pulini {unknown} | R Documentation |

## Identify Initial Pulse Locations

### Description

This function accomplishes the first step of the
algorithm in Yang, Liu and Wang (2004).
It finds potential pulse locations. The function itself can also be
used for pulse detection.

### Usage

pulini(x, y, data, method = c("pcp", "CLUSTER"), alpha,
control=list(pcp=list(spline=list(nb=~x, rk=cubic(x)), spar="v",
limnla=c(-10, 3)), cluster=list(sd=mean(y)*0.07, nnadir=2, npeak=3)))

### Arguments

`x` |
a vector of observation time points. |

`y` |
a vector of hormone concentrations. |

`data` |
a data frame containing the variables occurring in the `x`
and `y` arguments. If this option is not specified, the variables
should be on the search list. Missing values are not allowed. |

`method` |
the method to be used for identifying initial pulse
locations. If “pcp”, the change point method based on partial
smoothing spline models is used to detect pulse locations as change
points to the first derivative of the mean function. If “CLUSTER”,
the CLUSTER method proposed by Veldhuis and Johnson (1986) is used. |

`alpha` |
for `method="pcp"`, `alpha` controls the
significance level of a potential change point; for
`method="CLUSTER"`,
`alpha` controls the significance level of the $t$ test. |

`control` |
A list of two components, `pcp` and `cluster`,
to replace the default values in the `pcp` and
`CLUSTER` functions. |

### Details

`pulini`

is a wrapper of two other functions,
`pcp`

and
`CLUSTER`

. See these two
functions for details about control otions. Larger
`alpha` leads to more identified pulses, thus increases
false positive rate and decreases false negative rate.
`CLUSTER`

is faster than `pcp`

,
however, its false negative rate is usually a bit larger.

### Value

a vector of pulse locations.

### Author(s)

Yu-Chieh Yang, Anna Liu, Yuedong Wang

### References

Veldhuis, J. D. and Johnson, M. L. (1986), Cluster analysis:
a simple versatile and robust algorithm for endocrine pulse detection,
*American Journal of Physiology*, **250**, E486-E493.

Yang, Y. (2002), *Detecting Change Points and Hormone Pulses
Using Partial Spline Models*, Ph.D. Thesis, University of
California-Santa Barbara, Dept. of Statistics and Applied Probability.

Yang, Y. and Liu, A. and Wang, Y., (2004), *Detecting
Pulsatile Hormone Secretions Using Nonlinear Mixed Effects
Partial Spline Models*.
Available at www.pstat.ucsb.edu/faculty/yuedong/research.

### See Also

`CLUSTER`

, `pcp`

### Examples

pl1 <- pulini(time, conc, data=acth, method="pcp", alpha=0.6)
pl2 <- pulini(time, conc, data=acth, method="CLUSTER", alpha=.2,
control=list(cluster=list(sd=.05*mean(acth$conc))))