Short analysis report

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Alex Litovchenko

2026-03-06

This vignette shows a minimal analysis report: fit a model, print fixed effects, plot fitted vs observed, and show residual ACF and Q-Q.

1. Fit model(s)

library(tidyILD)
d <- ild_simulate(n_id = 8, n_obs_per = 10, irregular = TRUE, seed = 101)
x <- ild_prepare(d, id = "id", time = "time", gap_threshold = 7200)
x <- ild_center(x, y)
fit <- ild_lme(y ~ y_bp + y_wp + (1 | id), data = x, ar1 = FALSE, warn_no_ar1 = FALSE)
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> Model failed to converge with max|grad| = 0.117947 (tol = 0.002, component 1)
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
#>  - Rescale variables?

2. Tidy fixed-effects table

tidy_ild_model(fit)
#> # A tibble: 3 × 6
#>   term        estimate std_error   ci_low  ci_high p_value
#>   <chr>          <dbl>     <dbl>    <dbl>    <dbl>   <dbl>
#> 1 (Intercept) 9.93e-17  2.69e-17 4.65e-17 1.52e-16      NA
#> 2 y_bp        1   e+ 0  2.72e-17 1   e+ 0 1   e+ 0      NA
#> 3 y_wp        1   e+ 0  5.29e-17 1   e+ 0 1   e+ 0      NA

With cluster-robust standard errors (requires the clubSandwich package):

tidy_ild_model(fit, se = "robust", robust_type = "CR2")
#> # A tibble: 3 × 6
#>   term        estimate std_error     ci_low  ci_high p_value
#>   <chr>          <dbl>     <dbl>      <dbl>    <dbl>   <dbl>
#> 1 (Intercept) 9.93e-17  1.38e-16 -1.71 e-16 3.70e-16   0.472
#> 2 y_bp        1   e+ 0  2.07e-16  1.000e+ 0 1   e+ 0   0    
#> 3 y_wp        1   e+ 0  1.99e-17  1    e+ 0 1   e+ 0   0

3. Fitted vs observed

ild_plot(fit, type = "fitted")

Fitted vs observed

4. Residual diagnostics: ACF and Q-Q

diag <- ild_diagnostics(fit, type = c("residual_acf", "qq"))
diag
#> ILD diagnostics
#>   Engine: lmer
#>   AR1/CAR1: no
#>   Id column: id  Time column: time
#>   N persons: 8  N observations: 80
#>   Types computed: residual_acf, qq

plots <- plot_ild_diagnostics(diag)
plots$residual_acf
plots$qq

Residual ACF and Q-Q

With AR1 (nlme)

Same workflow with residual autocorrelation (nlme path). If the AR1 model fails to converge on this small example, we show the code only; in practice use more data or a different seed.

fit_ar1 <- tryCatch(
  ild_lme(y ~ y_bp + y_wp, data = x, random = ~ 1 | id, ar1 = TRUE),
  error = function(e) NULL
)
if (!is.null(fit_ar1)) {
  tidy_ild_model(fit_ar1)
} else {
  message("AR1 fit did not converge on this run; use ild_lme(..., ar1 = TRUE) with your data.")
}
#> AR1 fit did not converge on this run; use ild_lme(..., ar1 = TRUE) with your data.

if (!is.null(fit_ar1)) {
  diag_ar1 <- ild_diagnostics(fit_ar1, type = c("residual_acf", "qq"))
  diag_ar1
}

Time-varying effects (TVEM)

When the effect of a predictor may change over time (e.g. stronger in the morning), use ild_tvem() to fit a GAM with a smooth in time and a time-varying coefficient. The plot shows how the effect of the predictor varies over the study timeline; interpret the curve as the estimated effect at each time point (with uncertainty band).

set.seed(101)
d2 <- ild_simulate(n_id = 6, n_obs_per = 12, seed = 101)
d2$x <- rnorm(nrow(d2))
x2 <- ild_prepare(d2, id = "id", time = "time")
tv <- ild_tvem(x2, "y", "x", k = 5, re_id = TRUE)
ild_tvem_plot(tv)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.