Login Register

static BinaryResults.resid_pearson()

statsmodels.discrete.discrete_model.BinaryResults.resid_pearson

static BinaryResults.resid_pearson() [source]

Pearson residuals

Notes

Pearson residuals are defined to be

$r_j = \frac{(y - M_jp_j)}{\sqrt{M_jp_j(1-p_j)}}$

where $p_j=cdf(X\beta)$ and $M_j$ is the total number of observations sharing the covariate pattern $j$ .

For now $M_j$ is always set to 1.

Links:

http://statsmodels.sourceforge.net/stable/generated/statsmodels.discrete.discrete_model.BinaryResults.resid_pearson.html

doc_statsmodels

2017-01-18 16:16:58

Comments

Leave a Comment

Please login to continue.

Popular Articles

static BinaryResults.resid_dev() statsmodels.discrete.discrete_model.BinaryResults.resid_dev static BinaryResults.resid_dev() [source] Deviance residuals Notes Deviance residuals

static BinaryResults.resid_response() statsmodels.discrete.discrete_model.BinaryResults.resid_response static BinaryResults.resid_response() [source] The response residuals Notes Respo

static RegressionResults.resid_pearson() statsmodels.regression.linear_model.RegressionResults.resid_pearson static RegressionResults.resid_pearson() [source] Residuals, normalized to hav

static QuantRegResults.resid_pearson() statsmodels.regression.quantile_regression.QuantRegResults.resid_pearson static QuantRegResults.resid_pearson() Residuals, normalized to have unit

static OLSResults.resid_pearson() statsmodels.regression.linear_model.OLSResults.resid_pearson static OLSResults.resid_pearson() Residuals, normalized to have unit variance. Return