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")