A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, dichotomous, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and/or 'coxKM'. This function accepts as input kernel matrices.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, dichotomous, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' or 'coxKM'.
Creates an object of class nongeno containing a non-genotype design matrix, the type of kernel to generate, and the weights to be used to create the kernel matrix. This function is used to simplify the input structure of the call to KITdesign.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, dichotomous, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' or 'coxKM'. This function accepts as input objects of class geno and/or nongeno containing a design matrix, kernel type, and weights.
Creates an object of class geno containing a matrix of one-column-per-marker formated genotype data, the type of kernel, and the weights to be used to create the kernel matrix. This function is used to simplify the input structure of the call to KITdesign.
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