calculate_cov_from_corrse#
- pharmpy.modeling.calculate_cov_from_corrse(corr, se)[source]#
Calculate covariance matrix from a correlation matrix and standard errors
- Parameters:
corr (pd.DataFrame) – Correlation matrix
se (pd.Series) – Standard errors
- Returns:
pd.DataFrame – Covariance matrix
Examples
>>> from pharmpy.modeling import calculate_cov_from_corrse >>> from pharmpy.tools import load_example_modelfit_results >>> results = load_example_modelfit_results("pheno") >>> corr = results.correlation_matrix >>> se = results.standard_errors >>> corr PTVCL PTVV THETA_3 IVCL IVV SIGMA_1_1 PTVCL 1.000000 0.007099 -0.094640 -0.388059 0.097981 0.170820 PTVV 0.007099 1.000000 -0.356899 0.138062 0.356662 0.238441 THETA_3 -0.094640 -0.356899 1.000000 -0.096515 -0.062991 0.153616 IVCL -0.388059 0.138062 -0.096515 1.000000 -0.185111 0.159170 IVV 0.097981 0.356662 -0.062991 -0.185111 1.000000 -0.027495 SIGMA_1_1 0.170820 0.238441 0.153616 0.159170 -0.027495 1.000000 >>> calculate_cov_from_corrse(corr, se) PTVCL PTVV THETA_3 IVCL IVV SIGMA_1_1 PTVCL 4.411512e-08 4.009998e-08 -0.000002 -0.000001 1.538630e-07 8.178111e-08 PTVV 4.009998e-08 7.233518e-04 -0.000804 0.000050 7.171834e-05 1.461762e-05 THETA_3 -1.665011e-06 -8.040245e-04 0.007016 -0.000108 -3.944801e-05 2.932957e-05 IVCL -1.093431e-06 4.981380e-05 -0.000108 0.000180 -1.856651e-05 4.867245e-06 IVV 1.538630e-07 7.171834e-05 -0.000039 -0.000019 5.589820e-05 -4.685661e-07 SIGMA_1_1 8.178111e-08 1.461762e-05 0.000029 0.000005 -4.685661e-07 5.195664e-06
See also
calculate_se_from_cov
Standard errors from covariance matrix
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_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