calculate_corr_from_inf#
- pharmpy.modeling.calculate_corr_from_inf(information_matrix)[source]#
Calculate correlation matrix from an information matrix
- Parameters:
information_matrix (pd.DataFrame) – Information matrix
- Returns:
pd.DataFrame – Correlation matrix
Examples
>>> from pharmpy.modeling import load_example_model, calculate_corr_from_inf >>> model = load_example_model("pheno") >>> inf = model.modelfit_results.information_matrix >>> inf 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 >>> calculate_corr_from_inf(inf) 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
See also
calculate_se_from_cov
Standard errors from covariance matrix
calculate_se_from_inf
Standard errors from information matrix
calculate_corr_from_cov
Correlation matrix from covariance matrix
calculate_cov_from_inf
Covariance matrix from information matrix
calculate_cov_from_corrse
Covariance matrix from correlation matrix and standard errors
calculate_inf_from_cov
Information matrix from covariance matrix
calculate_inf_from_corrse
Information matrix from correlation matrix and standard errors