library.correg Submodule¶
Module Summary¶
Interfaces for the NAG Mark 30.3 correg Chapter.
correg - Correlation and Regression Analysis
This module is concerned with two techniques
correlation analysis and
regression modelling,
both of which are concerned with determining the inter-relationships among two or more variables.
Other modules of the NAG Library which cover similar problems are submodule fit and submodule opt. Submodule fit functions may be used to fit linear models by criteria other than least squares and also for polynomial regression; submodule opt functions may be used to fit nonlinear models and linearly constrained linear models.
See Also¶
naginterfaces.library.examples.correg:This subpackage contains examples for the
corregmodule. See also the Examples subsection.
Functionality Index¶
Correlation-like coefficients
all variables
casewise treatment of missing values:
coeffs_zero_miss_case()no missing values:
coeffs_zero()pairwise treatment of missing values:
coeffs_zero_miss_pair()subset of variables
casewise treatment of missing values:
coeffs_zero_subset_miss_case()no missing values:
coeffs_zero_subset()pairwise treatment of missing values:
coeffs_zero_subset_miss_pair()
Generalized linear models
binomial errors:
glm_binomial()computes estimable function:
glm_estfunc()gamma errors:
glm_gamma()Normal errors:
glm_normal()Poisson errors:
glm_poisson()prediction:
glm_predict()transform model parameters:
glm_constrain()
Hierarchical mixed effects regression
initiation:
mixeff_hier_init()using maximum likelihood:
mixeff_hier_ml()using restricted maximum likelihood:
mixeff_hier_reml()
Least angle regression (includes LASSO)
Additional parameter calculation:
lars_param()Model fitting
Cross-product matrix:
lars_xtx()Raw data:
lars()
Linear mixed effects regression
fitting (via REML or ML):
lmm_fit()initiation:
lmm_init()initiation, combine:
lmm_init_combine()via maximum likelihood (ML):
mixeff_ml()via restricted maximum likelihood (REML):
mixeff_reml()
Multiple linear regression
from correlation coefficients:
linregm_coeffs_const()from correlation-like coefficients:
linregm_coeffs_noconst()
Multiple linear regression/General linear model
add/delete observation from model:
linregm_obs_edit()add independent variable to model:
linregm_var_add()computes estimable function:
linregm_estfunc()delete independent variable from model:
linregm_var_del()general linear regression model:
linregm_fit()regression for new dependent variable:
linregm_fit_newvar()regression parameters from updated model:
linregm_update()transform model parameters:
linregm_constrain()
Nearest correlation matrix
fixed elements:
corrmat_fixed()fixed submatrix:
corrmat_shrinking()-factor structure:
corrmat_nearest_kfactor()method of Qi and Sun
element-wise weights:
corrmat_h_weight()unweighted, unbounded:
corrmat_nearest()weighted norm:
corrmat_nearest_bounded()rank-constrained:
corrmat_nearest_rank()shrinkage method:
corrmat_target()
Non-parametric rank correlation (Kendall and/or Spearman)
missing values
casewise treatment of missing values
overwriting input data:
coeffs_kspearman_miss_case_overwrite()preserving input data:
coeffs_kspearman_miss_case()pairwise treatment of missing values:
coeffs_kspearman_miss_pair()no missing values
overwriting input data:
coeffs_kspearman_overwrite()preserving input data:
coeffs_kspearman()
Partial least squares
calculates predictions given an estimated PLS model:
pls_pred()fits a PLS model for a given number of factors:
pls_fit()orthogonal scores using SVD:
pls_svd()orthogonal scores using Wold’s method:
pls_wold()
Product-moment correlation
correlation coefficients, all variables
casewise treatment of missing values:
coeffs_pearson_miss_case()no missing values:
coeffs_pearson()pairwise treatment of missing values:
coeffs_pearson_miss_pair()correlation coefficients, subset of variables
casewise treatment of missing values:
coeffs_pearson_subset_miss_case()no missing values:
coeffs_pearson_subset()pairwise treatment of missing values:
coeffs_pearson_subset_miss_pair()correlation matrix
compute correlation and covariance matrices:
corrmat()compute from sum of squares matrix:
ssqmat_to_corrmat()compute partial correlation and covariance matrices:
corrmat_partial()sum of squares matrix
Quantile regression
linear
comprehensive:
quantile_linreg()simple:
quantile_linreg_easy()
Residuals
Durbin–Watson test:
linregm_stat_durbwat()standardized residuals and influence statistics:
linregm_stat_resinf()
Ridge regression
ridge parameter(s) supplied:
ridge()ridge parameter optimized:
ridge_opt()
Robust correlation
Huber’s method:
robustm_corr_huber()user-supplied weight function only:
robustm_corr_user()user-supplied weight function plus derivatives:
robustm_corr_user_deriv()
Robust regression
compute weights for use with
robustm_user():robustm_wts()standard -estimates:
robustm()user-supplied weight functions:
robustm_user()variance-covariance matrix following
robustm_user():robustm_user_varmat()
Selecting regression model
all possible regressions:
linregm_rssq()forward selection:
linregm_fit_onestep()and statistics:
linregm_rssq_stat()
Service functions
for multiple linear regression
reorder elements from vectors and matrices:
linregm_service_reorder()select elements from vectors and matrices:
linregm_service_select()general option getting function:
optget()general option setting function:
optset()
Simple linear regression
no intercept:
linregs_noconst()no intercept with missing values:
linregs_noconst_miss()with intercept:
linregs_const()with intercept and with missing values:
linregs_const_miss()
Stepwise linear regression
Clarke’s sweep algorithm:
linregm_fit_stepwise()
For full information please refer to the NAG Library document
https://support.nag.com/numeric/nl/nagdoc_30.3/flhtml/g02/g02intro.html
Examples¶
- naginterfaces.library.examples.correg.coeffs_kspearman_miss_case_ex.main()[source]¶
Example for
naginterfaces.library.correg.coeffs_kspearman_miss_case().Kendall and Spearman rank correlation coefficients.
