plot_fui {fastFMM}

Default FUI plotting

Description

Take a fitted fui object produced by fastFMM::fui() and plot the point estimates of fixed effects. When variance was calculated, the plot function also returns 95% pointwise and joint confidence intervals.

Usage

plot_fui(
  fuiobj,
  num_row = NULL,
  xlab = "Functional Domain",
  title_names = NULL,
  ylim = NULL,
  align_x = NULL,
  x_rescale = 1,
  y_val_lim = 1.1,
  y_scal_orig = 0.05,
  return = FALSE
)

Arguments

fuiobj

A object returned from the fui function

num_row

An integer that specifies the number of rows the plots will be displayed on. Defaults to p/2, where p is the number of predictors.

xlab

A string that specifies the x-axis title (i.e., for the functional domain). Defaults to “Functional Domain”

title_names

A vector of strings that has length equal to number of covariates (plus intercept if relevant). Allows one to change the titles of the plots. Defaults to NULL which uses the variable names in the dataframe for titles.

ylim

A 2-dimensional vector that specifies limits of the y-axis in plots.

align_x

A scalar: aligns the plot to a certain point on the functional domain and sets this as 0. This is particularly useful if the functional domain is time. Defaults to 0.

x_rescale

A scalar: rescales the x-axis of plots which is especially useful if time is the functional domain and one wishes to, for example, account for the sampling rate. Defaults to 1.

y_val_lim

A positive scalar that extends the y-axis by a factor for visual purposes. Defaults to $1.10$. Typically does not require adjustment.

y_scal_orig

A positive scalar that determines how much to reduce the length of the y-axis on the bottom. Defaults to 0.05. Typically does not require adjustment.

return

Logical, indicating whether to return the data frame with the coefficient estimates and 95% confidence intervals (CIs). Defaults to FALSE.

Value

Plots of point estimates and CIs. If return = TRUE, also returns a list where each element is a data frame with the coefficient estimates and 95% confidence intervals (CIs).

Author(s)

Gabriel Loewinger [e-mail], Erjia Cui [e-mail]

References

Cui, E., Leroux, A., Smirnova, E., Crainiceanu, C. (2022). Fast Univariate Inference for Longitudinal Functional Models. Journal of Computational and Graphical Statistics, 31(1), 219-230.

Examples

library(refund)
set.seed(1)
DTI_use <- DTI[DTI$ID %in% sample(DTI$ID, 6),]
fit_dti <- fui(formula = cca ~ case + visit + sex + (1 | ID),
               data = DTI_use, family = "gaussian", var = TRUE)
plot_fui(fit_dti)