gL_test: Generalized Location (gL) Test
In dsoave/gJLS: Generalized Joint Location Scale (gJLS) Test
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
This function performs a generalized location (gL) test (Soave and Sun 2017) using the generalized least squares function, gls(), from the nlme package (Pinheiro and Bates 2000) to allow for correlated errors.
Usage
1
Arguments
model
a two-sided linear formula object describing the model, with the response (y) on the left and a ~ operator separating the covariates of interest on the right, separated by + operators.
data
a data frame containing the variables named in model and correlation arguments. This is required.
correlation
an optional corStruct object describing the within-group correlation structure. The correlation structure must be called directly from the nlme pacakge using "nlme::" (see examples below). See the documentation of corClasses for a description of the available corStruct classes. If a grouping variable is to be used, it must be specified in the form argument to the corStruct constructor. Defaults to NULL, corresponding to uncorrelated errors.
Details
No missing data are allowed - function will return an "error". Outcome (phenotype) must be quantitative and covariate (genotype) may be discrete (categorical) or continuous.
Value
gL_F the gL test statistic
numDF the gL test statistic numerator degrees of freedom
denDF the gL test statistic denominator degrees of freedom
gL_p the gL test p-value
Author(s)
David Soave
References
Soave, D., Corvol, H., Panjwani, N., Gong, J., Li, W., Boelle, P.Y., Durie, P.R., Paterson, A.D., Rommens, J.M., Strug, L.J., and Sun, L. (2015). A Joint Location-Scale Test Improves Power to Detect Associated SNPs, Gene Sets, and Pathways. American journal of human genetics 97, 125-138.
Soave, D. and Sun, L. (2017). A Generalized Levene's Scale Test for Variance Heterogeneity in the Presence of Sample Correlation and Group Uncertainty. Biometrics (Accepted).
See Also
Examples
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#################################################################################
## Example simulating data from Web Table 5 (Soave and Sun 2017 Biometrics)
#################################################################################
#### Simulation parameters
n=100 # number of sibling pairs
pA=0.2 # minor allele frequency
r1=0.5 # within-pair correlation
a=0.7 # group uncertainty
s0=1;s1=1.5;s2=2 # within-group variances
m0=0;m1=0.3;m2=0.6 # group means
#### identical by decent (IBD) sharing probabilities
GIBD1=c(pA^4,(1-pA)^4,4*pA^2*(1-pA)^2,2*pA^3*(1-pA),2*pA^3*(1-pA),2*pA*(1-pA)^3,2*pA*(1-pA)^3,
pA^2*(1-pA)^2,pA^2*(1-pA)^2)
GIBD2=c(pA^3,(1-pA)^3,pA*(1-pA),pA^2*(1-pA),pA^2*(1-pA),pA*(1-pA)^2,pA*(1-pA)^2,0,0)
GIBD3=c(pA^2,(1-pA)^2,2*pA*(1-pA),0,0,0,0,0,0)
### drawing the number of alleles shared IBD, D = 0, 1 or 2, from a multinomial distribution
### with parameters (0.25, 0.5, 0.25), independently for each sib-pair
IBD=rmultinom(1,size=n,prob=c(.25,.5,.25))
### Given the IBD status D, we then simulated paired genotypes (G1;G2), following
### the known conditional distribution of {(G1;G2)|D}
dfG=cbind(data.frame(rmultinom(1,size=IBD[1],prob=GIBD1)),data.frame(rmultinom(1,size=IBD[2],
prob=GIBD2)), data.frame(rmultinom(1,size=IBD[3],prob=GIBD3)))
G1G2=apply(dfG,1,sum)
XG=c(rep(c(2,0,1,1,0,2,1,2,0),c(G1G2)),rep(c(2,0,1,0,1,1,2,0,0),c(G1G2)))
dfr=data.frame(XG,cbind(rep(1:n,2)))
names(dfr)[1:2]=c("XG","FID")
dfr$sub=1:(2*n)
dfr=dfr[order(dfr$XG),]
dfr=dfr[order(dfr$FID),]
### Generate paired outcome data from a bivariate normal distribution, BVN(0,1,r1),
### where r1 is the the within sib-pair correlation.
blockC=matrix(c(1,r1,r1,1), 2)
yy=cbind(MASS::mvrnorm(n,rep(0,dim(blockC)[1]),blockC))
yy=data.frame(c(yy[,1],yy[,2]))
yy$FID=rep(1:n,2)
yy1=yy[order(yy$FID),]
dfr$y=c(yy1[,1])
#dfr$y=dfr$y*sqrt(s0*(dfr$XG==0)+s1*(dfr$XG==1)+s2*(dfr$XG==2))
### induced scale differences and mean differences bewteen the TRUE genotype groups
dfr$y=dfr$y*sqrt(s0*(dfr$XG==0)+s1*(dfr$XG==1)+s2*(dfr$XG==2))+(m0*(dfr$XG==0)+
m1*(dfr$XG==1)+m2*(dfr$XG==2))
