statsmodels.emplike.descriptive.DescStatMV.mv_mean_contour¶
-
DescStatMV.mv_mean_contour(mu1_low, mu1_upp, mu2_low, mu2_upp, step1, step2, levs=
(0.001, 0.01, 0.05, 0.1, 0.2)
, var1_name=None
, var2_name=None
, plot_dta=False
)[source]¶ Creates a confidence region plot for the mean of bivariate data
- Parameters:¶
- m1_low
float
Minimum value of the mean for variable 1
- m1_upp
float
Maximum value of the mean for variable 1
- mu2_low
float
Minimum value of the mean for variable 2
- mu2_upp
float
Maximum value of the mean for variable 2
- step1
float
Increment of evaluations for variable 1
- step2
float
Increment of evaluations for variable 2
- levs
list
Levels to be drawn on the contour plot. Default = (.001, .01, .05, .1, .2)
- plot_dtabool
If True, makes a scatter plot of the data on top of the contour plot. Defaultis False.
- var1_name
str
Name of variable 1 to be plotted on the x-axis
- var2_name
str
Name of variable 2 to be plotted on the y-axis
- m1_low
Notes
The smaller the step size, the more accurate the intervals will be
If the function returns optimization failed, consider narrowing the boundaries of the plot
Examples
>>> import statsmodels.api as sm >>> two_rvs = np.random.standard_normal((20,2)) >>> el_analysis = sm.emplike.DescStat(two_rvs) >>> contourp = el_analysis.mv_mean_contour(-2, 2, -2, 2, .1, .1) >>> contourp.show()
Last update:
Dec 16, 2024