>>> main() naginterfaces.library.correg.coeffs_kspearman_miss_case Python Example Results. Kendall and Spearman rank correlation coefficients. Observations: [ 1.70, 1.00, 0.50 2.80, 4.00, 3.00 0.60, 6.00, 2.50 1.80, 9.00, 6.00 0.99, 4.00, 2.50 1.40, 2.00, 5.50 1.80, 9.00, 7.50 2.50, 7.00, 0.00 0.99, 5.00, 3.00 ] Correlation coefficients: [ 1.0000, 0.2941, 0.4058 0.1429, 1.0000, 0.7537 0.2760, 0.5521, 1.0000 ]
- naginterfaces.library.examples.correg.corrmat_nearest_ex.main()[source]¶
Example for
naginterfaces.library.correg.corrmat_nearest().Find a nearest correlation matrix.
>>> main() naginterfaces.library.correg.corrmat_nearest Python Example Results. The Frobenius-nearest correlation matrix to a given square matrix. Symmetric nearest correlation matrix X: [ 1.00e+00 -8.08e-01, 1.00e+00 1.92e-01, -6.56e-01, 1.00e+00 1.07e-01, 1.92e-01, -8.08e-01, 1.00e+00 ]
- naginterfaces.library.examples.correg.glm_binomial_ex.main()[source]¶
Example for
naginterfaces.library.correg.glm_binomial().Use k-fold cross validation to estimate the true positive and negative rates of a prediction from a logistic regression model.
The data used in this example was simulated.
>>> main() naginterfaces.library.correg.glm_binomial Python Example Results. Use k-fold cross validation to estimate the true positive and negative rates of a prediction from a logistic regression model. Observed -------------------------- Predicted | Negative Positive Total -------------------------------------- Negative | 19 6 25 Positive | 3 12 15 Total | 22 18 40 True Positive Rate (Sensitivity): 0.67 True Negative Rate (Specificity): 0.86
- naginterfaces.library.examples.correg.glm_normal_ex.main()[source]¶
Example for
naginterfaces.library.correg.glm_normal().Fits a generalized linear model with Normal errors.
>>> main() naginterfaces.library.correg.glm_normal Python Example Results. Fits a generalized linear model with Normal errors. Fitted model summary: RSS is 3.872e-01 Degrees of freedom 3 Term Estimate Standard Error Variable: 0 -2.387e-02 2.779e-03 Variable: 1 6.381e-02 2.638e-03
- naginterfaces.library.examples.correg.lars_ex.main()[source]¶
Example for
naginterfaces.library.correg.lars().Least angle regression.
>>> main() naginterfaces.library.correg.lars Python Example Results. Least angle regression. Step Parameter Estimate ----------------------------------------------------------------- 1 0.000 0.000 3.125 0.000 0.000 0.000 2 0.000 0.000 3.792 0.000 0.000 -0.713 3 -0.446 0.000 3.998 0.000 0.000 -1.151 4 -0.628 -0.295 4.098 0.000 0.000 -1.466 5 -1.060 -1.056 4.110 -0.864 0.000 -1.948 6 -1.073 -1.132 4.118 -0.935 -0.059 -1.981 ----------------------------------------------------------------- alpha: -50.037 ----------------------------------------------------------------- Step Sum RSS df Cp Ck Step Size ----------------------------------------------------------------- 1 72.446 8929.855 2 13.355 123.227 72.446 2 103.385 6404.701 3 7.054 50.781 24.841 3 126.243 5258.247 4 5.286 30.836 16.225 4 145.277 4657.051 5 5.309 19.319 11.587 5 198.223 3959.401 6 5.016 12.266 24.520 6 203.529 3954.571 7 7.000 0.910 2.198 ----------------------------------------------------------------- sigma^2: 304.198
- naginterfaces.library.examples.correg.lars_param_ex.main()[source]¶
Example for
naginterfaces.library.correg.lars_param().Least angle regression, additional parameter estimates.
