Description Usage Arguments Value Examples
The following function determines a p value from the comparison of the slope of a regression line (i.e. logit). The function calculates a fold value from a set of initial conditions (replicate set 1) substracted from a set of final conditions (replicate set 2). The glm function is used to assess the fit of the dataset to the model, assuming family = binomial (link = "probit"), maxit = 1000. Determining the \logχ^2 pvalue for the model, null equals no significance for the set of significant probes to the model, in which case the Elbow method cannot be used to assess the dataset. If significant probes are important to the model, then the \logχ^2 pvalue will be below 0.05, in which case the Elbow method can be used to assess the dataset.
1  get_pvalue(my_data, upper_limit, lower_limit)

my_data 
A table (data.frame) to analyze the elbow variance of. The columns in the table should be as follows:

upper_limit 
the upper limit/cutoff for the elbow. 
lower_limit 
the lower limit/cutoff for the elbow. 
The calculated \logχ^2 pvalue for the elbow curve.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54  # 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$fold))
plus_minus < elbow_variance(probes, initial_conditions, final_conditions)
max_upper_variance < plus_minus$max_upper
min_upper_variance < plus_minus$min_upper
max_lower_variance < plus_minus$max_lower
min_lower_variance < plus_minus$min_lower
# rounded number for nice appearance graph
upper_limit < round(limits[[1]],digits=2)
# rounded number nice appearance for graph
lower_limit < round(limits[[2]],digits=2)
p_limits < null_variance(my_data, upper_limit, lower_limit, initial_conditions)
prowmin < p_limits[[1]]
prowmax < p_limits[[2]]
prowmedian < p_limits[[3]]
pvalue1 < get_pvalue(my_data, upper_limit, lower_limit)

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