# interv_fit: Interpolating the process, given an interval averaged series In c-foschi/mlintervals: Interpolation of interval averaged data using a maximum likelihood method

## Description

Maximum Likelihood interpolation of a Wiener process, given a series of observed integrals over adiacent time intervals of equal lenght. To get point estimates of the interpolating function, use method `predict`. The method assumes a process with homegeneous variance, but the estimated interpolating line is quite robust to heteroskedasticity, anyway, also scale parameter `sigma2` is estimated as well as SEs based on it.

## Usage

 `1` ```interv_fit(M, t_0 = 0, t_unit = 1, t_end = NULL) ```

## Arguments

 `M` series of observed integrals `t_0` time at beginning of the first interval `t_unit` time span of each interval `t_end` time at the end of the last interval. If `t_end` is specified, `t_unit` is ignored

## Details

`predict`

## Value

A list of class `interv_fit`, with the following attributes:

• `\$data`: series M

• `\$knots`: estimated value for the Wiener process in the points between intervals

• `\$sigma2`: estiamated scale parameter

• `\$se`: standard errors for the knots values. They depend on sigma2 and on homoskedasticity assumption

• `\$covariances`: covariances of consecutive knots estimates.

## Examples

 ```1 2 3 4 5 6``` ```M= c(0, 1, -1, 2, 4) mod= interv_fit(M) plot(mod) mod= interv_fit(M, t_0= 5, t_unit= 2) plot(mod) ```

c-foschi/mlintervals documentation built on July 1, 2020, 1:25 a.m.