>>> main() naginterfaces.library.correg.lars_param Python Example Results. Parameter Estimates from lars_xtx Step Parameter Estimate ----------------------------------------------------------------- 1 0.000 0.000 3.125 0.000 0.000 0.000 2 0.000 0.000 3.792 0.000 0.000 -0.713 3 -0.446 0.000 3.998 0.000 0.000 -1.151 4 -0.628 -0.295 4.098 0.000 0.000 -1.466 5 -1.060 -1.056 4.110 -0.864 0.000 -1.948 6 -1.073 -1.132 4.118 -0.935 -0.059 -1.981 Additional Parameter Estimates from lars_param nk Parameter Estimate ----------------------------------------------------------------- 0.2 0.000 0.000 0.625 0.000 0.000 0.000 1.2 0.000 0.000 3.258 0.000 0.000 -0.143 3.2 -0.483 -0.059 4.018 0.000 0.000 -1.214 4.5 -0.844 -0.676 4.104 -0.432 0.000 -1.707 5.2 -1.062 -1.071 4.112 -0.878 -0.012 -1.955
- naginterfaces.library.examples.correg.linregm_fit_ex.main()[source]¶
Example for
naginterfaces.library.correg.linregm_fit().Fit a general (multiple) linear regression model.
>>> main() naginterfaces.library.correg.linregm_fit Python Example Results. Fit a general (multiple) linear regression model. Fitted model summary: Model is not of full rank Rank: 4 RSS is 2.223e+01 Degrees of freedom 8 Term Estimate Standard Error Intercept: 3.056e+01 3.849e-01 Variable: 1 5.447e+00 8.390e-01 Variable: 2 6.743e+00 8.390e-01 Variable: 3 1.105e+01 8.390e-01 Variable: 4 7.320e+00 8.390e-01
- naginterfaces.library.examples.correg.linregm_fit_stepwise_ex.main()[source]¶
Example for
naginterfaces.library.correg.linregm_fit_stepwise().Stepwise linear regression.
>>> main() naginterfaces.library.correg.linregm_fit_stepwise Python Example Results. Stepwise linear regression. Starting Stepwise Selection Forward Selection Variable 1 Variance ratio = 1.260E+01 Variable 2 Variance ratio = 2.196E+01 Variable 3 Variance ratio = 4.403E+00 Variable 4 Variance ratio = 2.280E+01 Adding variable 4 to model Backward Selection Variable 4 Variance ratio = 2.280E+01 Keeping all current variables Forward Selection Variable 1 Variance ratio = 1.082E+02 Variable 2 Variance ratio = 1.725E-01 Variable 3 Variance ratio = 4.029E+01 Adding variable 1 to model Backward Selection Variable 1 Variance ratio = 1.082E+02 Variable 4 Variance ratio = 1.593E+02 Keeping all current variables Forward Selection Variable 2 Variance ratio = 5.026E+00 Variable 3 Variance ratio = 4.236E+00 Adding variable 2 to model Backward Selection Variable 1 Variance ratio = 1.540E+02 Variable 2 Variance ratio = 5.026E+00 Variable 4 Variance ratio = 1.863E+00 Dropping variable 4 from model Forward Selection Variable 3 Variance ratio = 1.832E+00 Variable 4 Variance ratio = 1.863E+00 Finished Stepwise Selection Fitted model summary: Term Estimate Standard Error Intercept: 5.258e+01 2.294e+00 Variable: 1 1.468e+00 1.213e-01 Variable: 2 6.623e-01 4.585e-02 RMS is 5.790e+00
- naginterfaces.library.examples.correg.lmm_init_combine_ex.main()[source]¶
Example for
naginterfaces.library.correg.lmm_init_combine()including calls tonaginterfaces.library.blgm.lm_submodel(),naginterfaces.library.blgm.lm_describe_data(),naginterfaces.library.correg.lmm_init(),naginterfaces.library.correg.lmm_fit()andnaginterfaces.library.blgm.handle_free()Multi-level linear mixed effects regression model using restricted maximum likelihood.
The data used in this example was simulated.
