Size of the confidence interval for the regression estimate. If True, estimate and plot a regression model relating the xĪnd y variables. If True, draw a scatterplot with the underlying observations (or Standard deviation of the observations in each bin. If 'ci', defer to the value of theĬi parameter. Size of the confidence interval used when plotting a central tendencyįor discrete values of x. x_ci “ci”, “sd”, int in or None, optional When this parameter is used, it implies that the default of This parameter is interpreted either as the number ofĮvenly-sized (not necessary spaced) bins or the positions of the binĬenters. The scatterplot is drawn the regression is still fit to the originalĭata. x_bins int or vector, optionalīin the x variable into discrete bins and then estimate the central If x_ci is given, this estimate will be bootstrapped and aĬonfidence interval will be drawn. This is useful when x is a discrete variable. x_estimator callable that maps vector -> scalar, optionalĪpply this function to each unique value of x and plot the Tidy (“long-form”) dataframe where each column is a variable and each When pandas objects are used, axes will be labeled with If strings, these should correspond with column names Parameters x, y: string, series, or vector array

There are a number of mutually exclusive options for estimating the Plot data and a linear regression model fit. regplot ( *, x = None, y = None, data = None, x_estimator = None, x_bins = None, x_ci = 'ci', scatter = True, fit_reg = True, ci = 95, n_boot = 1000, units = None, seed = None, order = 1, logistic = False, lowess = False, robust = False, logx = False, x_partial = None, y_partial = None, truncate = True, dropna = True, x_jitter = None, y_jitter = None, label = None, color = None, marker = 'o', scatter_kws = None, line_kws = None, ax = None ) ¶

Cplot for logistic