tfestimate: Transfer Function Estimate
In gsignal: Signal Processing

tfestimate

R Documentation

Transfer Function Estimate

Description

Finds a transfer function estimate for signals.

Usage

tfestimate(
  x,
  window = nextpow2(sqrt(NROW(x))),
  overlap = 0.5,
  nfft = ifelse(isScalar(window), window, length(window)),
  fs = 1,
  detrend = c("long-mean", "short-mean", "long-linear", "short-linear", "none")
)

tfe(
  x,
  window = nextpow2(sqrt(NROW(x))),
  overlap = 0.5,
  nfft = ifelse(isScalar(window), window, length(window)),
  fs = 1,
  detrend = c("long-mean", "short-mean", "long-linear", "short-linear", "none")
)

Arguments

`x`	input data, specified as a numeric vector or matrix. In case of a vector it represents a single signal; in case of a matrix each column is a signal.
`window`	If `window` is a vector, each segment has the same length as `window` and is multiplied by `window` before (optional) zero-padding and calculation of its periodogram. If `window` is a scalar, each segment has a length of `window` and a Hamming window is used. Default: `nextpow2(sqrt(length(x)))` (the square root of the length of `x` rounded up to the next power of two). The window length must be larger than 3.
`overlap`	segment overlap, specified as a numeric value expressed as a multiple of window or segment length. 0 <= overlap < 1. Default: 0.5.
`nfft`	Length of FFT, specified as an integer scalar. The default is the length of the `window` vector or has the same value as the scalar `window` argument. If `nfft` is larger than the segment length, (seg_len), the data segment is padded `nfft - seg_len` zeros. The default is no padding. Nfft values smaller than the length of the data segment (or window) are ignored. Note that the use of padding to increase the frequency resolution of the spectral estimate is controversial.
`fs`	sampling frequency (Hertz), specified as a positive scalar. Default: 1.
`detrend`	character string specifying detrending option; one of: `"long-mean"` remove the mean from the data before splitting into segments (default) `"short-mean"` remove the mean value of each segment `"long-linear"` remove linear trend from the data before splitting into segments `"short-linear"` remove linear trend from each segment `"none"` no detrending

Details

tfestimate uses Welch's averaged periodogram method.

Value

A list containing the following elements:

freq: vector of frequencies at which the spectral variables are estimated. If x is numeric, power from negative frequencies is added to the positive side of the spectrum, but not at zero or Nyquist (fs/2) frequencies. This keeps power equal in time and spectral domains. If x is complex, then the whole frequency range is returned.
trans: NULL for univariate series. For multivariate series, a matrix containing the transfer function estimates between different series. Column i + (j - 1) * (j - 2)/2 of coh contains the cross-spectral estimates between columns i and j of x, where i < j.

Note

The function tfestimate (and its deprecated alias tfe) is a wrapper for the function pwelch, which is more complete and more flexible.

Author(s)

Peter V. Lanspeary, pvl@mecheng.adelaide.edu.au.
Conversion to R by Geert van Boxtel, G.J.M.vanBoxtel@gmail.com.

Examples

fs <- 1000
f <- 250
t <- seq(0, 1 - 1/fs, 1/fs)
s1 <- sin(2 * pi * f * t) + runif(length(t))
s2 <- sin(2 * pi * f * t - pi / 3) + runif(length(t))
rv <- tfestimate(cbind(s1, s2), fs = fs)
plot(rv$freq, 10*log10(abs(rv$trans)), type="l", xlab = "Frequency",
 ylab = "Tranfer Function Estimate (dB)", main = colnames((rv$trans)))

h <- fir1(30, 0.2, window = rectwin(31))
x <- rnorm(16384)
y <- filter(h, x)
tfe <- tfestimate(cbind(x, y), 1024, fs = 500)
plot(tfe$freq, 10*log10(abs(tfe$trans)), type="l", xlab = "Frequency",
  ylab = "Tranfer Function Estimate (dB)", main = colnames((tfe$trans)))

gsignal documentation built on Sept. 12, 2024, 6:27 a.m.