tests/expanded/test.haplo.power.R

In haplo.stats: Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous

#$Author: sinnwell $
#$Date: 2011/12/05 20:53:27 $
#$Header: /projects/genetics/cvs/cvsroot/haplo.stats/test/test.haplo.power.R,v 1.1 2011/12/05 20:53:27 sinnwell Exp $
#$Locker:  $
#$Log: test.haplo.power.R,v $
#Revision 1.1  2011/12/05 20:53:27  sinnwell
#changed from .q to .R, to work with R check
#
## package: haplo.stats
## test script: haplo.power

## settings

verbose=TRUE
tmp <- Sys.setlocale("LC_COLLATE", "C")
tmp <- Sys.getlocale('LC_COLLATE')

require(haplo.stats)


set.seed(10)
  
if(verbose) cat("prepare a 5-marker set of haplotypes\n")

haplo <- rbind(
               c(     1,    2,    2,    1,    2),
               c(     1,    2,    2,    1,    1),
               c(     1,    1,    2,    1,    1),
               c(     1,    2,    1,    1,    2),
               c(     1,    2,    2,    2,    1),
               c(     1,    2,    1,    1,    1),
               c(     1,    1,    2,    2,    1),
               c(     1,    1,    1,    1,    2),
               c(     1,    2,    1,    2,    1),
               c(     1,    1,    1,    2,    1),
               c(     2,    2,    1,    1,    2),
               c(     1,    1,    2,    1,    2),
               c(     1,    1,    2,    2,    2),
               c(     1,    2,    2,    2,    2),
               c(     2,    2,    2,    1,    2),
               c(     1,    1,    1,    1,    1),
               c(     2,    1,    1,    1,    1),
               c(     2,    1,    2,    1,    1),
               c(     2,    2,    1,    1,    1),
               c(     2,    2,    1,    2,    1),
               c(     2,    2,    2,    1,    1))
dimnames(haplo)[[2]] <- paste("loc", 1:ncol(haplo), sep=".")
haplo <- data.frame(haplo)

haplo.freq <- c(0.170020121, 0.162977867, 0.123742455, 0.117706237, 0.097585513, 0.084507042,
                0.045271630, 0.039235412, 0.032193159, 0.019114688, 0.019114688, 0.013078471,
                0.013078471, 0.013078471, 0.013078471, 0.006036217, 0.006036217, 0.006036217,
                0.006036217, 0.006036217, 0.006036217)

## define index for risk haplotypes (having alleles 1-1 at loci 2 and 3)
haplo.risk <- (1:nrow(haplo))[haplo$loc.2==1 & haplo$loc.3==1]

## define index for baseline haplotype
base.index <-  1


############ Example for Case-control power/sample size

## specify OR for risk haplotypes
or <- 1.25

## determine beta regression coefficients for risk haplotypes

haplo.beta <- numeric(length(haplo.freq))
haplo.beta[haplo.risk] <-  log(or)

# Note that non-risk haplotypes have beta=0, as does the intercept
# (haplotype with base.index value). 

if(verbose) cat("Compute total sample size for given power\n")

ss.cc <- haplo.power.cc(haplo, haplo.freq, base.index, haplo.beta, case.frac=.5, prevalence=.1, alpha=.05, power=.8)

if(verbose) cat("Compute power for given sample size\n")

power.cc <- haplo.power.cc(haplo, haplo.freq, base.index, haplo.beta, case.frac=.5, prevalence=.1, alpha=.05, sample.size=11978)


############ Example for Quantitative trait power/sample size


# Because it can be easier to speficy genetic effect size in terms of
# a regression model R-squared value (r2), we use an
# auxiliary function to set up haplo.beta based on a specifed r2 value:


tmp <- find.haplo.beta.qt(haplo,haplo.freq,base.index,haplo.risk, r2=0.01, y.mu=0, y.var=1)
haplo.beta <- tmp$beta

# Compute sample size for given power

ss.qt <- haplo.power.qt(haplo, haplo.freq, base.index, haplo.beta, y.mu=0, y.var=1, alpha=.05, power=.80) 
  
# Compute power for given sample size

power.qt <- haplo.power.qt(haplo, haplo.freq, base.index, haplo.beta, y.mu=0, y.var=1, alpha=.05, sample.size = 2091)


print(ss.cc)
print(power.cc)
print(ss.qt)
print(power.qt)