Packages

p

scalation

analytics

package analytics

The analytics package contains classes, traits and objects for analytics including clustering and prediction.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. analytics
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Type Members

  1. class ANCOVA extends Predictor with Error

    The ANCOVA class supports ANalysis of COVAriance 'ANCOVA'.

    The ANCOVA class supports ANalysis of COVAriance 'ANCOVA'. It allows the addition of a categorical treatment variable 't' into a multiple linear regression. This is done by introducing dummy variables 'dj' to distinguish the treatment level. The problem is again to fit the parameter vector 'b' in the augmented regression equation

    y = b dot x + e = b0 + b_1 * x_1 + b_2 * x_2 + ... b_k * x_k + b_k+1 * d_1 + b_k+2 * d_2 + ... b_k+l * d_l + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y

    where 'x_pinv' is the pseudo-inverse.

    See also

    see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

  2. class ANOVA extends Predictor with Error

    The ANOVA class supports one-way ANalysis Of VAriance (ANOVA).

    The ANOVA class supports one-way ANalysis Of VAriance (ANOVA). It is framed using General Linear Model 'GLM' notation and supports the use of one binary/categorical treatment variable 't'. This is done by introducing dummy variables 'd_j' to distinguish the treatment level. The problem is again to fit the parameter vector 'b' in the following equation

    y = b dot x + e = b_0 + b_1 * d_1 + b_1 * d_2 ... b_k * d_k + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y

    where 'x_pinv' is the pseudo-inverse.

    See also

    psych.colorado.edu/~carey/Courses/PSYC5741/handouts/GLM%20Theory.pdf

  3. class ARMA extends Predictor with Error

    The ARMA class provide basic time series analysis capabilities for Auto- Regressive 'AR' and Moving Average 'MA' models.

    The ARMA class provide basic time series analysis capabilities for Auto- Regressive 'AR' and Moving Average 'MA' models. In an 'ARMA(p, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving Average components of the model. ARMA models are often used for forecasting.

  4. class CanCorrelation extends Reducer with Error

    The CanCorrelation class performs Canonical Correlation Analysis 'CCA' on two random vectors.

    The CanCorrelation class performs Canonical Correlation Analysis 'CCA' on two random vectors. Samples for the first one are stored in the 'x' data matrix and samples for the second are stored in the 'y' data matrix. Find vectors a and b that maximize the correlation between x * a and y * b.

    max {rho (x * a, y * b)}

    Additional vectors orthogonal to a and b can also be found.

  5. trait Clusterer extends AnyRef

    The Clusterer trait provides a common framework for several clustering algorithms.

  6. class ExpRegression extends Predictor with Error

    The ExpRegression class supports exponential regression.

    The ExpRegression class supports exponential regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the exponential regression equation

    log (mu (x)) = b dot x = b_0 + b_1 * x_1 + ... b_k * x_k

    See also

    www.stat.uni-muenchen.de/~leiten/Lehre/Material/GLM_0708/chapterGLM.pdf

  7. trait GLM extends AnyRef

    A General Linear Model 'GLM' can be developed using the GLM trait and object (see below).

    A General Linear Model 'GLM' can be developed using the GLM trait and object (see below). The implementation currently supports univariate models with multivariate models (where each response is a vector) planned for the future. It provides factory methods for the following special types of GLMs: SimpleRegression - simple linear regression, Regression - multiple linear regression using Ordinary Least Squares 'OLS' Regression_WLS - multiple linear regression using Weighted Least Squares 'WLS' RidgeRegression - robust multiple linear regression, TranRegression - transformed (e.g., log) multiple linear regression, PolyRegression - polynomial regression, TrigRegression - trigonometric regression ResponseSurface - response surface regression, ANOVA - GLM form of ANalysis Of VAriance, ANCOVA - GLM form of ANalysis of COVAriance.

  8. class HierClustering extends Clusterer with Error

    Cluster several vectors/points using hierarchical clustering.

