CGdata {ECG} | R Documentation |
Builds a CGdata (center of gravity) object for a series of single observations.
CGdata(data, from=min(data$x), to=max(data$x), responseFraction = 0.5,
useConstantDelta = FALSE, fixedResponseFraction = 0.5,
useFixedResponseFraction = FALSE, replaceOutliers = TRUE,
responseLowerLimit = min(data$y), responseUpperLimit = max(data$y),
alpha = 0.05, signifDigits = 2, ...)
data |
a data frame structure containing (x, y) columns. |
from |
a numeric value with the initial value of |
to |
a numeric value with the final value of |
responseFraction |
a real value with the fraction (0,1) of the maximum height to be considered in the analysis. |
useConstantDelta |
a logic value, if true then it assumes the values of |
fixedResponseFraction |
a numeric with the fraction of height to be used as a reference to normilize, default value is 0.5. |
useFixedResponseFraction |
a logic value, if TRUE then it uses the value of |
replaceOutliers |
a logic value, if true then it uses the value of |
responseLowerLimit |
a real value to be used as the default to replace outlier values lower than expected. |
responseUpperLimit |
a real value to be used as the default to replace outlier values larger than expected. |
alpha |
a real value, it defines the level of error type I used to estimate the
coverage factor |
signifDigits |
an integer value, it defines the number of significant digits to be used for displaying the result and its uncertainty, default value is 2. |
... |
additional parameters. |
x |
numeric, the estimated value |
u |
numeric, the estimated uncertainty associated to x |
moments |
numeric vector, the estimated mean, variance, skweness and kurtosis |
input |
list, contains the input parameters |
frame |
list, contains the reference values of the analysis. This
information is used to build a verbosed version of its plot. The content of
the list is:
|
H. Gasca-Aragon
require(ECG)
N<- 1000
set.seed(12345)
d1<- 1-sin(seq(1:(5/2*N))/N*pi-pi*3/4)+rnorm(5/2*N, 0, 0.01)
dat<- data.frame(x=1:length(d1),
y=100*(d1-min(d1))/(max(d1)-min(d1)))
CGres <- CGdata(dat)
CGres