| Type: | Package |
| Title: | Composite Kernel Machine Regression Based on Likelihood Ratio Test |
| Version: | 0.2.3 |
| Author: | Ni Zhao [aut], Haoyu Zhang [aut, cre] |
| Maintainer: | Haoyu Zhang <andrew.haoyu@gmail.com> |
| Description: | Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).) |
| License: | GPL-3 |
| Depends: | R (≥ 3.1.0), |
| Imports: | Rcpp (≥ 0.12.17), mgcv, MASS, nlme, compiler, stats |
| LinkingTo: | RcppEigen, Rcpp |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | yes |
| Packaged: | 2018-09-13 21:00:37 UTC; haoyuzhang |
| Repository: | CRAN |
| Date/Publication: | 2018-09-13 21:40:02 UTC |
ColSum_C
Description
ColSum_C
Usage
ColSum_C(AA)
Arguments
AA |
Matrix |
ColSumtwomatrix_C
Description
ColSumtwomatrix_C
Usage
ColSumtwomatrix_C(AA, BB)
Arguments
AA |
Matrix |
BB |
Matrix |
Eigen_C
Description
Eigen_C
Usage
Eigen_C(As)
Arguments
As |
A sysmetric matrix |
Eigen_C_value
Description
Eigen_C_value
Usage
Eigen_C_value(As)
Arguments
As |
A sysmetric matrix |
Elementwisesquare_C
Description
Elementwisesquare_C
Usage
Elementwisesquare_C(AA)
Arguments
AA |
Matrix |
LR0_fixRho_C
Description
LR0_fixRho_C
Usage
LR0_fixRho_C(LamdasR, muR, w1R, w2R, nminuspx)
Arguments
LamdasR |
Lamda Number |
muR |
mu vector |
w1R |
w1 vector |
w2R |
w2 vector |
nminuspx |
n-px |
LR0_fixRho_LRT_C
Description
LR0_fixRho_LRT_C
Usage
LR0_fixRho_LRT_C(LamdasR, muR, w1R, w2R, nminuspx, xiR)
Arguments
LamdasR |
Lamda Number |
muR |
mu vector |
w1R |
w1 vector |
w2R |
w2 vector |
nminuspx |
n-px |
xiR |
Vector |
MatMult_C
Description
MatMult_C
Usage
MatMult_C(A, B)
Arguments
A |
first matrix |
B |
second matrix |
MatrixPlus_C
Description
MatrixPlus_C
Usage
MatrixPlus_C(A, B)
Arguments
A |
First Matrix |
B |
Second Matrix |
MatrixRowMax_C
Description
MatrixRowMax_C
Usage
MatrixRowMax_C(AA)
Arguments
AA |
Matrix |
NumxMatrix_C
Description
NumxMatrix_C
Usage
NumxMatrix_C(A, B)
Arguments
A |
Number |
B |
Matrix |
Sum_C
Description
Sum_C
Usage
Sum_C(AA)
Arguments
AA |
Vector |
VecMultMat_C
Description
VecMultMat_C
Usage
VecMultMat_C(A, B)
Arguments
A |
Vector |
B |
Matrix |
Vecplus_C
Description
Vecplus_C
Usage
Vecplus_C(A, B)
Arguments
A |
Vector |
B |
Vector |
doubleloop
Description
doubleloop
Usage
doubleloop(K1R, K2R, P0R, AR, U1R, wR, LamdasR, nminuspx, all_rho, LR0_allRhoR)
Arguments
K1R |
K1 matrix |
K2R |
K2 matrix |
P0R |
P0 matrix |
AR |
A matrix |
U1R |
U1 vector |
wR |
w matrix |
LamdasR |
Lamdas vector |
nminuspx |
n-px |
all_rho |
the rho vector |
LR0_allRhoR |
the matrix of likelihood ratio |
doubleloop_LRT
Description
doubleloop_LRT
Usage
doubleloop_LRT(K1R, K2R, P0R, AR, U1R, wR, LamdasR, nminuspx, all_rho,
LR0_allRhoR)
Arguments
K1R |
K1 matrix |
K2R |
K2 matrix |
P0R |
P0 matrix |
AR |
A matrix |
U1R |
U1 vector |
wR |
w matrix |
LamdasR |
Lamdas vector |
nminuspx |
n-px |
all_rho |
rho vector |
LR0_allRhoR |
LR0_allRhomatrix |
Title
Description
Title
Usage
getp_Qreg_P(null, LR, qmax = 1)
Arguments
null |
a vector of likelihood ratio under the null hypothesis. Generated from simulation |
LR |
the likelihood of the data |
qmax |
quantile of likelihood ratio vector. Most time put as 1, all the likelihood ratio vector under the null hypothesis will be used |
Title
Description
Title
Usage
getp_Qreg_dir(null, LR, qmax = 1)
Arguments
null |
a vector of likelihood ratio under the null hypothesis. Generated from simulation |
LR |
the likelihood of the data |
qmax |
quantile of likelihood ratio vector. Most time put as 1, all the likelihood ratio vector under the null hypothesis will be used |
Title
Description
Title
Usage
getp_Qreg_logP(null, LR, qmax = 1)
Arguments
null |
a vector of likelihood ratio under the null hypothesis. Generated from simulation |
LR |
the likelihood of the data |
qmax |
quantile of likelihood ratio vector. Most time put as 1, all the likelihood ratio vector under the null hypothesis will be used |
Title
Description
Title
Usage
getp_au1(null, LR)
Arguments
null |
a vector of likelihood ratio under the null hypothesis. Generated from simulation |
LR |
the likelihood of the data |
Title
Description
Title
Usage
getp_aud_estimate_pi_first(null, LR)
Arguments
null |
a vector of likelihood ratio under the null hypothesis. Generated from simulation |
LR |
the likelihood of the data |
ifelsetest_C
Description
ifelsetest_C
Usage
ifelsetest_C(x)
Arguments
x |
Vector |
Composite kernel machine regression based likelihood ratio test.
