elbow_variance {ELBOW} | R Documentation

## calculates the variance for the upper and lower limits of an Elbow curve

### Description

The following function determines the variation of cut off limits for significance between individual subsets. The first step is to generate the subsets, then the function determines the highest and lowest values for the upper limit and the highest and lowest values for the lower limit. These values are used to determine and upper and lower error value reflecting fold dataset variance.

### Usage

      elbow_variance(probes, initial_conditions,
        final_conditions)

### Arguments

`probes` | the data set of probes.
`initial_conditions` | a data set of replicates corresponding to initial conditions.
`final_conditions` | a data set of replicates corresponding to final conditions.

### Value

a list containing the following keys:

- max_upper — the maximum upper elbow limit (most positive)

- min_upper — the minimum upper elbow limit

- max_upper — the maximum lower elbow limit

- min_upper — the minimum lower elbow limit (most negative)

### Examples

    # read in the EcoliMutMA sample data from the package
    		data(EcoliMutMA, package="ELBOW")
    		csv_data <- EcoliMutMA
    		# - OR - Read in a CSV file (uncomment - remove the #'s
    		#        - from the line below and replace 'filename' with
    		#        the CSV file's filename)
    		# csv_data <- read.csv(filename)

    		# set the number of initial and final condition replicates both to three
    		init_count  <- 3
    		final_count <- 3

    		# Parse the probes, intial conditions and final conditions
    		# out of the CSV file.  Please see: extract_working_sets
    		# for more information.
    		#
    		# init_count should be the number of columns associated with
    		#       the initial conditions of the experiment.
    		# final_count should be the number of columns associated with
    		#       the final conditions of the experiment.
    		working_sets <- extract_working_sets(csv_data, init_count, final_count)

    		probes <- working_sets[[1]]
    		initial_conditions <- working_sets[[2]]
    		final_conditions <- working_sets[[3]]

    		# Uncomment to output the plot to a PNG file (optional)
    		# png(file="output_plot.png")

    		my_data <- replicates_to_fold(probes, initial_conditions, final_conditions)

    		# compute the elbow for the dataset
    		limits <- do_elbow(data.frame(my_data$fold))

    		plus_minus <- elbow_variance(probes, initial_conditions, final_conditions)

[Package ELBOW version 1.16.0 Index]