cshare {SHARE} | R Documentation |
The cshare function seeks the most informative set of SNPs for genetic association in a targeted region by growing/shrinking haplotypes with one more/less SNP in a stepwise fashion, and comparing prediction errors of different models via cross-validation or BIC.
cshare(haploObj, status, nfold = 10, maxsnps, tol = 1e-08, verbose = FALSE, ModSelMethod = c("Cross-Val", "BIC"), Minherit = c("additive", "dominant", "recessive"))
haploObj |
A object of calss "haplo". haploObj could be
generated by the function haplo . |
status |
A character string indicating the column name of the phenotype in
haploObj@pheno to be used as the clinical status in the analysis. |
nfold |
An integer that determines how many folds in the cross-validation, if ModSelMethod="Cross-Val" |
maxsnps |
An integer that determines the maximal number of SNPs to be chosen. The default is 6. |
tol |
The convergence parameters in haplotype logistic regression |
verbose |
TRUE/FALSE to decide whether to create log file for debug |
ModSelMethod |
Model selection method. Possible methods are "Cross-Val" for cross-validation, and "BIC" for Bayesian information criterion |
Minherit |
Mode of inheritance. Possible mode are "additive", "dominant", and "recessive" |
This function takes input from a phased genotype dataset and case-control status, performs stepwise search for the most informative haplotypes based on deviance criterion, either by cross-validation or by BIC. The function output the prediction deviances of a ladder of models up to the size of "maxsnps" values and best haplotype model selected.
cshare
returns a object of class "share"
James Y. Dai
Dai, J. Y., LeBlanc, M., Smith, N. L., Psaty, B. M. and Kooperberg, C. (2009). SHARE: an adaptive algorithm to select the most informative set of SNPs for genetic associat ion. Biostatistics, In Press.
## See vignette for more details ## Not run: unphasedKerem <- list() unphasedKerem[["Cross-Val"]] <- cshare(unphasedHaplo, status="CF", nfold=20, maxsnps=5, ModSelMethod="Cross-Val", Minherit="additive") unphasedKerem[["Cross-Val"]] unphasedKerem[["BIC"]] <- cshare(unphasedHaplo, , status="CF", maxsnps=5, ModSelMethod="BIC", Minherit="additive", verbose=1) unphasedKerem[["BIC"]] ## End(Not run)