ibmpredictiveanalytics Goto Github PK
Name: IBM SPSS Predictive Analytics
Type: Organization
Bio: Extensions, Samples and much more
Location: Armonk - New York
IBM SPSS Predictive Analytics's Projects
stats_find_file
Create file handle in location of a searched for file
stats_firthlog
Firth logistic regression
stats_fleiss_kappa
Compute Fleiss Multi-Rater Kappa Statistics
stats_gam
This procedure estimates a generalied additive model. GAM s are linear in predictor terms that can be simple variables or vario us kinds of splines or polynomials. You can specify the error distri bution and link function for the model.
stats_garch
GARCH models
stats_gbm
Estimate generalized boosted regression models
stats_gbmpred
Calculate predictions from generalized boosted regression mod
stats_get_r
Get information about R workspace contents and create SPSS da
stats_get_triples
Read a file in Triple-S format
stats_getset_dataset
Open an SPSS file and apply a permanent dataset name to the r esulting dataset.
stats_grm
Fit graded response models to ordinal data
stats_heckman_regr
Estimate censored/truncated regression or switching regression
stats_if
Execute blocks of code conditionally
stats_inequality
Various inequality measures for a variable distribution
stats_irm
Fit three parameter item response models
stats_kernel_density
Fit kernel-smoothed distributions
stats_latent_class
Latent Class Analysis
stats_linear_elastic_net_regression
Fits linear elastic net regression models using Python sklearn classes.
stats_linear_lasso_regression
Fits linear lasso regression models using Python sklearn classes.
stats_linear_ridge_regression
Fits linear ridge regression models using Python sklearn classes.
stats_loess
Fit a loess curve to a model
stats_lp
Solve linear and integer programming problems
stats_make_catalog
Build a dataset of variable information from multiple datasets.
stats_mcset_convert
Convert a multiple category set to a multiple dichotomy set.
stats_nonpar_regr
Nonparametric regression.
stats_ntile_analysis
This procedure, also known in the literature as decile analysis, produces a table and charts that group the predicted probabilities from a classification procedure such as logistic regression, trees, and SVM into ntiles in order to better understand their distribution and to assist in using these for formulating decision rules.
stats_open_project
Open a project
stats_optbinex
Construct optimal bins for categorical or continuous target
stats_optimal_designmc
Generate an optimal design
stats_output_attrs
Set Viewer page attributes and setup via syntax
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