    Cluster several vectors/points using hierarchical clustering. Start with each point forming its own cluster and merge clusters until there are only 'k'.

  9. class KMeansClustering extends Clusterer with Error

    The KMeansClustering class cluster several vectors/points using k-means clustering.

    The KMeansClustering class cluster several vectors/points using k-means clustering. Either (1) randomly assign points to 'k' clusters or (2) randomly pick 'k' points as initial centroids (technique (1) to work better and is the primary technique). Iteratively, reassign each point to the cluster containing the closest centroid. Stop when there are no changes to the clusters.

  10. class KalmanFilter extends AnyRef

    The KalmanFilter class is used to fit state-space models.

    The KalmanFilter class is used to fit state-space models.

    See also

    en.wikipedia.org/wiki/Kalman_filter FIX: needs more thorough testing

  11. class MarkovClustering extends Clusterer with Error

    The MarkovClustering class implements a Markov Clustering Algorithm 'MCL' and is used to cluster nodes in a graph.

    The MarkovClustering class implements a Markov Clustering Algorithm 'MCL' and is used to cluster nodes in a graph. The graph is represented as an edge-weighted adjacency matrix (a non-zero cell indicates nodes i and j are connected).

    The primary constructor takes either a graph (adjacency matrix) or a Markov transition matrix as input. If a graph is passed in, the normalize method must be called to convert it into a Markov transition matrix. Before normalizing, it may be helpful to add self loops to the graph. The matrix (graph or transition) may be either dense or sparse. See the MarkovClusteringTest object at the bottom of the file for examples.

  12. class NMFactorization extends AnyRef

    The NMFactorization class factors a matrix 'v' into two non negative matrices 'w' and 'h' such that 'v = wh' approximately.

    The NMFactorization class factors a matrix 'v' into two non negative matrices 'w' and 'h' such that 'v = wh' approximately.

    See also

    en.wikipedia.org/wiki/Non-negative_matrix_factorization

  13. class NeuralNet extends Predictor with Error

    The NeuralNet class supports basic 3-layer (input, hidden and output) Neural Networks.

    The NeuralNet class supports basic 3-layer (input, hidden and output) Neural Networks. Given several input and output vectors (training data), fit the weights connecting the layers, so that for a new input vector 'zi', the net can predict the output vector 'zo' ('zh' is the intermediate value at the hidden layer), i.e.,

    zi --> zh = f (w * zi) --> zo = g (v * zh)

    Note, w_0 and v_0 are treated as biases, so zi_0 and zh_0 must be 1.0.

  14. class NonLinRegression extends Predictor with Error

    The NonLinRegression class supports non-linear regression.

    The NonLinRegression class supports non-linear regression. In this case, 'x' can be multi-dimensional '[1, x1, ... xk]' and the function 'f' is non-linear in the parameters 'b'. Fit the parameter vector 'b' in the regression equation

    y = f(x, b) + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector 'b' by using Non-linear Programming to minimize Sum of Squares Error 'SSE'.

    See also

    www.bsos.umd.edu/socy/alan/stats/socy602_handouts/kut86916_ch13.pdf

  15. class Perceptron extends Predictor with Error

    The Perceptron class supports single-valued 2-layer (input and output) Neural-Networks.

    The Perceptron class supports single-valued 2-layer (input and output) Neural-Networks. Given several input vectors and output values (training data), fit the weights 'w' connecting the layers, so that for a new input vector 'zi', the net can predict the output value 'zo', i.e., 'zi --> zo = f (w dot zi)'. Note, w0 is treated as the bias, so x0 must be 1.0.

  16. class PoissonRegression extends Predictor with Error

    The PoissonRegression class supports Poisson regression.

    The PoissonRegression class supports Poisson regression. In this case, x' may be multi-dimensional '[1, x_1, ... x_k]'. Fit the parameter vector 'b' in the Poisson regression equation

    log (mu(x)) = b dot x = b_0 + b_1 * x_1 + ... b_k * x_k

    where 'e' represents the residuals (the part not explained by the model) and 'y' is now integer valued.

