calculate_prec_from_corrse#

pharmpy.modeling.calculate_prec_from_corrse(corr, se)[source]#

Calculate precision matrix from a correlation matrix and standard errors

Parameters:

corr (pd.DataFrame) – Correlation matrix
se (pd.Series) – Standard errors

Returns:

pd.DataFrame – Precision matrix

Examples

>>> from pharmpy.modeling import calculate_prec_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_prec_from_corrse(corr, se)
                  PTVCL          PTVV       THETA_3           IVCL           IVV      SIGMA_1_1
PTVCL      2.995565e+07  22660.041788  16057.848052  203511.410335 -39474.240358 -820116.179011
PTVV       2.266004e+04   2129.908225    260.176399    -375.266263  -2800.818557   -7718.757955
THETA_3    1.605785e+04    260.176399    187.038825     177.207512   -205.808434   -2225.144772
IVCL       2.035114e+05   -375.266263    177.207512    7527.518562   2462.972906   -9977.457873
IVV       -3.947424e+04  -2800.818557   -205.808434    2462.972906  22343.197906    9370.736254
SIGMA_1_1 -8.201162e+05  -7718.757955  -2225.144772   -9977.457873   9370.736254  249846.006431