[R] Need help with vector designations in a Function

Thu May 12 19:14:23 CEST 2005

My function works fine if the X and Y exist in the T314 data.  How can I
code the macro to allow vector designations in the function different than
X and Y?  Maybe something to do with environment?

rm(list=ls())

# Data from NCHRP Appendix A -
http://trb.org/publications/nchrp/nchrp_w71.pdf
T314 <- structure(list(Lab = as.integer(c(1:60)), X = c(4.89, 3.82, 2.57,
2.3,2.034, 2, 1.97, 1.85,1.85, 1.85, 1.84, 1.82, 1.82, 1.77, 1.76, 1.67,
1.66,
1.63, 1.62,1.62, 1.55, 1.54, 1.54, 1.53, 1.53, 1.44, 1.428, 1.42, 1.39,
1.36,
1.35, 1.31, 1.28, 1.24, 1.24, 1.23, 1.22, 1.21, 1.19, 1.18, 1.18, 1.18,
1.17,
1.16, 1.13, 1.13, 1.099, 1.09, 1.09, 1.08, 1.07, 1.05, 0.98, 0.97, 0.84,
0.808,
0.69, 0.63, 0.6, 0.5), Y = c(5.28, 3.82, 2.41, 2.32, 2.211, 1.46, 2.24,
1.91,
1.78, 1.63, 1.81, 1.92, 1.2, 1.67, 1.28, 1.59, 1.45, 2.06, 1.91, 1.19,
1.26,
1.79, 1.39, 1.48, 0.72, 1.29, 1.517, 1.71, 1.12, 1.38, 0.93, 1.36, 1.2,
1.23,
0.71, 1.29, 1.26, 1.48, 1.26, 1.33, 1.21, 1.04, 1.57, 1.42, 1.08, 1.04,
1.33,
1.33, 1.2, 1.05, 1.24, 0.91, 0.99, 1.06, 1.27, 0.702, 0.77, 0.58, 1,
0.38)),
.Names = c("Lab", "X", "Y" ), class = "data.frame",
row.names = as.character(c(1:60)))

### Be sure to remove NA data prior to oOut()
oOut <- function(dsin, dsout, X, Y)
  {
  oOutsub <- function(olimit){
    # Get Medians for Invalid Data Determination
    Xmed <- median(dsin$X); Ymed <- median(dsin$Y)
    # Make new dataset with (Y-X)-(Ymedian-Xmedian) column
    dsout <- cbind(dsin, XY=(dsin$Y-dsin$X)-(Ymed-Xmed))
    # Get median for new column
    XYmed <- median(dsout$XY)
    iqx <- diff(quantile(dsin$X, c(0.125, .875)))
    iqy <- diff(quantile(dsin$Y, c(0.125, .875)))
    iqxy <- diff(quantile(dsout$XY, c(0.125, .875)))
    # Invalid Upper Limits
    iulX <- quantile(dsin$X, 0.875)+olimit*iqx

    iulY <- quantile(dsin$Y, 0.875)+olimit*iqy
    iulXY <- quantile(dsout$XY, 0.875)+olimit*iqxy
    # Invalid Lower Limits
    illX <- quantile(dsin$X, 0.125)-olimit*iqx
    illY <- quantile(dsin$Y, 0.125)-olimit*iqy
    illXY <- quantile(dsout$XY, 0.125)-olimit*iqxy

    dsout <- subset(dsout, with(dsout, X <= iulX & X >= illX))
    dsout <- subset(dsout, with(dsout, Y <= iulY & Y >= illY))
    dsout <- subset(dsout, with(dsout, XY <= iulXY & XY >= illXY))
    dsout
    }
  dsout <- oOutsub(1.555)  #Eliminates Invalid Data
  dsin <- dsout
  dsout <- oOutsub(0.674)  #Eliminates Outlier Data
  dsout <- dsout[1:(ncol(dsout)-2)]  #Trim outer 2 XY columns
  dsout
  }

T314.o <- oOut(T314, T314.o, X, Y)
T314.o # showing resutls.  Notice 2nd XY name

cv <- function(x)
{
sd(x)/(mean(x))*100
}

T314 <- cbind(T314, X.mean=mean(T314$X))
T314 <- cbind(T314, X.sd=sd(T314$X))
T314 <- cbind(T314, X.cv=cv(T314$X))
T314 <- cbind(T314, Y.count=NROW(T314$Y))
T314 <- cbind(T314, Y.mean=mean(T314$Y))
T314 <- cbind(T314, Y.sd=sd(T314$Y))
T314 <- cbind(T314, Y.cv=cv(T314$Y))

T314.o <- cbind(T314.o, X.mean=mean(T314.o$X))
T314.o <- cbind(T314.o, X.sd=sd(T314.o$X))
T314.o <- cbind(T314.o, X.cv=cv(T314.o$X))
T314.o <- cbind(T314.o, Y.count=NROW(T314.o$Y))
T314.o <- cbind(T314.o, Y.mean=mean(T314.o$Y))
T314.o <- cbind(T314.o, Y.sd=sd(T314.o$Y))
T314.o <- cbind(T314.o, Y.cv=cv(T314.o$Y))

T314.o <- cbind(T314.o, ElimLabs=paste(setdiff(T314$Lab, T314.o$Lab),
collapse=", "))
# Number of standard deviations
T314.o <- cbind(T314.o, X.nsd=(T314.o$X-T314.o$X.mean)/T314.o$X.sd)
T314.o <- cbind(T314.o, Y.nsd=(T314.o$Y-T314.o$Y.mean)/T314.o$Y.sd)
# X Ratings next
T314.o<-cbind(T314.o, X.rate=NA)
for (i in 1:nrow(T314.o))
{s<-ifelse(T314.o$X.nsd[i]<0,-1,1)
if(abs(T314.o$X.nsd[i])<1) T314.o$X.rate[i]=s*5
if(abs(T314.o$X.nsd[i])>=1 & T314.o$X.nsd[i]<1.5) T314.o$X.rate[i]=s*4
if(abs(T314.o$X.nsd[i])>=1.5 & T314.o$X.nsd[i]<2) T314.o$X.rate[i]=s*3
if(abs(T314.o$X.nsd[i])>=2 & T314.o$X.nsd[i]<2.5) T314.o$X.rate[i]=s*2
if(abs(T314.o$X.nsd[i])>=2.5 & T314.o$X.nsd[i]<3) T314.o$X.rate[i]=s*1
if(abs(T314.o$X.nsd[i])>=3) T314.o$X.rate[i]=0
}
# Y Ratings next
T314.o<-cbind(T314.o, Y.rate=NA)
for (i in 1:nrow(T314.o))
{s<-ifelse(T314.o$Y.nsd[i]<0,-1,1)
if(abs(T314.o$Y.nsd[i])<1) T314.o$Y.rate[i]=s*5
if(abs(T314.o$Y.nsd[i])>=1 & T314.o$Y.nsd[i]<1.5) T314.o$Y.rate[i]=s*4
if(abs(T314.o$Y.nsd[i])>=1.5 & T314.o$Y.nsd[i]<2) T314.o$Y.rate[i]=s*3
if(abs(T314.o$Y.nsd[i])>=2 & T314.o$Y.nsd[i]<2.5) T314.o$Y.rate[i]=s*2
if(abs(T314.o$Y.nsd[i])>=2.5 & T314.o$Y.nsd[i]<3) T314.o$Y.rate[i]=s*1
if(abs(T314.o$Y.nsd[i])>=3) T314.o$Y.rate[i]=0
}

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