    See also

    see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

  17. class PolyRegression extends Predictor with Error

    The PolyRegression class supports polynomial regression.

    The PolyRegression class supports polynomial regression. In this case, 't' is expanded to [1, t, t2 ... tk]. Fit the parameter vector 'b' in the regression equation

    y = b dot x + e = b_0 + b_1 * t + b_2 * t2 ... b_k * tk + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y

    where 'x_pinv' is the pseudo-inverse.

    See also

    www.ams.sunysb.edu/~zhu/ams57213/Team3.pptx

  18. trait Predictor extends AnyRef

    The Predictor trait provides a common framework for several predictors.

    The Predictor trait provides a common framework for several predictors. A predictor is for unbounded responses (real or integer). When the number of distinct responses is bounded by some integer 'k', a classifier should be used.

  19. class PrincipalComponents extends Reducer with Error

    The PrincipalComponents class performs the Principal Component Analysis 'PCA' on data matrix 'x'.

    The PrincipalComponents class performs the Principal Component Analysis 'PCA' on data matrix 'x'. It can be used to reduce the dimensionality of the data. First find the Principal Components 'PC's by calling 'findPCs' and then call 'reduce' to reduce the data (i.e., reduce matrix 'x' to a lower dimensionality matrix).

  20. class QuadraticFit extends AnyRef

    The QuadraticFit class uses multiple regression to fit a quadratic surface to the function 'f'.

    The QuadraticFit class uses multiple regression to fit a quadratic surface to the function 'f'. This is useful when computing 'f' is costly, for example in simulation optimization. The fit is over a multi-dimensional grid and can be used for interpolation and limited extrapolation.

  21. class RandomGraph extends AnyRef

    The RandomGraph class generates random undirected graphs with clusters (as adjacency matrices).

  22. trait Reducer extends AnyRef

    The Reducer trait provides a common framework for several data reduction algorithms.

  23. class Regression [MatT <: MatriD, VecT <: VectoD] extends Predictor with Error

    The Regression class supports multiple linear regression.

    The Regression class supports multiple linear regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the regression equation

    y = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y [ alternative: b = solve (y) ]

    where 'x_pinv' is the pseudo-inverse. Three techniques are provided:

    'QR' // QR Factorization: slower, more stable (default) 'Cholesky' // Cholesky Factorization: faster, less stable (reasonable choice) 'Inverse' // Inverse/Gaussian Elimination, classical textbook technique (outdated)

    See also

    see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

  24. class Regression_WLS extends Predictor with Error

    The Regression_WLS class supports weighted multiple linear regression.

    The Regression_WLS class supports weighted multiple linear regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the regression equation

    y = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e

    where 'e' represents the residuals (the part not explained by the model). Use Weighted Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y [ alternative: b = solve (y) ]

    where 'x_pinv' is the pseudo-inverse. Three techniques are provided:

    'Fac_QR' // QR Factorization: slower, more stable (default) 'Fac_Cholesky' // Cholesky Factorization: faster, less stable (reasonable choice) 'Inverse' // Inverse/Gaussian Elimination, classical textbook technique (outdated)

    See also

    www.markirwin.net/stat149/Lecture/Lecture3.pdf

  25. class ResponseSurface extends Predictor with Error

    The ResponseSurface class uses multiple regression to fit a quadratic/cubic surface to the data.

    The ResponseSurface class uses multiple regression to fit a quadratic/cubic surface to the data. For example in 2D, the quadratic regression equation is

    y = b dot x + e = [b_0, ... b_k] dot [1, x_0, x_02, x_1, x_0*x_1, x_12] + e

    See also

    scalation.metamodel.QuadraticFit

  26. class RidgeRegression extends Predictor with Error

    The RidgeRegression class supports multiple linear regression.

