Provides a matrix of plots for objects created by compare.yai .
● Data Source:
CranContrib
● Keywords: hplot
● Alias: plot.compare.yai
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plot.notablyDifferent
(Package: yaImpute) :
Plots the scaled root mean square differences between observed and predicted
Provides a descriptive plot of the Imputation Error Profile for object(s) created by notablyDifferent .
● Data Source:
CranContrib
● Keywords: hplot
● Alias: plot.notablyDifferent
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0 images
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correctBias
(Package: yaImpute) :
Correct bias by selecting different near neighbors
Change the neighbor selections in a yai object such that bias (if any) in the average value of an expression of one or more variables is reduced to be within a defined confidence interval.
● Data Source:
CranContrib
● Keywords: misc, multivariate
● Alias: correctBias
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0 images
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cor.yai
(Package: yaImpute) :
Correlation between observed and imputed
Computes the correlation between observed and imputed values for each observation that has both.
● Data Source:
CranContrib
● Keywords: misc, multivariate
● Alias: cor.yai
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impute.yai
(Package: yaImpute) :
Impute variables from references to targets
Imputes the observation for variables from a reference observation to a target observation. Also, imputes a value for a reference from other references. This practice is useful for validation (see yai ). Variables not available in the original data may be imputed using argument ancillaryData .
● Data Source:
CranContrib
● Keywords: misc, multivariate
● Alias: impute, impute.yai
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0 images
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yai
(Package: yaImpute) :
Find K nearest neighbors
Given a set of observations, yai
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: yaImpute, yai
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errorStats
(Package: yaImpute) :
Compute error components of k-NN imputations
Error properties of estimates derived from imputation differ from those of regression-based estimates because the two methods include a different mix of error components. This function computes a partitioning of error statistics as proposed by Stage and Crookston (2007).
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: errorStats
●
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foruse
(Package: yaImpute) :
Report a complete imputation
Provides a matrix of all observations with the reference observation identification best used to represent it, followed by the distance.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: foruse
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rmsd.yai
(Package: yaImpute) :
Root Mean Square Difference between observed and imputed
Computes the root mean square difference (RMSD) between observed and imputed values for each observation that has both. RMSD is computationally like RMSE, but they differ in interpretation. The RMSD values can be scaled to afford comparisons among variables.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: rmsd, rmsd.yai
●
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unionDataJoin
(Package: yaImpute) :
Combines data from several sources
Takes any combination of several data frames or matrices and creates a new data frame. The rows are defined by a union of all row names in the arguments, and the columns are defined by a union of all column names in the arguments. The data are loaded into this new frame where column and row names match the individual inputs. Duplicates are tolerated with the last one specified being the one kept. NAs are returned for combinations of rows and columns where no data exist. Factors are processed as necessary.
● Data Source:
CranContrib
● Keywords: misc
● Alias: unionDataJoin
●
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