help on smoothing volatility surface..

This script below pulls yahoo data via a function in quantmod, then
massages the data around to forumalate a 3D graph with RGL library,
attached is a ggplot to show the data i'm trying to create a surface with
in separate line geoms . the issue is that the 3D graph looks very ugly and
cut up because of the limited quantities of points on the front month
expirations.. can anyone tell me whats going on here , what i can do to fix
this.. do i need to smooth each expiration's line then interpolate.... ??




library(RQuantLib)
library(quantmod)
library(rgl)
library(akima)
library(ggplot2)
library(plyr)

GetIV <- function(type, value,
                 underlying, strike,dividendYield, riskFreeRate, maturity,
volatility,
                 timeSteps=150, gridPoints=151) {

   AmericanOptionImpliedVolatility(type, value,
                                   underlying, strike,dividendYield,
riskFreeRate, maturity, volatility,
                                   timeSteps=150,
gridPoints=151)$impliedVol
}


GetDelta <- function(type, underlying, strike,
                    dividendYield, riskFreeRate, maturity, volatility,
                    timeSteps=150, gridPoints=149, engine="CrankNicolson")
{

   AmericanOption(type,underlying, strike, dividendYield, riskFreeRate,
maturity, volatility,
                  timeSteps=150, gridPoints=149,
engine="CrankNicolson")$delta
}
# set what symbol you want vol surface for
underlying <- 'GOOG'
# set what your volatility forcast or assumption is
volforcast <- .25
# Get symbols current price
underlying.price <- getQuote(underlying,what=yahooQF("Last Trade (Price
Only)"))$Last

OC <- getOptionChain(underlying, NULL)
#check data
head(OC)
lputs <- lapply(OC, FUN = function(x) x$puts[grep("[A-Z]\\d{6}[CP]\\d{8}$",
rownames(x$puts)), ])
head(lputs) #check for NA values, yahoo returns all NA values sometimes
puts <- do.call('rbind', lputs )
#check data
head(puts,5)

symbols <- as.vector(unlist(lapply(lputs, rownames)))
expiries <- unlist(lapply(symbols, FUN = function(x) regmatches(x=x,
regexpr('[0-9]{6}', x) )))
puts$maturity <- as.numeric((as.Date(expiries, "%y%m%d") - Sys.Date())/365)

puts$IV <- mapply(GetIV, value = puts$Ask, strike = puts$Strike, maturity =
puts$maturity,
                 MoreArgs= list(type='put', underlying= underlying.price,
                                dividendYield=0, riskFreeRate = 0.01,
                                volatility = volforcast), SIMPLIFY=TRUE)

puts$delta <- mapply(GetDelta, strike =  puts$Strike, volatility = puts$IV,
                    maturity = puts$maturity, MoreArgs= list(type='put',

underlying=underlying.price, dividendYield=0,
                                                             riskFreeRate
= 0.01 ), SIMPLIFY=TRUE)

# subset out itm puts
puts <- subset(puts, delta < -.09 & delta > -.5 )

expiries.formated <- format(as.Date(levels(factor(expiries)), format =
'%y%m%d'), "%B %d, %Y")

fractionofyear.levels <- levels(factor(puts$maturity))

xyz <- with(puts, interp(x=maturity, y=delta*100, z=IV*100,
                        xo=sort(unique(maturity)), extrap=FALSE ))

with(xyz, persp3d(x,y,z, col=heat.colors(length(z))[rank(z)],
xlab='maturity',
                 ylab='delta', zlab='IV', main='IV Surface'))

putsplot <- ggplot(puts, aes(delta, IV, group = factor(maturity), color =
factor(maturity))) +
   labs(x = "Delta", y = "Implied Volatilty", title="Volatility Smile",
color = "GooG \nExpiration") +
   scale_colour_discrete( breaks=c(fractionofyear.levels),
                          labels=c(expiries.formated)) +
   geom_line() +
   geom_point()

putsplot

   [[alternative HTML version deleted]]