    The RidgeRegression class supports multiple linear regression. In this case, 'x' is multi-dimensional [x_1, ... x_k]. Both the input matrix 'x' and the response vector 'y' are centered (zero mean). Fit the parameter vector 'b' in the regression equation

    y = b dot x + e = b_1 * x_1 + ... b_k * x_k + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y [ alternative: b = solve (y) ]

    where 'x_pinv' is the pseudo-inverse. Three techniques are provided:

    'QR' // QR Factorization: slower, more stable (default) 'Cholesky' // Cholesky Factorization: faster, less stable (reasonable choice) 'Inverse' // Inverse/Gaussian Elimination, classical textbook technique (outdated)

    See also

    statweb.stanford.edu/~tibs/ElemStatLearn/

  27. class SimpleRegression extends Predictor with Error

    The SimpleRegression class supports simple linear regression.

    The SimpleRegression class supports simple linear regression. In this case, the vector 'x' consists of the constant one and a single variable 'x_1', i.e., (1, x_1). Fit the parameter vector 'b' in the regression equation

    y = b dot x + e = (b_0, b_1) dot (1, x_1) + e = b_0 + b_1 * x_1 + e

    where 'e' represents the residuals (the part not explained by the model).

  28. class SimpleTest [VecT <: VectoD] extends AnyRef
  29. class TranRegression extends Predictor with Error

    The TranRegression class supports transformed multiple linear regression.

    The TranRegression class supports transformed multiple linear regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the transformed regression equation

    transform (y) = b dot x + e = b_0 + b_1 * x_1 + b_2 * x_2 ... b_k * x_k + e

    where 'e' represents the residuals (the part not explained by the model) and 'transform' is the function (defaults to log) used to transform the response vector 'y'. Common transforms: log (y), sqrt (y) when y > 0 More generally, a Box-Cox Transformation may be applied.

    See also

    www.ams.sunysb.edu/~zhu/ams57213/Team3.pptx

    citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.469.7176&rep=rep1&type=pdf Use Least-Squares (minimizing the residuals) to fit the parameter vector b = x_pinv * y where 'x_pinv' is the pseudo-inverse. Caveat: this class does not provide transformations on columns of matrix 'x'.

  30. class TrigRegression extends Predictor with Error

    The TrigRegression class supports trigonometric regression.

    The TrigRegression class supports trigonometric regression. In this case, 't' is expanded to '[1, sin (wt), cos (wt), sin (2wt), cos (2wt), ...]'. Fit the parameter vector 'b' in the regression equation

    y = b dot x + e = b_0 + b_1 sin (wt) + b_2 cos (wt) + b_3 sin (2wt) + b_4 cos (2wt) + ... + e

    where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector

    b = x_pinv * y

    where 'x_pinv' is the pseudo-inverse.

    See also

    link.springer.com/article/10.1023%2FA%3A1022436007242#page-1

Value Members

  1. val BASE_DIR: String

    The relative path for base directory

  2. object ANCOVATest extends App

    The ANCOVATest object tests the ANCOVA class using the following regression equation.

    The ANCOVATest object tests the ANCOVA class using the following regression equation.

    y = b dot x = b_0 + b_1*x_1 + b_2*x_2 + b_3*d_1 + b_4*d_2

  3. object ANOVATest extends App

    The ANOVATest object tests the ANOVA class using the following regression equation.

    The ANOVATest object tests the ANOVA class using the following regression equation.

    y = b dot x = b_0 + b_1*d_1 + b_2*d_2

  4. object ARMATest extends App

    The ARMATest object is used to test the ARMA class.

  5. object Centering

    The Centering object is used to center the input matrix 'x'.

    The Centering object is used to center the input matrix 'x'. This is done by subtracting the column means from each value.

  6. object ExpRegressionTest extends App

    The ExpRegressionTest object tests ExpRegression class using the following exponential regression problem.

  7. object ExpRegressionTest2 extends App

    The ExpRegressionTest2 object has a basic test for the ExpRegression class.

