Love-boost.R

## Updated 2020-09-20 simplifying to four functions

`bootdif` <-
  function(y, g, conf.level=0.95, B.reps = 2000) {
    lowq = (1 - conf.level)/2
    g <- as.factor(g)
    a <- attr(Hmisc::smean.cl.boot(y[g==levels(g)[1]], B=B.reps, reps=TRUE),'reps')
    b <- attr(Hmisc::smean.cl.boot(y[g==levels(g)[2]], B=B.reps, reps=TRUE),'reps')
    meandif <- diff(tapply(y, g, mean, na.rm=TRUE))
    a.b <- quantile(b-a, c(lowq,1-lowq))
    res <- c(meandif, a.b)
    names(res) <- c('Mean Difference',lowq, 1-lowq)
    res
  }



`saifs.ci` <- 
  function(x, n, conf.level=0.95, dig=3)
  {
    p.sample <- round(x/n, digits=dig)
    
    p1 <- x / (n+1)
    p2 <- (x+1) / (n+1)
    
    var1 <- (p1*(1-p1))/n
    se1 <- sqrt(var1)
    var2 <- (p2*(1-p2))/n
    se2 <- sqrt(var2)
    
    lowq = (1 - conf.level)/2
    tcut <- qt(lowq, df=n-1, lower.tail=FALSE)
    
    lower.bound <- round(p1 - tcut*se1, digits=dig)
    upper.bound <- round(p2 + tcut*se2, digits=dig)
    res <- c(p.sample, lower.bound, upper.bound)
    names(res) <- c('Sample Proportion',lowq, 1-lowq)
    res
  }


`twobytwo` <-
  function(a,b,c,d, namer1 = "Row1", namer2 = "Row2", namec1 = "Col1", namec2 = "Col2", 
           conf.level = 0.95)
    # build 2 by 2 table and run Epi library's twoby2 command to summarize
    # from the row-by-row counts in a cross-tab
    # upper left cell is a, upper right is b, lower left is c, lower right is d
    # names are then given in order down the rows then across the columns
    # use standard epidemiological format - outcomes in columns, treatments in rows
  {
    .Table <- matrix(c(a, b, c, d), 2, 2, byrow=T, dimnames=list(c(namer1, namer2), c(namec1, namec2)))
    Epi::twoby2(.Table, alpha = 1 - conf.level)
  }

# Code from Gelman and Carlin

`retrodesign` <- function(A, s, alpha=.05, df=Inf, 
                        n.sims=10000){
    z <- qt(1-alpha/2, df)
    p.hi <- 1 - pt(z-A/s, df)
    p.lo <- pt(-z-A/s, df)
    power <- p.hi + p.lo
    typeS <- p.lo/power
    estimate <- A + s*rt(n.sims,df)
    significant <- abs(estimate) > s*z
    exaggeration <- mean(abs(estimate)[significant])/A
    return(list(power=power, typeS=typeS, 
                exaggeration=exaggeration))
}