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
                  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_prec_from_cov(cov)
                  PTVCL          PTVV       THETA_3           IVCL           IVV      SIGMA_1_1
PTVCL      2.995567e+07  22660.028196  16057.855248  203511.614428 -39474.250514 -820118.299536
PTVV       2.266003e+04   2129.904642    260.176234    -375.266233  -2800.816246   -7718.769557
THETA_3    1.605786e+04    260.176234    187.038903     177.207683   -205.808480   -2225.150449
IVCL       2.035116e+05   -375.266233    177.207683    7527.530027   2462.974821   -9977.488860
IVV       -3.947425e+04  -2800.816246   -205.808480    2462.974821  22343.198618    9370.758371
SIGMA_1_1 -8.201183e+05  -7718.769557  -2225.150449   -9977.488860   9370.758371  249847.177845