  8. object GLM extends GLM

    The GLM object makes the GLM trait's methods directly available.

    The GLM object makes the GLM trait's methods directly available. This approach (using traits and objects) allows the methods to also be inherited.

  9. object GLMTest extends App

    The GLMTest object tests the GLM object using the following regression equation.

    The GLMTest object tests the GLM object using the following regression equation.

    y = b dot x = b_0 + b_1*x_1 + b_2*x_2 + b_3*d_1 + b_4*d_2

  10. object GZLM extends GLM

    A Generalized Linear Model 'GZLM' can be developed using the GZLM object.

    A Generalized Linear Model 'GZLM' can be developed using the GZLM object. It provides factory methods for General Linear Models 'GLM' via inheritance and for proper Generalized Linear Models: LogisticRegression - logistic regression, PoissonRegression - Poisson regression, ExpRegression - Exponential regression,

  11. object GZLMTest extends App

    The GZLMTest object tests the GZLM object using the following regression equation.

    The GZLMTest object tests the GZLM object using the following regression equation.

    y = b dot x = b_0 + b_1*x_1 + b_2*x_2 + b_3*d_1 + b_4*d_2

  12. object HierClusteringTest extends App

    The HierClusteringTest object is used to test the HierClustering class.

  13. object KMeansClusteringTest extends App

    The KMeansClusteringTest object is used to test the KMeansClustering class.

    The KMeansClusteringTest object is used to test the KMeansClustering class. > run-main scalation.analytics.KMeansClusteringTest

  14. object KalmanFilterTest extends App

    The KalmanFilterTest object is used to test the KalmanFilter class.

    The KalmanFilterTest object is used to test the KalmanFilter class.

    See also

    en.wikipedia.org/wiki/Kalman_filter > run-main scalation.analytics.KalmanFilterTest

  15. object LogisticFunction

    The LogisticFunction object contains Activation functions.

  16. object MarkovClusteringTest extends App

    The MarkovClusteringTest object is used to test the MarkovClustering class.

    The MarkovClusteringTest object is used to test the MarkovClustering class.

    See also

    www.cs.ucsb.edu/~xyan/classes/CS595D-2009winter/MCL_Presentation2.pdf

  17. object MarkovClusteringTest2 extends App

    The MarkovClusteringTest2 object is used to test the MarkovClustering class.

  18. object NMFactorizationTest extends App

    The NMFactorizationTest object to test NMFactorizationTest class.

  19. object NeuralNetTest extends App

    The NeuralNetTest object is used to test the NeuralNet class.

    The NeuralNetTest object is used to test the NeuralNet class. For this test, the initial weights are used for used for prediction.

  20. object NeuralNetTest2 extends App

    The NeuralNetTest2 object is used to test the NeuralNet class.

    The NeuralNetTest2 object is used to test the NeuralNet class. For this test, training data is used to fit the weights before using them for prediction.

    See also

    http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf

  21. object NonLinRegressionTest extends App

    The NonLinRegressionTest object tests the NonLinRegression class: y = f(x; b) = b0 + exp (b1 * x0).

    The NonLinRegressionTest object tests the NonLinRegression class: y = f(x; b) = b0 + exp (b1 * x0).

    See also

    www.bsos.umd.edu/socy/alan/stats/socy602_handouts/kut86916_ch13.pdf Answers: sse = 49.45929986243339 fit = (VectorD (58.606566327280426, -0.03958645286504356), 0.9874574894685292) predict (VectorD (50.0)) = 8.09724678182599 FIX: check this example

  22. object PerceptronTest extends App

    The PerceptronTest object is used to test the Perceptron class.

    The PerceptronTest object is used to test the Perceptron class. For this test, the initial weights are used for used for prediction.

  23. object PerceptronTest2 extends App

    The PerceptronTest2 object is used to test the Perceptron class.

