Remove is_paired/paired for repeated measures

[LI-Yixuan]

1. slopeplot corrected 2.paired is removed

[LI-Yixuan]

Example of shared control experiments:

import numpy as np import pandas as pd import dabest import pylab import matplotlib.pyplot as plt

from scipy.stats import norm np.random.seed(9999) Ns = 20 # The number of samples taken from each population

c1 = norm.rvs(loc=3, scale=0.4, size=Ns) c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns) c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns) t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns) t3 = norm.rvs(loc=3, scale=0.75, size=Ns) t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns) t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns) t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

females = np.repeat('Female', Ns/2).tolist() males = np.repeat('Male', Ns/2).tolist() gender = females + males

id_col = pd.Series(range(1, Ns+1))

df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, 'Control 2' : c2, 'Test 2' : t2, 'Control 3' : c3, 'Test 3' : t3, 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6, 'Gender' : gender, 'ID' : id_col })

shared_control = dabest.load(df , idx=(("Control 1", "Test 1", "Test 2","Test 3"), ("Control 2", "Test 4","Test 5", "Test 6") ))

shared_control.mean_diff.plot(color_col="Gender"); pylab.show()

Example of repeated measures against baseline experiments:

baseline = dabest.load(df, id_col = "ID", idx=(("Control 1", "Test 1", "Test 2","Test 3"), ("Control 2", "Test 4","Test 5", "Test 6") ), repeated_measures="baseline")

baseline.mean_diff.plot(color_col="Gender"); pylab.show()

Example of sequential design of repeated measures experiments:

sequential = dabest.load(df, id_col = "ID", idx=(("Control 1", "Test 1", "Test 2","Test 3"), ("Control 2", "Test 4","Test 5", "Test 6") ), repeated_measures="sequential")

sequential.mean_diff.plot(color_col="Gender"); pylab.show()

[josesho]

I'll take a look soon; just want to know how this is different from #112?

[LI-Yixuan]

I'll take a look soon; just want to know how this is different from #112?

They are generally the same except that paired and is_paired are removed in this version, only repeated_measures is used. And the slopeplot is implemented for both baseline and sequential repeated measures.

[josesho]

They are generally the same except that paired and is_paired are removed in this version, only repeated_measures is used.

So paired as a keyword is totally gone? This means that current paired plots no longer function?

[LI-Yixuan]

They are generally the same except that paired and is_paired are removed in this version, only repeated_measures is used.

So paired as a keyword is totally gone? This means that current paired plots no longer function?

Yep in this version I only have repeated_measures as a parameter in load(), because paired is kind of a duplicate of repeated_measures; is_paired is replaced by repeated_measures: instead of checking if is_paired is True, currently the code checks for repeated_measures is not None. Paired plots still work but need to be specified by repeated_measures = "sequential" or repeated_measures = "baseline"

[LI-Yixuan]

They are generally the same except that paired and is_paired are removed in this version, only repeated_measures is used.

So paired as a keyword is totally gone? This means that current paired plots no longer function?

They are generally the same except that paired and is_paired are removed in this version, only repeated_measures is used.

So paired as a keyword is totally gone? This means that current paired plots no longer function?

Yep in this version I only have repeated_measures as a parameter in load(), because paired is kind of a duplicate of repeated_measures; is_paired is replaced by repeated_measures: instead of checking if is_paired is True, currently the code checks for repeated_measures is not None. Paired plots still work but need to be specified by repeated_measures = "sequential" or repeated_measures = "baseline"

Hmmm or perhaps I could propose another way of implementation:

• keep paired and repeated_measures
• keep is_paired: is_paired is not a boolean representation of paired in load(), but rather a representation of whether the comparisons of data are paired; is_paired = True when 1) paired=True 2) repeated_measures = 'sequential' 3) repeated_measures='baseline'

Will this be better?

