def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
xlabel=None, ylabel=None, colors=[], markers=[],
linestyles=[], legendloc='best', legendtitle=None,
**kwargs):
"""
Interaction plot for factor level statistics.
Note. If categorial factors are supplied levels will be internally
recoded to integers. This ensures matplotlib compatiblity.
uses pandas.DataFrame to calculate an `aggregate` statistic for each
level of the factor or group given by `trace`.
Parameters
----------
x : array-like
The `x` factor levels constitute the x-axis. If a `pandas.Series` is
given its name will be used in `xlabel` if `xlabel` is None.
trace : array-like
The `trace` factor levels will be drawn as lines in the plot.
If `trace` is a `pandas.Series` its name will be used as the
`legendtitle` if `legendtitle` is None.
response : array-like
The reponse or dependent variable. If a `pandas.Series` is given
its name will be used in `ylabel` if `ylabel` is None.
func : function
Anything accepted by `pandas.DataFrame.aggregate`. This is applied to
the response variable grouped by the trace levels.
plottype : str {'line', 'scatter', 'both'}, optional
The type of plot to return. Can be 'l', 's', or 'b'
ax : axes, optional
Matplotlib axes instance
xlabel : str, optional
Label to use for `x`. Default is 'X'. If `x` is a `pandas.Series` it
will use the series names.
ylabel : str, optional
Label to use for `response`. Default is 'func of response'. If
`response` is a `pandas.Series` it will use the series names.
colors : list, optional
If given, must have length == number of levels in trace.
linestyles : list, optional
If given, must have length == number of levels in trace.
markers : list, optional
If given, must have length == number of lovels in trace
kwargs
These will be passed to the plot command used either plot or scatter.
If you want to control the overall plotting options, use kwargs.
Returns
-------
fig : Figure
The figure given by `ax.figure` or a new instance.
Examples
--------
>>> import numpy as np
>>> np.random.seed(12345)
>>> weight = np.random.randint(1,4,size=60)
>>> duration = np.random.randint(1,3,size=60)
>>> days = np.log(np.random.randint(1,30, size=60))
>>> fig = interaction_plot(weight, duration, days,
... colors=['red','blue'], markers=['D','^'], ms=10)
>>> import matplotlib.pyplot as plt
>>> plt.show()
.. plot::
import numpy as np
from statsmodels.graphics.factorplots import interaction_plot
np.random.seed(12345)
weight = np.random.randint(1,4,size=60)
duration = np.random.randint(1,3,size=60)
days = np.log(np.random.randint(1,30, size=60))
fig = interaction_plot(weight, duration, days,
colors=['red','blue'], markers=['D','^'], ms=10)
import matplotlib.pyplot as plt
#plt.show()
"""
from pandas import DataFrame
fig, ax = utils.create_mpl_ax(ax)
response_name = ylabel or getattr(response, 'name', 'response')
ylabel = '%s of %s' % (func.func_name, response_name)
xlabel = xlabel or getattr(x, 'name', 'X')
legendtitle = legendtitle or getattr(trace, 'name', 'Trace')
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
x_values = x_levels = None
if isinstance(x[0], str):
x_levels = [l for l in np.unique(x)]
x_values = xrange(len(x_levels))
x = _recode(x, dict(zip(x_levels, x_values)))
data = DataFrame(dict(x=x, trace=trace, response=response))
plot_data = data.groupby(['trace', 'x']).aggregate(func).reset_index()
# return data
# check plot args
n_trace = len(plot_data['trace'].unique())
if linestyles:
try:
assert len(linestyles) == n_trace
except AssertionError, err:
raise ValueError("Must be a linestyle for each trace level")
def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
xlabel=None, ylabel=None, colors = [], markers = [],
linestyles = [], legendloc='best', legendtitle=None,
**kwargs):
"""
Parameters
----------
x : array-like
The `x` factor levels are the x-axis. If a `pandas.Series` is given
its name will be used in `xlabel` if `xlabel` is None.
trace : array-like
The `trace` factor levels will form the trace. If `trace` is a
`pandas.Series` its name will be used as the `legendtitle` if
`legendtitle` is None.
response : array-like
The reponse variable. If a `pandas.Series` is given
its name will be used in `ylabel` if `ylabel` is None.
func : function
Anything accepted by `pandas.DataFrame.aggregate`. This is applied to
the response variable grouped by the trace levels.
plottype : str {'line', 'scatter', 'both'}, optional
The type of plot to return. Can be 'l', 's', or 'b'
ax : axes, optional
Matplotlib axes instance
xlabel : str, optional
Label to use for `x`. Default is 'X'. If `x` is a `pandas.Series` it
will use the series names.
ylabel : str, optional
Label to use for `response`. Default is 'func of response'. If
`response` is a `pandas.Series` it will use the series names.
colors : list, optional
If given, must have length == number of levels in trace.
linestyles : list, optional
If given, must have length == number of levels in trace.
markers : list, optional
If given, must have length == number of lovels in trace
kwargs
These will be passed to the plot command used either plot or scatter.
If you want to control the overall plotting options, use kwargs.
Returns
-------
fig : Figure
The figure given by `ax.figure` or a new instance.
Examples
--------
>>> import numpy as np
>>> np.random.seed(12345)
>>> weight = np.random.randint(1,4,size=60)
>>> duration = np.random.randint(1,3,size=60)
>>> days = np.log(np.random.randint(1,30, size=60))
>>> fig = interaction_plot(weight, duration, days,
... colors=['red','blue'], markers=['D','^'], ms=10)
>>> import matplotlib.pyplot as plt
>>> plt.show()
"""
from pandas import DataFrame
fig, ax = utils.create_mpl_ax(ax)
if ylabel is None:
try: # did we get a pandas.Series
response_name = response.name
except:
response_name = 'response'
#NOTE: py3 compatible?
ylabel = '%s of %s' % (func.func_name, response_name)
if xlabel is None:
try:
x_name = x.name
except:
x_name = 'X'
if legendtitle is None:
try:
legendtitle = trace.name
except:
legentitle = 'Trace'
ax.set_ylabel(ylabel)
ax.set_xlabel(x_name)
data = DataFrame(dict(x=x, trace=trace, response=response))
plot_data = data.groupby(['trace', 'x']).aggregate(func).reset_index()
# check plot args
n_trace = len(plot_data['trace'].unique())
if linestyles:
try:
assert len(linestyles) == n_trace
except AssertionError, err:
raise ValueError("Must be a linestyle for each trace level")
