Last data update: 2014.03.03

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Results 1 - 10 of 20 found.
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regr (Package: yhat) : Regression effect reporting for lm class objects

The regr reports beta weights, standardized beta weights, structure coefficients, adjusted effect sizes, and commonality coefficients for lm class objects.
● Data Source: CranContrib
● Keywords:
● Alias: regr
● 0 images

ci.yhat (Package: yhat) : Compute CI

This function retrieves the proper elements from boot.ci.
● Data Source: CranContrib
● Keywords:
● Alias: ci.yhat
● 0 images

commonalityCoefficients (Package: yhat) : Commonality Coefficents

Commonality Coefficients returns a list of two tables. The first table CC contains the list of commonality coefficients and the percent variance for each effect. The second CCTotByVar totals the unique and common effects for each independent variable.
● Data Source: CranContrib
● Keywords: models, regression
● Alias: commonalityCoefficients
● 0 images

genList (Package: yhat) : Generate List R^2 Values

Use the bitmap matrix to generate the list of R^2 values needed.
● Data Source: CranContrib
● Keywords:
● Alias: genList
● 0 images

plotCI.yhat (Package: yhat) : Plot CIs from yhat

This function plots CIs that have been produced from /codebooteval.yhat.
● Data Source: CranContrib
● Keywords: models, regression
● Alias: plotCI.yhat
● 0 images

dombin (Package: yhat) : Dominance Analysis

For each level of dominance and pairs of predictors in the full model, this function indicates whether a predictor "x1" dominates "x2", predictor "x2" dominates "x1", or that dominance cannot be established between predictors.
● Data Source: CranContrib
● Keywords: models, regression
● Alias: dombin
● 0 images

calc.yhat (Package: yhat) : More regression indices for lm class objects

Reports beta weights, validity coefficients, structure coefficients, product measures, commonality analysis coefficients, and dominance analysis weights for lm class objects.
● Data Source: CranContrib
● Keywords:
● Alias: calc.yhat
● 0 images

yhat-package (Package: yhat) : Interpreting Regression Effects

The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights, structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.
● Data Source: CranContrib
● Keywords: package
● Alias: yhat, yhat-package
● 0 images

canonVariate (Package: yhat) : Canonical Commonality Analysis

The canonCommonality function produces commonality data for a given canonical variable set. Using the variables in a given canonical set to partition the variance of the canonical variates produced from the other canonical set, commonality data is supplied for the number of canonical functions requested.
● Data Source: CranContrib
● Keywords: multivariate
● Alias: canonVariate
● 0 images

effect.size (Package: yhat) : Effect Size Computation for lm

Creates adjusted effect sizes for linear regression.
● Data Source: CranContrib
● Keywords:
● Alias: effect.size
● 0 images