### Convert the genotypes to pair of indicator variables for G=1 and G=2 minor alleles.
dfr$X1=with(dfr,(XG==1))+0
dfr$X2=with(dfr,(XG==2))+0
#################################################################################
## Analysis of true genotypes
#################################################################################
### Generalized scale, location and joint location-scale tests, using a compound
### symmetric correlation structure to account for within sib-pair correlation.
gS_test(model=y~X1+X2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gL_test(model=y~X1+X2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gJLS_test(model.loc=y~X1+X2,model.scale=y~X1+X2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
### Generalized scale, location and joint location-scale tests, ignoring correlation
### structure (incorrect analysis)
gS_test(model=y~X1+X2,data=dfr)
gL_test(model=y~X1+X2,data=dfr)
gJLS_test(model.loc=y~X1+X2,model.scale=y~X1+X2,data=dfr)
### Convert the simulated true genotypes (XG) to probabilistic data (p) using a Dirichlet distribution
### with scale parameters a for the correct genotype category and (1-a)/2 for the other two
#install and load MCMCpack packageto use the rdirichlet function
#install.packages('MCMCpack')
#library(MCMCpack)
dfr=dfr[order(dfr$XG),]
XG<-dfr$XG
p=rbind(rdirichlet(length(XG[XG==0]),c(a,(1-a)/2,(1-a)/2)), rdirichlet(length(XG[XG==1]),
c((1-a)/2,a,(1-a)/2)),rdirichlet(length(XG[XG==2]),c((1-a)/2,(1-a)/2,a)) )
dfr$p1=p[,2]
dfr$p2=p[,3]
dfr$dosage=dfr$p1+2*dfr$p2
### Best guess genotypes based on probabilistic data
B_G=function(x){return(which(x==max(x)))}
dfr$bgX=apply(p,1, B_G)-1
#################################################################################
## Analysis of genotype probabilities (reflecting group uncertainity)
#################################################################################
### Generalized scale, location and joint location-scale tests, using a compound
### symmetric correlation structure to account for within sib-pair correlation.
gS_test(model=y~p1+p2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gL_test(model=y~p1+p2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gJLS_test(model.loc=y~p1+p2,model.scale=y~p1+p2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
dsoave/gJLS documentation built on May 15, 2019, 4:22 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
This function performs a generalized location (gL) test (Soave and Sun 2017) using the generalized least squares function, gls(), from the nlme package (Pinheiro and Bates 2000) to allow for correlated errors.
Usage
1 |
Arguments
model |
a two-sided linear formula object describing the model, with the response (y) on the left and a ~ operator separating the covariates of interest on the right, separated by + operators. |
data |
a data frame containing the variables named in model and correlation arguments. This is required. |
correlation |
an optional corStruct object describing the within-group correlation structure. The correlation structure must be called directly from the nlme pacakge using "nlme::" (see examples below). See the documentation of corClasses for a description of the available corStruct classes. If a grouping variable is to be used, it must be specified in the form argument to the corStruct constructor. Defaults to NULL, corresponding to uncorrelated errors. |
Details
No missing data are allowed - function will return an "error". Outcome (phenotype) must be quantitative and covariate (genotype) may be discrete (categorical) or continuous.
Value
gL_F the gL test statistic
numDF the gL test statistic numerator degrees of freedom
denDF the gL test statistic denominator degrees of freedom
gL_p the gL test p-value
Author(s)
David Soave
References
Soave, D., Corvol, H., Panjwani, N., Gong, J., Li, W., Boelle, P.Y., Durie, P.R., Paterson, A.D., Rommens, J.M., Strug, L.J., and Sun, L. (2015). A Joint Location-Scale Test Improves Power to Detect Associated SNPs, Gene Sets, and Pathways. American journal of human genetics 97, 125-138.
Soave, D. and Sun, L. (2017). A Generalized Levene's Scale Test for Variance Heterogeneity in the Presence of Sample Correlation and Group Uncertainty. Biometrics (Accepted).
