corImputation

corImputation imputes missing data based on the correlation that exists between row levels.

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the mixed-model-equations-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available in sommer.

Giovanny Covarrubias-Pazaran

sommer

Solving Mixed Model Equations in R

corImputation function

<dl> <dt>wide</dt>
<dd>numeric matrix with individuals in rows and time variable in columns (e.g., environments, genetic markers, etc.).</dd> <dt>Gu</dt>
<dd>optional correlation matrix between the individuals or row levels. If NULL it will be computed as the correlation of t(wide).</dd> <dt>nearest</dt>
<dd>integer value describing how many nearest neighbours (the ones showing the highest correlation) should be used to average and return the imputed value.</dd> <dt>roundR</dt>
<dd>a TRUE/FALSE statement describing if the average result should be rounded or not. This may be specifically useful for categorical data in the form of numbers (e.g., -1,0,1).</dd></dl>

Arguments

Author

Imputing a matrix using correlations — corImputation

<dl>

 <dt>wide</dt>
<dd>numeric matrix with individuals in rows and time variable in columns (e.g., environments, genetic markers, etc.).</dd>

 <dt>Gu</dt>
<dd>optional correlation matrix between the individuals or row levels. If NULL it will be computed as the correlation of t(wide).</dd>

 <dt>nearest</dt>
<dd>integer value describing how many nearest neighbours (the ones showing the highest correlation) should be used to average and return the imputed value.</dd>

 <dt>roundR</dt>
<dd>a TRUE/FALSE statement describing if the average result should be rounded or not. This may be specifically useful for categorical data in the form of numbers (e.g., -1,0,1).</dd>

</dl>

corImputation: Imputing a matrix using correlations

Description

Usage

Value

Arguments

Author

References

See Also

Examples