[LI-Yixuan]

In this version, repeated_measures is completely replaced by paired/is_paired and paired = "baseline" now produces a multi-paired plot. A demo is here:

import numpy as np
import pandas as pd
import dabest
import pylab
import matplotlib.pyplot as plt

from scipy.stats import norm
np.random.seed(9999) # Fix the seed so the results are replicable.
# pop_size = 10000 # Size of each population.
Ns = 20 # The number of samples taken from each population

c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

females = np.repeat('Female', Ns/2).tolist()
males = np.repeat('Male', Ns/2).tolist()
gender = females + males

id_col = pd.Series(range(1, Ns+1))

df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
                     'Control 2' : c2,     'Test 2' : t2,
                     'Control 3' : c3,     'Test 3' : t3,
                     'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
                     'Gender'    : gender, 'ID'  : id_col
                    })

sequential = dabest.load(df, id_col = "ID", idx=(("Control 1", "Test 1",
                                       "Test 2","Test 3"),
                                       ("Control 2", "Test 4","Test 5", "Test 6")
                                     ), paired = "sequential")

sequential.mean_diff.plot(color_col="Gender");
pylab.show()

baseline = dabest.load(df, id_col = "ID", idx=(("Control 1", "Test 1",
                                       "Test 2","Test 3"),
                                       ("Control 2", "Test 4","Test 5", "Test 6")
                                     ), paired = "baseline")

baseline.mean_diff.plot(color_col="Gender");
pylab.show()

baseline.mean_diff.plot(color_col="Gender", show_pairs = False);
pylab.show()

# example of shared-control plot
shared = dabest.load(df, id_col = "ID", idx=(("Control 1", "Test 1",
                                       "Test 2","Test 3"),
                                       ("Control 2", "Test 4","Test 5", "Test 6")
                                     ))

shared.mean_diff.plot(color_col="Gender");
pylab.show()

[LI-Yixuan]

Here is an example using 0to2_beforeduringafter.csv data:

import numpy as np
import pandas as pd
import dabest
import pylab
import matplotlib.pyplot as plt

data = pd.read_csv("0to2_beforeduringafter.csv").dropna()
data = data.rename(columns={'120beforeFeedSpeed_mm/s_Mean':"before",
                            'duringFeedSpeed_mm/s_Mean':"during",
                            '120afterFeedSpeed_mm/s_Mean':"after"})

# example of sequential repeated measures
sequential = dabest.load(data, id_col = 'ChamberID', idx=("before", "during", "after"),
                                        paired = "sequential")

sequential.mean_diff.plot(color_col="Sex");
pylab.show()

# example of baseline repeated measures
baseline = dabest.load(data, id_col = 'ChamberID', idx=("before", "during", "after"),
                                        paired = "baseline")

baseline.mean_diff.plot(color_col="Sex");
pylab.show()

baseline.mean_diff.plot(color_col="Sex", show_pairs = False, raw_marker_size=1.7);
pylab.show()

# example of shared-control plot
shared = dabest.load(data, id_col = 'ChamberID', idx=("before", "during", "after"))

shared.mean_diff.plot(color_col="Sex", raw_marker_size=1.7);
pylab.show()

[adamcc]

I'm sorry, this doesn't fit with our needs. I would prefer to revert to the previous functionality.

[josesho]

I'm sorry, this doesn't fit with our needs. I would prefer to revert to the previous functionality.

Actually Adam, I think this might be a more functional/ergonomic design of the API. Let me take a closer look!

[LI-Yixuan]

Example of the lastest rm baseline plot:

import numpy as np
import pandas as pd
import dabest
import pylab
import matplotlib.pyplot as plt

from scipy.stats import norm
np.random.seed(9999) # Fix the seed so the results are replicable.
# pop_size = 10000 # Size of each population.
Ns = 20 # The number of samples taken from each population

# Create samples
c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

# Add a `gender` column for coloring the data.
females = np.repeat('Female', Ns/2).tolist()
males = np.repeat('Male', Ns/2).tolist()
gender = females + males

# Add an `id` column for paired data plotting.
id_col = pd.Series(range(1, Ns+1))

# Combine samples and gender into a DataFrame.
df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
                     'Control 2' : c2,     'Test 2' : t2,
                     'Control 3' : c3,     'Test 3' : t3,
                     'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
                     'Gender'    : gender, 'ID'  : id_col
                    })

# example of baseline repeated measures
baseline = dabest.load(df, id_col = "ID", idx=(("Control 1", "Test 1",
                                       "Test 2","Test 3"),
                                       ("Control 2", "Test 4","Test 5", "Test 6")
                                     ), paired = "baseline")

baseline.mean_diff.plot(color_col="Gender");
pylab.show()

baseline.mean_diff.plot(color_col="Gender", show_pairs = False);
pylab.show()