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | #################################################################################
## Example simulating data from Web Table 5 (Soave and Sun 2017 Biometrics)
#################################################################################
#### Simulation parameters
n=100 # number of sibling pairs
pA=0.2 # minor allele frequency
r1=0.5 # within-pair correlation
a=0.7 # group uncertainty
s0=1;s1=1.5;s2=2 # within-group variances
m0=0;m1=0.3;m2=0.6 # group means
#### identical by decent (IBD) sharing probabilities
GIBD1=c(pA^4,(1-pA)^4,4*pA^2*(1-pA)^2,2*pA^3*(1-pA),2*pA^3*(1-pA),2*pA*(1-pA)^3,2*pA*(1-pA)^3,
pA^2*(1-pA)^2,pA^2*(1-pA)^2)
GIBD2=c(pA^3,(1-pA)^3,pA*(1-pA),pA^2*(1-pA),pA^2*(1-pA),pA*(1-pA)^2,pA*(1-pA)^2,0,0)
GIBD3=c(pA^2,(1-pA)^2,2*pA*(1-pA),0,0,0,0,0,0)
### drawing the number of alleles shared IBD, D = 0, 1 or 2, from a multinomial distribution
### with parameters (0.25, 0.5, 0.25), independently for each sib-pair
IBD=rmultinom(1,size=n,prob=c(.25,.5,.25))
### Given the IBD status D, we then simulated paired genotypes (G1;G2), following
### the known conditional distribution of {(G1;G2)|D}
dfG=cbind(data.frame(rmultinom(1,size=IBD[1],prob=GIBD1)),data.frame(rmultinom(1,size=IBD[2],
prob=GIBD2)), data.frame(rmultinom(1,size=IBD[3],prob=GIBD3)))
G1G2=apply(dfG,1,sum)
XG=c(rep(c(2,0,1,1,0,2,1,2,0),c(G1G2)),rep(c(2,0,1,0,1,1,2,0,0),c(G1G2)))
dfr=data.frame(XG,cbind(rep(1:n,2)))
names(dfr)[1:2]=c("XG","FID")
dfr$sub=1:(2*n)
dfr=dfr[order(dfr$XG),]
dfr=dfr[order(dfr$FID),]
### Generate paired outcome data from a bivariate normal distribution, BVN(0,1,r1),
### where r1 is the the within sib-pair correlation.
blockC=matrix(c(1,r1,r1,1), 2)
yy=cbind(MASS::mvrnorm(n,rep(0,dim(blockC)[1]),blockC))
yy=data.frame(c(yy[,1],yy[,2]))
yy$FID=rep(1:n,2)
yy1=yy[order(yy$FID),]
dfr$y=c(yy1[,1])
#dfr$y=dfr$y*sqrt(s0*(dfr$XG==0)+s1*(dfr$XG==1)+s2*(dfr$XG==2))
### induced scale differences and mean differences bewteen the TRUE genotype groups
dfr$y=dfr$y*sqrt(s0*(dfr$XG==0)+s1*(dfr$XG==1)+s2*(dfr$XG==2))+(m0*(dfr$XG==0)+
m1*(dfr$XG==1)+m2*(dfr$XG==2))
### Convert the genotypes to pair of indicator variables for G=1 and G=2 minor alleles.
dfr$X1=with(dfr,(XG==1))+0
dfr$X2=with(dfr,(XG==2))+0
#################################################################################
## Analysis of true genotypes
#################################################################################
### Generalized scale, location and joint location-scale tests, using a compound
### symmetric correlation structure to account for within sib-pair correlation.
gS_test(model=y~X1+X2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gL_test(model=y~X1+X2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gJLS_test(model.loc=y~X1+X2,model.scale=y~X1+X2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
### Generalized scale, location and joint location-scale tests, ignoring correlation
### structure (incorrect analysis)
gS_test(model=y~X1+X2,data=dfr)
gL_test(model=y~X1+X2,data=dfr)
gJLS_test(model.loc=y~X1+X2,model.scale=y~X1+X2,data=dfr)
### Convert the simulated true genotypes (XG) to probabilistic data (p) using a Dirichlet distribution
### with scale parameters a for the correct genotype category and (1-a)/2 for the other two
#install and load MCMCpack packageto use the rdirichlet function
#install.packages('MCMCpack')
#library(MCMCpack)
dfr=dfr[order(dfr$XG),]
XG<-dfr$XG
p=rbind(rdirichlet(length(XG[XG==0]),c(a,(1-a)/2,(1-a)/2)), rdirichlet(length(XG[XG==1]),
c((1-a)/2,a,(1-a)/2)),rdirichlet(length(XG[XG==2]),c((1-a)/2,(1-a)/2,a)) )
dfr$p1=p[,2]
dfr$p2=p[,3]
dfr$dosage=dfr$p1+2*dfr$p2
### Best guess genotypes based on probabilistic data
B_G=function(x){return(which(x==max(x)))}
dfr$bgX=apply(p,1, B_G)-1
#################################################################################
## Analysis of genotype probabilities (reflecting group uncertainity)
#################################################################################
### Generalized scale, location and joint location-scale tests, using a compound
### symmetric correlation structure to account for within sib-pair correlation.
gS_test(model=y~p1+p2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gL_test(model=y~p1+p2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
gJLS_test(model.loc=y~p1+p2,model.scale=y~p1+p2,data=dfr,correlation=nlme::corCompSymm(form=~1|FID))
|
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