calculate_se_from_cov

pharmpy.modeling.calculate_se_from_cov(cov)

Calculate standard errors from a covariance matrix

Parameters:

cov (pd.DataFrame) – Input covariance matrix

Returns:

pd.Series – Standard errors

Examples

>>> from pharmpy.modeling import calculate_se_from_cov
>>> from pharmpy.tools import load_example_modelfit_results
>>> results = load_example_modelfit_results("pheno")
>>> cov = results.covariance_matrix
>>> cov
                  PTVCL          PTVV   THETA_3      IVCL           IVV     SIGMA_1_1
PTVCL      4.411510e-08  4.010000e-08 -0.000002 -0.000001  1.538630e-07  8.178090e-08
PTVV       4.010000e-08  7.233530e-04 -0.000804  0.000050  7.171840e-05  1.461760e-05
THETA_3   -1.665010e-06 -8.040250e-04  0.007016 -0.000108 -3.944800e-05  2.932950e-05
IVCL      -1.093430e-06  4.981380e-05 -0.000108  0.000180 -1.856650e-05  4.867230e-06
IVV        1.538630e-07  7.171840e-05 -0.000039 -0.000019  5.589820e-05 -4.685650e-07
SIGMA_1_1  8.178090e-08  1.461760e-05  0.000029  0.000005 -4.685650e-07  5.195640e-06
>>> calculate_se_from_cov(cov)
PTVCL        0.000210
PTVV         0.026895
THETA_3      0.083762
IVCL         0.013415
IVV          0.007477
SIGMA_1_1    0.002279
dtype: float64

See also

calculate_se_from_prec

Standard errors from precision matrix

calculate_corr_from_cov

Correlation matrix from covariance matrix

calculate_cov_from_prec

Covariance matrix from precision matrix

calculate_cov_from_corrse

Covariance matrix from correlation matrix and standard errors

calculate_prec_from_cov

Precision matrix from covariance matrix

calculate_prec_from_corrse

Precision matrix from correlation matrix and standard errors

calculate_corr_from_prec

Correlation matrix from precision matrix