Obtains the maximum likelihood estimates of the parameters for linear mixed effects models with random intercept and a stationary or non-stationary stochastic process component, under multivariate normal response distribution

1 2 |

`formula` |
a typical |

`data` |
a data frame from which the variables are to be extracted |

`id` |
a numerical vector for subject identification |

`process` |
a character string for the stochastic process: |

`timeVar` |
a numerical vector for the time variable |

`init` |
a numerical vector of initial values for the variance parameters to
start the Fisher-Scoring or Nelder-Mead algorithms;
if the user does not provide their own values |

`tol` |
a numerical value for the maximum tolerance to assess the convergence |

`maxiter` |
a numerical value for the number of iterations for the Fisher-Scoring or Nelder-Mead algorithms |

`silent` |
a character string, if set to |

For `"process"`

, `"sgp-powered-power-method"`

is a general form for stationary process
with powered correlation function. `"power"`

is the shapre parameter and
corresponds to `"c"`

in

*exp(-|t-s|^c/φ),*

where *t* and *s* are two time points and *φ* is the scale parameter,
and `"method"`

might be `"fs"`

for Fisher-Scoring or `"nm"`

for Nelder-Mead.
Some examples are: `"sgp-powered-1-fs"`

for stationary process with exponential correlation
function with Fisher-Scoring algorithm and
`"sgp-powered-2-nm"`

for stationary process with Gaussian correlation
function with Nelder-Mead algorithm. Similarly, `"sgp-matern-kappa"`

is a general form for
stationary process with Matern correlation
function. `"kappa"`

is the shape parameter and corresponds to *κ* in

*≤ft\{ 2^{κ-1} Γ ≤ft( κ \right) \right \}^{-1} ≤ft(|t - s|/ν \right)^{κ} K_{κ} ≤ft(|t - s|/ν \right),*

*t* and *s* are two time points and *ν* is the scale parameter.
An example is `"sgp-matern-0.5"`

for
stationary process with exponential correlation function. Nelder-Mead algorithm is automatically specified
for the choice of Matern, i.e. Fisher-Scoring is not available.

`"init"`

assumes the following:

- 3 element vectors for `"process = bm"`

and `"process = ibm"`

, with initials for the
variances of random intercept, stochastic process and measurement error, respectively

- 4 element vector for `"process = iou"`

, with initials for the variance of
random intercept variance, (two) parameters of the stochastic process, variance of measurement error

- 3 element vector for `"process = sgp-powered-power-fs"`

, with initials for
log of the fraction of the variance of random intercept and variance of the process,
log(*φ*) and log of the fraction of the variance of measurement error and
variance of the process.

- `NULL`

for any of the specification of `"process"`

, in which case `lmenssp`

finds the initals internally using the `lme`

function of the nlme package.

Returns the results as lists

Ozgur Asar, Peter J. Diggle

Diggle PJ (1988) An approach to the analysis of repeated measurements. *Biometrics*, **44**, 959-971.

Diggle PJ, Sousa I, Asar O (2015) Real time monitoring of progression towards renal failure in primary care patients.
*Biostatistics*, 16(3), 522-536.

Taylor JMG, Cumberland WG, Sy JP (1994) A Stochastic Model for Analysis of Longitudinal AIDS Data.
*Journal of the American Statistical Association*, **89**, 727-736.

1 2 3 4 5 6 7 8 9 10 11 | ```
# loading the data set and subsetting it for the first 20 patients
# for the sake illustration of the usage of the functions
data(data.sim.ibm)
data.sim.ibm.short <- data.sim.ibm[data.sim.ibm$id <= 20, ]
# fitting the model with integrated Brownian motion
fit.ibm <- lmenssp(log.egfr ~ sex + bage + fu + pwl, data = data.sim.ibm.short,
id = data.sim.ibm.short$id, process = "ibm", timeVar = data.sim.ibm.short$fu,
silent = FALSE)
fit.ibm
``` |

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