gamLoessScan | R Documentation |
Fitting a Generalized Additive Mixed Models (GAMM) with Local Polynomial Regression in association testing.
gamLoessScan(genotype, traits, U, cv_method = "adaptive_cv", model_metric = "RMSE", n_hyperparameter_search = 10,verbose=TRUE, ...)
genotype |
Varants/genotypes matrix coding in reference allele (0,1,2) or variant count |
traits |
Traits |
U |
Covariates or confounding factors |
cv_method |
Cross-validation |
model_metric |
Model performance metrics, based on which the optimal model is determined. |
n_hyperparameter_search |
Number of hyperparameters for tuning |
verbose |
whether print training messages. |
... |
other arguments passing to generalized additive mixed models (gam) |
Fits the specified generalized additive mixed model (GAMM) with LOESS smoothness.
The weights of variants as well as their p-values
Wood S.N. (2006b) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
Wang, Y. (1998) Mixed effects smoothing spline analysis of variance. J.R. Statist. Soc. B 60, 159-174.
Lin, X and Zhang, D. (1999) Inference in generalized additive mixed models by using smoothing splines. JRSSB. 55(2):381-400.
# not run f <- system.file('extdata',package='VariantScan') infile <- file.path(f, "sim1.csv") geno=read.csv(infile) traitq=geno[,14] genotype=geno[,-c(1:14)] PCs=prcomp(genotype) test=gamLoessScan(genotype =genotype,traits =(traitq),U=PCs$x[,1:2],n_hyperparameter_search=5)
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