Check 'Arma' filter
Arguments
- b
moving average (
MA
) polynomial coefficients.- a
auto-regressive (
AR
) polynomial coefficients.- w
normalized frequency, ranging from 0 to 1, where 1 is 'Nyquist'
- r_expected
attenuation in decibel of each
w
- fs
sample rate, used to infer the frequencies and formatting print message, not used in calculation; leave it blank by default
Examples
# create a butterworth filter with -3dB (half-power) at [1, 5] Hz
# and -60dB stop-band attenuation at [0.5, 6] Hz
sample_rate <- 20
nyquist <- sample_rate / 2
specs <- buttord(
Wp = c(1, 5) / nyquist,
Ws = c(0.5, 6) / nyquist,
Rp = 3,
Rs = 60
)
filter <- butter(specs)
# filter quality is poor because the AR-coefficients
# creates singular matrix with unstable inverse,
# this will cause `filtfilt` to fail
check_filter(
b = filter$b, a = filter$a,
# frequencies (normalized) where power is evaluated
w = c(1, 5, 0.5, 6) / nyquist,
# expected power
r_expected = c(3, 3, 60, 60)
)
#> <RAVE filter quality test>
#> Attenuation:
#> Freq=0.1 xPi rad/s, mag=-4.094 dB (expected=3 dB)
#> Freq=0.5 xPi rad/s, mag=-3 dB (expected=3 dB)
#> Freq=0.05 xPi rad/s, mag=-129.7 dB (expected=60 dB)
#> Freq=0.6 xPi rad/s, mag=-63.23 dB (expected=60 dB)
#> Reciprocal condition number: 4.4e-21 < .Machine$double.eps
#>
#> WARNING:
#> * Unstable autoregressive (AR) polynomial coefficients