Como ajustar dinamicamente o ylim em gráficos R base?

Tenho uma lista de dados (veja abaixo), que represento graficamente usando este script:

# Define plotting parameters
pch_values <- c(16, 17)  # Different symbols for beta_0 and beta_1
colors <- c("red", "blue", "gray60")  # Colors for different noise types
lty_values <- c(1, 2, 3)  # Line types for different noise types
quartile_titles <- c("Absolute Bias of First Quartile", "Absolute Bias of Second Quartile (Median)", "Absolute Bias of Third Quartile")

# Directory for saving plots
output_dir <- "Abs_Quartile_Bias_Plots"
if (!dir.exists(output_dir)) dir.create(output_dir)

# Compute unified y-axis limits for all quartile plots in test_data
y_values_all <- unlist(sapply(test_data, function(noise_data) {
  sapply(noise_data, function(T_data) T_data)
}), use.names = FALSE)

ylim_min <- min(y_values_all, na.rm = TRUE)
ylim_max <- max(y_values_all, na.rm = TRUE)
margin <- 0.05 * (ylim_max - ylim_min)  # Add 5% margin above and below
unified_ylim <- c(ylim_min - margin, ylim_max + margin)

# Set output file
png(file.path(output_dir, "Abs_Quartile_Bias_Case1.png"), 
    width = 1200, height = 400, res = 120)
par(mfrow = c(1, 3), mar = c(4, 4, 2, 1))  # 3 side-by-side subplots

# Plot each absolute quartile bias
for (quartile_idx in 1:3) {
  # Start a blank plot with unified y-axis limits
  plot(NULL, log = "x", xlim = range(sample_sizes), ylim = unified_ylim, 
       xlab = "Sample Size (T)", 
       ylab = quartile_titles[quartile_idx],
       main = quartile_titles[quartile_idx])
  
  # Add lines and points for beta_0 and beta_1 for each noise type
  for (noise_idx in seq_along(noise_types)) {
    noise <- noise_types[noise_idx]
    for (coef_name_idx in seq_along(pch_values)) {
      coef_name <- c("beta_0", "beta_1")[coef_name_idx]  # Explicitly specify coefficient names
      
      y_values <- sapply(test_data[[noise]][[coef_name]], function(T_data) T_data[[quartile_idx]])
      
      # Plot lines
      lines(sample_sizes, y_values, col = colors[noise_idx], lty = lty_values[noise_idx], lwd = 1)
      # Plot points
      points(sample_sizes, y_values, col = colors[noise_idx], pch = pch_values[coef_name_idx])
    }
  }
  
  # Add legends
  if (quartile_idx == 1) {  # Only add legends to the first plot
    legend("topright", 
           legend = c(expression(beta[0]), expression(beta[1])),
           pch = pch_values, 
           col = "black", bty = "n")
    legend("topright", 
           legend = str_to_title(noise_types), 
           col = colors, 
           lty = lty_values, 
           lwd = 1, bty = "n", inset = c(.18, 0))
  }
}

dev.off()

O que produz o seguinte valor:

O problema, como você pode ver, é que ele ylimnão é ajustado para cada subtrama, o que cria gráficos em que a extremidade superior ylimfica longe do maior valor das linhas dentro do subtrama.

Pergunta:

Alguém pode me ajudar a modificar o código para que ele ylimseja ajustado dinamicamente ao intervalo de dados em questão?

Peço desculpas se meus dados não estiverem organizados de forma ideal.

Aqui está a dput()saída dos dados em questão:

