Description Usage Arguments Value Examples

The methods for objects of class `'FMM'`

are:

`coef` | coef method for S4 class `'FMM'` , |

`summary` | summary method for S4 class `'FMM'` , |

`fitted` | fitted method for S4 class `'FMM'` , |

`resid` | resid method for S4 class `'FMM'` . |

1 2 3 4 5 6 7 8 9 10 11 |

`object` |
object of class |

`...` |
additional arguments passed to the method. |

The function

`coef()`

returns a`list`

with two components:`M`

A numeric value. The estimated intercept parameter *M*.`wave`

A `data.frame`

with the estimates of each FMM wave parameters. It is organised as one component per row.The function

`summary()`

displays relevant results of the fitting. When it is assigned, this function returns a`list`

with the following components:`coef`

The `list`

returns by the function`coef()`

.`peak.time`

A `data.frame`

with the estimates of the peak and trough times of each wave. It is organised as one component per row.`resid`

The `vector`

of residuals.`R.squared`

A numerical value with the R squared of each wave. The total R square is computed as the sum of the contribution of each component. The function

`fitted()`

returns a`data.frame`

with two columns:`timePoints`

and`fittedValues`

.The function

`resid()`

returns a numeric`vector`

with residuals of the model.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
## Generate example data:
fmm2.data <- generateFMM(0, 2, c(1.5, 3.4), c(0.2, 2.3), c(0.1, 0.2),
plot = FALSE, outvalues = TRUE,
sigmaNoise = 0.5) # add a gaussian noise with sigma = 0.5
## Fit the FMM model with nback = 2 component
## fit is an object of S4 class 'FMM'
fit <- fitFMM(vData = fmm2.data$y,timePoints = fmm2.data$t,nback = 2,
lengthAlphaGrid = 24,lengthOmegaGrid = 10, numReps = 1)
## Extract coefficients of the model:
coef(fit)
## Summarize results:
summary(fit)
## Results on a list:
res <- summary(fit)
res$peak.time # fiducial points
## fitted values:
fit.values <- fitted(fit)
## residuals
res <- resid(fit)
``` |

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