Data imported from txt tables to R.
feces <- read.csv("~/git/preservation_stats/presv_fig1_data/feces_fig1.csv")
oral <- read.csv("~/git/preservation_stats/presv_fig1_data/oral_fig1.csv")
skin <- read.csv("~/git/preservation_stats/presv_fig1_data/skin_fig1.csv")Each Preservation method will be extracted from their respective data tables
with a parser function for this porpose written in R named 'generate_vector()':
Example:
from BodySite 'feces'
feces_ultralow = parse_vector(data = feces, line = 1)## [1] 18045 6458 2959 12129 7648
Then analyze through graphic of distribution to determine their
distribution if any
# distribution_test() makes a summary of information from the data set and
# tries ploting a normal,uniform and exponential distribution graph from
# this data to fit the histogram of the sample.
# The aim of the graph-fit plot is to find ( if any) a distribution like
# behavior that could fit the data for testing and comparisons.
distribution_test(feces_ultralow)
As seen in the plot, the data doesnt fit any of the distribution
to which they where tested. This left us with only one posibility
being that our samples are to small, the only viable option is
to use a student-t test.
Since we need to find normality to used a student-t,
Shapiro-Wilk Normality test is performed on each Presv. Method.
shapiro.test(feces_ultralow)##
## Shapiro-Wilk normality test
##
## data: feces_ultralow
## W = 0.9592, p-value = 0.8023
# Test P-value should be >= .95 to be consider a normal distribution thruogh
# this test.