calculate_prec_from_cov#
- pharmpy.modeling.calculate_prec_from_cov(cov)[source]#
Calculate precision matrix from a covariance matrix
- Parameters:
cov (pd.DataFrame) – Covariance matrix
- Returns:
pd.DataFrame – Precision matrix
Examples
>>> from pharmpy.modeling import calculate_prec_from_cov >>> from pharmpy.tools import load_example_modelfit_results >>> results = load_example_modelfit_results("pheno") >>> cov = results.covariance_matrix >>> cov POP_CL POP_VC COVAPGR IIV_CL IIV_VC SIGMA POP_CL 4.408600e-08 4.761930e-08 -0.000002 -0.000001 1.552150e-07 8.042430e-08 POP_VC 4.761930e-08 7.233190e-04 -0.000804 0.000050 7.174490e-05 1.467290e-05 COVAPGR -1.679560e-06 -8.041750e-04 0.007015 -0.000108 -3.935790e-05 2.922260e-05 IIV_CL -1.090290e-06 4.989580e-05 -0.000108 0.000180 -1.863210e-05 5.049910e-06 IIV_VC 1.552150e-07 7.174490e-05 -0.000039 -0.000019 5.588920e-05 -4.497590e-07 SIGMA 8.042430e-08 1.467290e-05 0.000029 0.000005 -4.497590e-07 5.197970e-06 >>> calculate_prec_from_cov(cov) POP_CL POP_VC COVAPGR IIV_CL IIV_VC SIGMA POP_CL 2.993428e+07 22261.039122 16027.859538 203633.930854 -39113.269503 -817314.801755 POP_VC 2.226104e+04 2129.852212 260.115313 -373.896066 -2799.330946 -7697.921603 COVAPGR 1.602786e+04 260.115313 187.053488 177.987340 -205.261483 -2224.522815 IIV_CL 2.036339e+05 -373.896066 177.987340 7542.279597 2472.034556 -10209.416944 IIV_VC -3.911327e+04 -2799.330946 -205.261483 2472.034556 22348.216559 9193.203130 SIGMA -8.173148e+05 -7697.921603 -2224.522815 -10209.416944 9193.203130 249978.454601
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_cov_from_corrse
Covariance matrix from correlation matrix and standard errors
calculate_prec_from_corrse
Precision matrix from correlation matrix and standard errors
calculate_corr_from_prec
Correlation matrix from precision matrix