This will be taken into account whenĬomputing the confidence intervals by performing a multilevel bootstrap If the x and y observations are nested within sampling units, This value for “final” versions of plots. Value attempts to balance time and stability you may want to increase Number of bootstrap resamples used to estimate the ci. TheĬonfidence interval is estimated using a bootstrap for largeĭatasets, it may be advisable to avoid that computation by setting This willīe drawn using translucent bands around the regression line. 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 ( data = None, *, x = None, y = 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 ) #
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