movavgf: Return a self-defined-functionalized (not just a table...

In Chitran1987/StatsChitran: StatsChitran

Return a self-defined-functionalized (not just a table function ) moving average of the 2D dataset in question

Description

The function returns a 2 column data frame with the first column named X. The second column named Y contains the smoothed dataset

Usage

movavgf(X, Y, bn, fn, f, val)

Arguments

X	The X vector of the 2D dataset
Y	The Y vector of the 2D dataset
bn	The no of elements before the current element that is needed to be included in the averaging window
fn	The no of elements after the current element that is needed to be included in the averaging window
f	This argument is a function using which the moving window used for averaging of the X, Y dataset is created. The window is centered at wc = "val", (see argument "val"). If "s" is the sampling interval of variable X, then the window abscissa vector "w"" is calculated as.. w = seq(val - bn*s, val + fn*s) The window ordinate is calculated as f_seq = f(w).
val	The value around which the averaging window should be centered. For example, for a standard normal distribution val = 0, but for a gaussian with a mean of 4, val = 4.

Details

Ensure before using that...
1) f_seq does not contain Inf elements.
2) f_seq does not contain NAs.
3) f_seq does not contain NANs.
All of the above will result in errors being thrown by the function.

Value

The return type is always a 2 column data frame with the first column named X is same as the input argument X, while the second column Y is the smoothed dataset.

Author(s)

Chitran Ghosal

Examples

library(StatsChitran)
#build the dataset
set.seed(11)
X <- seq(-4, 4, by = 0.001)
Y <- X^2/16 + gauss(X, mu =  2, sig = 0.3, amp = 3, probability = F)
Y <- Y + rnorm(length(X), mean = 1, sd = 5)


##define the averaging function
lorentz <- function(v){
  y <- 1/((v - 3)^2 + 5)
  return(y)
}

##divide the plot into 4 co-ordinates
subplot(c(2,2))

##plot to check the averaging function
v <- seq(-10, 10, by=0.001)
plot(v, lorentz(v), type='l', col = 'red', main = 'averaging window', lwd=3)
#mark the position of the center value "val"
abline(v = 3)


##plot the dataset
plot(X, Y, col = rgb(0, 0.5, 0, 0.1), pch = 19, main = 'dataset vs averages')
#calculate the lorentz moving average
dat <- movavgf(X, Y, bn=300, fn=300, f=lorentz, val = 3)
#calculate the simple moving average (available in StatsChitran)
dat1 <- movavg(X, Y, bn=300, fn=300)
#plot the averages
lines(dat$X, dat$Y, col=rgb(1,0,0, 0.5), type='l', lwd = 3)
lines(dat1$X, dat1$Y, col=rgb(0,0,1, 0.25), type='l', lwd = 3)
#add legend
legend("topleft", legend = c("dataset", "lorentz MA", "MA"),
       col = c(rgb(0, 0.5, 0, 0.5), rgb(1,0,0, 0.5), rgb(0,0,1, 0.25)), lwd = c(0, 3, 3), pch = c(19, NA, NA),
        bty = "n")



##plot the averages only
plot(dat$X, dat$Y, col=rgb(1,0,0, 0.5), type='l', lwd = 3, main = 'moving averages')
lines(dat1$X, dat1$Y, col=rgb(0,0,1, 0.25), type='l', lwd = 3)
#add legend
legend("topleft", legend = c("lorentz MA", "MA"),
       col = c(rgb(1,0,0, 0.5), rgb(0,0,1, 0.25)), lwd = c( 3, 3), pch = c( NA, NA),
       bty = "n")


##plot the errors
vec <- dat1$Y -dat$Y
plot(X, vec, type = 'l', col=rgb(0,1,1), main = 'absolute diff bw averages')
#add legend
legend("bottomleft", legend = "lorentz MA - MA",
       col = rgb(0,1,1), lwd = 1, pch = NA,
       bty = "n")

Chitran1987/StatsChitran documentation built on Feb. 23, 2025, 8:30 p.m.