>>> main() naginterfaces.library.correg.lmm_init_combine Python Example Results. Linear mixed effects regression model using REML. Random Parameter Estimates ========================== Estimate Standard Label Error 0.683 0.506 V7 . V12 (Lvl 1) ... 0.504 2.693 V4 (Lvl 3) . V11 (Lvl 3) . V10 (Lvl 2) . V12 (Lvl 3) Fixed Parameter Estimates ========================= Estimate Standard Label Error 1.643 2.460 Intercept -1.622 0.855 V1 (Lvl 2) -2.482 1.142 V2 (Lvl 2) 0.462 1.214 V2 (Lvl 3) Variance Components =================== Estimate Label 0.563 V7 . V12 5.820 V8 . V12 10.860 V9 . V12 19.628 V5 . V11 . V12 40.534 V6 . V11 . V12 36.323 V3 . V11 . V10 . V12 12.451 V4 . V11 . V10 . V12 Sigma^2 = 0.003 -2 Log Likelihood = 608.195
- naginterfaces.library.examples.correg.pls_ex.main()[source]¶
Example for
naginterfaces.library.correg.pls().Partial least squares (PLS) regression using singular value decomposition, parameter estimates, predictions.
>>> main() naginterfaces.library.correg.pls Python Example Results. PLS regression using SVD; param. estimates; predictions. Begin regression. Begin estimation. Begin prediction. Predictions: [ 0.2133 0.5153 0.1438 0.4460 0.1716 2.4808 0.0963 1.4476 -0.1546 -0.5492 0.5393 0.2685 -1.1333 1.7974 0.4972 ]
- naginterfaces.library.examples.correg.quantile_linreg_ex.main()[source]¶
Example for
naginterfaces.library.correg.quantile_linreg().Multiple linear quantile regression (comprehensive interface.)
>>> main() naginterfaces.library.correg.quantile_linreg Python Example Results. Quantile regression model fitted to Engels' 1857 study of household expenditure on food. Quantile: 0.100 Lower Parameter Upper Limit Estimate Limit 0 74.946 110.142 145.337 1 0.370 0.402 0.433 Covariance matrix: [ 3.191e+02 -2.541e-01, 2.587e-04 ] Quantile: 0.250 Lower Parameter Upper Limit Estimate Limit 0 64.232 95.483 126.735 1 0.446 0.474 0.502 Covariance matrix: [ 2.516e+02 -2.004e-01, 2.039e-04 ] Quantile: 0.500 Lower Parameter Upper Limit Estimate Limit 0 55.399 81.482 107.566 1 0.537 0.560 0.584 Covariance matrix: [ 1.753e+02 -1.396e-01, 1.421e-04 ] Quantile: 0.750 Lower Parameter Upper Limit Estimate Limit 0 41.372 62.396 83.421 1 0.625 0.644 0.663 Covariance matrix: [ 1.139e+02 -9.068e-02, 9.230e-05 ] Quantile: 0.900 Lower Parameter Upper Limit Estimate Limit 0 26.829 67.351 107.873 1 0.650 0.686 0.723 Covariance matrix: [ 4.230e+02 -3.369e-01, 3.429e-04 ] First 10 residuals: Quantile Obs. 0.10000 0.25000 0.50000 0.75000 0.90000 1 -23.10718 -38.84219 -61.00711 -77.14462 -99.86551 2 -16.70358 -41.20981 -73.81193 -100.11463 -127.96277 3 13.48419 -37.04518 -100.61322 -157.07478 -200.13481 4 36.09526 4.52393 -36.48522 -70.97584 -102.95390 5 83.74310 44.08476 -6.54743 -50.41028 -87.11562 6 143.66660 89.90799 22.49734 -37.70668 -82.65437 7 187.39134 142.05288 84.66171 34.21603 -5.80963 8 196.90443 140.73220 70.44951 7.44831 -38.91027 9 194.55254 114.45726 15.70761 -75.01861 -135.36147 10 105.62394 12.32563 -102.13482 -208.16238 -276.22311
- naginterfaces.library.examples.correg.ridge_opt_ex.main()[source]¶
Example for
naginterfaces.library.correg.ridge_opt().Ridge regression, optimizing a ridge regression parameter.
>>> main() naginterfaces.library.correg.ridge_opt Python Example Results. Ridge regression optimizing GCV prediction error for a body fat model. Value of ridge parameter: 0.0712 Sum of squares of residuals: 1.0917e+02 Degrees of freedom: 16 Number of effective parameters: 2.9059 Parameter estimates 1 20.1950 2 9.7934 3 9.9576 4 -2.0125 Number of iterations: 6 Ridge parameter minimises GCV Estimated prediction errors: GCV = 7.4718 UEV = 6.3862 FPE = 7.3141 BIC = 8.2380 LOO CV = 7.5495 Residuals 1 -1.9894 2 3.5469 3 -3.0392 4 -3.0309 5 -0.1899 6 -0.3146 7 0.9775 8 4.0157 9 2.5332 10 -2.3560 11 0.5446 12 2.3989 13 -4.0876 14 3.2778 15 0.2894 16 0.7330 17 -0.7116 18 -0.6092 19 -2.9995 20 1.0110 Variance inflation factors 1 0.2928 2 0.4162 3 0.8089