    The PerceptronTest2 object is used to test the Perceptron class. For this test, training data is used to fit the weights before using them for prediction.

    See also

    http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf

  24. object PoissonRegressionTest extends App

    The PoissonRegression object tests the PoissonRegression class.

    The PoissonRegression object tests the PoissonRegression class.

    See also

    http://www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aci = 29.533, pseudo_rSq = 0.4178

  25. object PoissonRegressionTest2 extends App

    The PoissonRegressionTest2 object tests the PoissonRegression class.

    The PoissonRegressionTest2 object tests the PoissonRegression class.

    See also

    www.stat.wisc.edu/~mchung/teaching/.../GLM.logistic.Rpackage.pdf

    statmaster.sdu.dk/courses/st111/module03/index.html

  26. object PolyRegressionTest extends App

    The PolyRegressionTest object tests PolyRegression class using the following regression equation.

    The PolyRegressionTest object tests PolyRegression class using the following regression equation.

    y = b dot x = b_0 + b_1*t + b_2*t^2.

  27. object PrincipalComponentsTest extends App

    The PrincipalComponentsTest object is used to test the PrincipalComponents class.

    The PrincipalComponentsTest object is used to test the PrincipalComponents class.

    See also

    www.ce.yildiz.edu.tr/personal/songul/file/1097/principal_components.pdf > run-main scalation.analytics.PrincipalComponentsTest

  28. object Probability extends Error

    The Probability object provides methods for operating on univariate and bivariate probability distributions of discrete random variables 'X' and 'Y'.

    The Probability object provides methods for operating on univariate and bivariate probability distributions of discrete random variables 'X' and 'Y'. A probability distribution is specified by its probability mass functions (pmf) stored either as a "probability vector" for a univariate distribution or a "probability matrix" for a bivariate distribution.

    joint probability matrix: pxy(i, j) = P(X = x_i, Y = y_j) marginal probability vector: px(i) = P(X = x_i) conditional probability matrix: px_y(i, j) = P(X = x_i|Y = y_j)

    In addition to computing joint, marginal and conditional probabilities, methods for computing entropy and mutual information are also provided. Entropy provides a measure of disorder or randomness. If there is little randomness, entropy will close to 0, while when randomness is high, entropy will be close to, e.g., 'log2 (px.dim)'. Mutual information provides a robust measure of dependency between random variables (contrast with correlation).

    See also

    scalation.stat.StatVector

  29. object ProbabilityTest extends App

    The ProbabilityTest object is used to test the Probability object.

  30. object ProbabilityTest2 extends App

    The ProbabilityTest2 provides upper bound for 'entropy' and 'entropy_k'.

  31. object QuadraticFitTest extends App

    The QuadraticFitTest object is used to test the QuadraticFit class for a two dimensional case.

  32. object QuadraticFitTest2 extends App

    The QuadraticFitTest2 object is used to test the QuadraticFit class for a three dimensional case.

  33. object QuadraticFitTest3 extends App

    The QuadraticFitTest3 object is used to test the QuadraticFit class for a three dimensional case with noise.

  34. object RandomGraphTest extends App

    The RandomGraphTest object is used to test the RandomGraph class.

  35. object RegTechnique extends Enumeration

    The RegTechnique object defines the implementation techniques available.

  36. object RegressionTest extends App

    The RegressionTest object tests Regression class using the following regression equation.

    The RegressionTest object tests Regression class using the following regression equation.

    y = b dot x = b_0 + b_1*x_1 + b_2*x_2.

    Test regression and backward elimination.

    See also

    http://statmaster.sdu.dk/courses/st111/module03/index.html > run-main scalation.analytics.RegressionTest

  37. object RegressionTest2 extends App

    The RegressionTest2 object tests Regression class using the following regression equation.

    The RegressionTest2 object tests Regression class using the following regression equation.

    y = b dot x = b_0 + b_1*x1 + b_2*x_2.