Description
Composite kernel machine regression based likelihood ratio test. The approximate method for likelihood ratio test tend to be too conservative for small alpha values. We recommend not using it in GWAS
Usage
omniLRT_fast(y, X, K1, K2, N = 10000, length.lambda = 200,
length.rho = 21)
Arguments
y |
: y is the vecgtor of the continous outcomes. |
X |
: X denotes the additional covariates. |
K1 |
: K1 is the first kernel corresponding to the genetic main effect. |
K2 |
: K2 is the second kernel corresponding to the genetic and environment interaction effect. |
N |
: N is th total number of randomly generated normal variables used to generate the emprical null distribution of LRT. Default value is 10,000. |
length.lambda |
: the length of lambda. Dafult value is 200. The values of lambda are all more than 0. |
length.rho |
: the length of rho. Default value is 21. The values of rho are between 0 and 1. |
Value
the result is a list containing three elements. 1. p.dir is the p-value of likelihood ratio test based on emprical distrition. 2. p.aud is the p-value by approximating the null distribution as a mixture of a point mass at zero with probability b and weighted chi square distribution with d degrees of freedom with probality of 1-b. 3. LR is the likelihood ratio test statistics.
Examples
set.seed(6)
n = 50 # the number of observations
X = rnorm(n) # the other covariates
p = 2 # two snp in a gene will be simulated
G = runif(n*p)< 0.5
G = G + runif(n*p) < 0.5
G = matrix(G, n,p) #genetic matrix
E = (runif(n) < 0.5)^2 #enviroment effect
y = rnorm(n) + G[,1] * 0.3 #observations
omniLRT_fast(y, X = cbind(X, E),K1 = G %*% t(G),K2 = (G*E) %*% t(G * E))
Composite kernel machine regression based restricted likelihood ratio test
Description
Composite kernel machine regression based restricted likelihood ratio test
Usage
omniRLRT_fast(y, X, K1, K2, N = 10000, length.rho = 200,
length.lambda = 21)
Arguments
y |
vector of the continous outcomes. |
X |
the additional covariates. |
K1 |
the first kernel corresponding to the genetic main effect. |
K2 |
the second kernel corresponding to the genetic and environment interaction effect. |
N |
total number of randomly generated normal variables used to generate the emprical null distribution of LRT. Default value is 10,000. |
length.rho |
the length of rho. Default value is 21. The values of rho are between 0 and 1. |
length.lambda |
the length of lambda. Dafult value is 200. The values of lambda are all more than 0. |
Value
the result is a list containing three elements. 1. p.dir is the p-value of restricted likelihood ratio test based on emprical distrition. 2. p.aud is the p-value by approximating the null distribution as a mixture of a point mass at zero with probability b and weighted chi square distribution with d degrees of freedom with probality of 1-b. 3. LR is the likelihood ratio test statistics.
Examples
set.seed(6)
n = 50 # the number of observations
X = rnorm(n) # the other covariates
p = 2 # two snp in a gene will be simulated
G = runif(n*p)< 0.5
G = G + runif(n*p) < 0.5
G = matrix(G, n,p) #genetic matrix
E = (runif(n) < 0.5)^2 #enviroment effect
y = rnorm(n) + G[,1] * 0.3 #observations
omniRLRT_fast(y, X = cbind(X, E),K1 = G %*% t(G),K2 = (G*E) %*% t(G * E))