results from a logistic regression on a scale that is not logarithmic.
penalty	An input to specify whether the confidence intervals should be corrected for multiple comparisons. The default is NULL, so there is no correction. Other options include "bonferroni" and "sidak".
m	Indicates how many comparisons are being done and the number that should be used to correct for multiple comparisons. The default is NULL.
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 data frame.
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.

Value

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.

Examples

## Not run: 
# Simulate random data
GroupA <- rnorm(50)
GroupB <- rnorm(50)
RandomData <- data.frame(GroupA, GroupB)
rob <- lm(GroupA ~ GroupB, data = RandomData)
bob <- curve_gen(rob, "GroupB")

## End(Not run)

Zadchow/concurve documentation built on Jan. 11, 2024, 4:55 a.m.