    Test regression using QR Decomposition and Gaussian Elimination for computing the pseudo-inverse. > run-main scalation.analytics.RegressionTest2

  38. object RegressionTest3 extends App

    The RegressionTest3 object tests the multi-collinearity method in the Regression class using the following regression equation.

    The RegressionTest3 object tests the multi-collinearity method in the Regression class using the following regression equation.

    y = b dot x = b_0 + b_1*x_1 + b_2*x_2 + b_3*x_3 + b_4 * x_4

    See also

    online.stat.psu.edu/online/development/stat501/data/bloodpress.txt > run-main scalation.analytics.RegressionTest3

    online.stat.psu.edu/online/development/stat501/12multicollinearity/05multico_vif.html

  39. object Regression_WLSTest extends App

    The Regression_WLSTest object tests Regression_WLS class using the following regression equation.

    The Regression_WLSTest object tests Regression_WLS class using the following regression equation.

    y = b dot x = b_0 + b_1*x_1 + b_2*x_2.

    Test regression and backward elimination.

    See also

    http://statmaster.sdu.dk/courses/st111/module03/index.html

  40. object ResponseSurfaceTest extends App

    The ResponseSurfaceTest object is used to test the ResponseSurface class.

  41. object RidgeRegressionTest extends App

    The RidgeRegressionTest object tests RidgeRegression class using the following regression equation.

    The RidgeRegressionTest object tests RidgeRegression class using the following regression equation.

    y = b dot x = b_1*x_1 + b_2*x_2.

    Test regression and backward elimination.

    See also

    http://statmaster.sdu.dk/courses/st111/module03/index.html

  42. object RidgeRegressionTest2 extends App

    The RidgeRegressionTest2 object tests RidgeRegression class using the following regression equation.

    The RidgeRegressionTest2 object tests RidgeRegression class using the following regression equation.

    y = b dot x = b_1*x1 + b_2*x_2.

    Test regression using QR Decomposition and Gaussian Elimination for computing the pseudo-inverse.

  43. object RidgeRegressionTest3 extends App

    The RidgeRegressionTest3 object tests the multi-collinearity method in the RidgeRegression class using the following regression equation.

    The RidgeRegressionTest3 object tests the multi-collinearity method in the RidgeRegression class using the following regression equation.

    y = b dot x = b_1*x_1 + b_2*x_2 + b_3*x_3 + b_4 * x_4

    See also

    online.stat.psu.edu/online/development/stat501/data/bloodpress.txt

    online.stat.psu.edu/online/development/stat501/12multicollinearity/05multico_vif.html

  44. object SimpleRegressionTest extends App

    The SimpleRegressionTest object to test SimpleRegression class: 'y = b dot x = (b_0, b_1) dot (1, x_1)'.

    The SimpleRegressionTest object to test SimpleRegression class: 'y = b dot x = (b_0, b_1) dot (1, x_1)'.

    See also

    http://www.analyzemath.com/statistics/linear_regression.html

  45. object SimpleRegressionTest2 extends App

    The SimpleRegressionTest2 object to test SimpleRegression class:

    The SimpleRegressionTest2 object to test SimpleRegression class:

    y = b dot x = b_0 + b_1*x_1.

    See also

    http://mathbits.com/mathbits/tisection/Statistics2/linear.htm

  46. object SimpleTest extends App
  47. object TranRegressionTest extends App

    The TranRegressionTest object tests TranRegression class using the following regression equation.

    The TranRegressionTest object tests TranRegression class using the following regression equation.

    log (y) = b dot x = b_0 + b_1*x_1 + b_2*x_2.

    > run-main scalation.analytics.TranRegressionTest

  48. object TrigRegressionTest extends App

    The TrigRegressionTest object tests TrigRegression class using the following regression equation.

    The TrigRegressionTest object tests TrigRegression class using the following regression equation.

    y = b dot x = b_0 + b_1*t + b_2*t^2.

Inherited from AnyRef

Inherited from Any

Ungrouped