plot_fit | R Documentation |

This function plots the observed data, the estimated mean trajectories, and
the estimated nonlinear functions from the output of `plsmm_lasso`

.

```
plot_fit(
x,
y,
series,
t,
name_group_var,
plsmm_output,
predicted = FALSE,
show_obs = FALSE
)
```

`x` |
A matrix of predictors. |

`y` |
A continuous vector of response variable. |

`series` |
A variable representing different series or groups in the data modeled as a random intercept. |

`t` |
A numeric vector indicating the time points. |

`name_group_var` |
A character string specifying the name of the grouping variable. |

`plsmm_output` |
Output object obtained from the |

`predicted` |
Logical indicating whether to plot predicted values. If |

`show_obs` |
Logical. If |

If `predicted`

is `TRUE`

the function uses the model from `plsmm_output`

to predict unobserved time points on a continuous grid of time.

Two plots:

The first plot shows the observed data and the estimated mean trajectories.

The second plot shows the estimated nonlinear functions.

```
set.seed(123)
data_sim <- simulate_group_inter(
N = 50, n_mvnorm = 3, grouped = TRUE,
timepoints = 3:5, nonpara_inter = TRUE,
sample_from = seq(0, 52, 13),
cos = FALSE, A_vec = c(1, 1.5)
)
sim <- data_sim$sim
x <- as.matrix(sim[, -1:-3])
y <- sim$y
series <- sim$series
t <- sim$t
bases <- create_bases(t)
lambda <- 0.0046
gamma <- 0.00000001
plsmm_output <- plsmm_lasso(x, y, series, t,
name_group_var = "group", bases$bases,
gamma = gamma, lambda = lambda, timexgroup = TRUE,
criterion = "BIC"
)
plot_fit(x, y, series, t, name_group_var = "group", plsmm_output)
```

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