gsignal (version 0.3-7)

filter.sgolayFilter: Savitzky-Golay filtering

Description

Filter a signal with a Savitzky-Golay FIR filter.

Usage

# S3 method for sgolayFilter
filter(filt, x, ...)
sgolayfilt(x, p = 3, n = p + 3 - p%%2, m = 0, ts = 1)

Value

The filtered signal, of the same dimensions as the input signal.

Arguments

filt

Filter characteristics, usually the result of a call to sgolay

x

the input signal to be filtered, specified as a vector or as a matrix. If x is a matrix, each column is filtered.

...

Additional arguments (ignored)

p

Polynomial filter order; must be smaller than n.

n

Filter length; must a an odd positive integer.

m

Return the m-th derivative of the filter coefficients. Default: 0

ts

Scaling factor. Default: 1

Author

Paul Kienzle, .
Conversion to R Tom Short,
adapted by Geert van Boxtel, .

Details

Savitzky-Golay smoothing filters are typically used to "smooth out" a noisy signal whose frequency span (without noise) is large. They are also called digital smoothing polynomial filters or least-squares smoothing filters. Savitzky-Golay filters perform better in some applications than standard averaging FIR filters, which tend to filter high-frequency content along with the noise. Savitzky-Golay filters are more effective at preserving high frequency signal components but less successful at rejecting noise.

Savitzky-Golay filters are optimal in the sense that they minimize the least-squares error in fitting a polynomial to frames of noisy data.

See Also

sgolay

Examples

# Compare a 5 sample averager, an order-5 butterworth lowpass
# filter (cutoff 1/3) and sgolayfilt(x, 3, 5), the best cubic
# estimated from 5 points.
bf <- butter(5, 1/3)
x <- c(rep(0, 15), rep(10, 10), rep(0, 15))
sg <- sgolayfilt(x)
plot(sg, type="l", xlab = "", ylab = "")
lines(filtfilt(rep(1, 5) / 5, 1, x), col = "red") # averaging filter
lines(filtfilt(bf, x), col = "blue")              # butterworth
points(x, pch = "x")                              # original data
legend("topleft", c("sgolay (3,5)", "5 sample average", "order 5
Butterworth", "original data"), lty=c(1, 1, 1, NA),
pch = c(NA, NA, NA, "x"), col = c(1, "red", "blue", 1))