function - Continuous PowerTransform/BoxCox Transformation in R -

i have dataset need transfer normal distribution.

first, generate reproducible dataset.

df <- runif(500, 0, 100) 

second, define function. function continue transforming d.f. until p > 0.05. transformed d.f. generated , named y.

boxcoxtrans <- function(y)     {     lambda <- 1     constant <- 0     while(shapiro.test(y)$p.value < 0.10)      {         constant <- abs(min(y, na.rm = true)) + 0.001         y <- y + constant         lambda <- powertransform(y)$lambda         y <- y ^ lambda     }     assign("y", y, envir = .globalenv)  } 

third, test df

shapiro.test(df)  shapiro-wilk normality test  data:  df w = 0.95997, p-value = 2.05e-10 

because p < 0.05, transform df

boxcoxtrans(df) 

then gives me following error messages,

error in qr.resid(xqr, w * fam(y, lambda, j = true)) :  na/nan/inf in foreign function call (arg 5) 

what did wrong?

you use box-muller transformation generate approximately normal distribution random uniform distribution. might more appropriate box-cox transformation, afaik typically applied convert skewed distribution 1 normal.

here's example of box-muller transformation applied set of uniformly distributed numbers:

set.seed(1234) size <- 5000 <- runif(size) b <- runif(size) y <- sqrt(-2 * log(a)) * cos(2 * pi * b) plot(density(y), main = "example of box-muller transformation", xlab="x", ylab="f(x)") library(nortest) #> lillie.test(y) # #   lilliefors (kolmogorov-smirnov) normality test # #data:  y #d = 0.009062, p-value = 0.4099 # #> shapiro.test(y) # #   shapiro-wilk normality test # #data:  y #w = 0.99943, p-value = 0.1301 # 

hope helps.