plot_elbow {ELBOW} | R Documentation |
Plots an elbow curve and its associated data:
the upper and lower elbow limits for the curve
the upper, lower, and median initial condition Elbow plots
the \logχ^2 p-value for the Elbow curve
the variance for the upper and lower Elbow cut-off values
plot_elbow(my_data, upper_limit, lower_limit, pvalue1, prowmin, prowmax, prowmedian, max_upper_variance, min_upper_variance, max_lower_variance, min_lower_variance, gtitle = "")
my_data |
A table (data.frame) to plot. The columns in the table should be as follows:
|
upper_limit |
the upper limit/cut-off for the elbow. |
lower_limit |
the lower limit/cut-off for the elbow. |
pvalue1 |
the \logχ^2 p-value for the elbow curve. |
prowmin |
the error limit based on the maximum value for each probe. |
prowmax |
the error limit based on the minimum value for each probe. |
prowmedian |
the null (median) value for each probe. |
max_upper_variance |
the maximum upper elbow limit (most positive). |
min_upper_variance |
the minimum upper elbow limit. |
max_lower_variance |
the maximum lower elbow limit. |
min_lower_variance |
the minimum lower elbow limit (most negative). |
gtitle |
the title to display for the graph. |
# 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) plot_elbow(my_data, upper_limit, lower_limit, pvalue1, prowmin, prowmax, prowmedian, max_upper_variance, min_upper_variance, max_lower_variance, min_lower_variance, "Title of the ELBOW Plot")