> dput(test_data)
list(gaussian = list(beta_0 = list(`50` = c(`25%` = 0.243594614710333, 
`50%` = 0.00277753736411457, `75%` = 0.245669686373254), `100` = c(`25%` = 0.171918499273751, 
`50%` = 9.39373213166839e-05, `75%` = 0.183152746837558), `200` = c(`25%` = 0.116881126444175, 
`50%` = 0.00117104968639603, `75%` = 0.123610284184898), `500` = c(`25%` = 0.0804521896728776, 
`50%` = 0.00103027837221392, `75%` = 0.0784101295289328), `1000` = c(`25%` = 0.0541010700519603, 
`50%` = 0.00294703982537503, `75%` = 0.0531978110356446), `2000` = c(`25%` = 0.0377543581178926, 
`50%` = 0.00333965713516848, `75%` = 0.0413079011502395), `5000` = c(`25%` = 0.0217425538489762, 
`50%` = 0.000460109571013723, `75%` = 0.0257457441062152)), beta_1 = list(
    `50` = c(`25%` = 0.00882506794511806, `50%` = 0.000371504996548033, 
    `75%` = 0.00765082797750871), `100` = c(`25%` = 0.00290263844961158, 
    `50%` = 6.82498075157412e-05, `75%` = 0.00286586470881645
    ), `200` = c(`25%` = 0.00109095625190614, `50%` = 5.22460831402505e-05, 
    `75%` = 0.00109448558611547), `500` = c(`25%` = 0.00026571655332952, 
    `50%` = 8.85613155698906e-07, `75%` = 0.000259202833770455
    ), `1000` = c(`25%` = 9.21700578033757e-05, `50%` = 7.24859772205377e-07, 
    `75%` = 9.59841859602406e-05), `2000` = c(`25%` = 3.41775667214161e-05, 
    `50%` = 3.30163678108342e-06, `75%` = 2.86060854759462e-05
    ), `5000` = c(`25%` = 8.28928386886751e-06, `50%` = 4.18267251500737e-07, 
    `75%` = 8.43229566438453e-06))), laplace = list(beta_0 = list(
    `50` = c(`25%` = 0.198717632408904, `50%` = 0.0081310174987681, 
    `75%` = 0.210213471331161), `100` = c(`25%` = 0.130486991150159, 
    `50%` = 0.00513011870745728, `75%` = 0.151674765218005), 
    `200` = c(`25%` = 0.111212423845099, `50%` = 0.00734704246964579, 
    `75%` = 0.0981520122896047), `500` = c(`25%` = 0.0630465314163474, 
    `50%` = 0.00897075972839323, `75%` = 0.0683817884348215), 
    `1000` = c(`25%` = 0.042034483419907, `50%` = 0.00132855758101336, 
    `75%` = 0.0425837417269435), `2000` = c(`25%` = 0.0339129308142302, 
    `50%` = 0.00345531227818663, `75%` = 0.0280143719137393), 
    `5000` = c(`25%` = 0.0198791807771237, `50%` = 0.000479565883414246, 
    `75%` = 0.0194025102135911)), beta_1 = list(`50` = c(`25%` = 0.00740494352386101, 
`50%` = 0.00058286479518399, `75%` = 0.00741009508728219), `100` = c(`25%` = 0.00244184197921071, 
`50%` = 0.000134746791191187, `75%` = 0.00244511866997321), `200` = c(`25%` = 0.000866632964359182, 
`50%` = 8.7678493771115e-05, `75%` = 0.0010573916668597), `500` = c(`25%` = 0.000235006834866658, 
`50%` = 9.75544170911391e-06, `75%` = 0.000212441344117131), 
    `1000` = c(`25%` = 8.00301522252411e-05, `50%` = 1.07320472242378e-06, 
    `75%` = 7.29608951477445e-05), `2000` = c(`25%` = 2.54472054908028e-05, 
    `50%` = 1.46816056423305e-06, `75%` = 2.84447257516973e-05
    ), `5000` = c(`25%` = 6.16220803828504e-06, `50%` = 1.48241995123755e-07, 
    `75%` = 6.65929311072233e-06))), cauchy = list(beta_0 = list(
    `50` = c(`25%` = 0.274524411682779, `50%` = 0.0361625726093231, 
    `75%` = 0.336776027564404), `100` = c(`25%` = 0.198215929997078, 
    `50%` = 0.00328917426856501, `75%` = 0.207109168934052), 
    `200` = c(`25%` = 0.155318692086412, `50%` = 0.00672679937945397, 
    `75%` = 0.154703229084234), `500` = c(`25%` = 0.0880029220252172, 
    `50%` = 0.00278228951379011, `75%` = 0.0884479446139164), 
    `1000` = c(`25%` = 0.0688536372229773, `50%` = 0.00305819621170977, 
    `75%` = 0.0644673760777885), `2000` = c(`25%` = 0.0507696330400698, 
    `50%` = 0.000188404592404767, `75%` = 0.0500372607497894), 
    `5000` = c(`25%` = 0.0310586078673812, `50%` = 0.0017270922827648, 
    `75%` = 0.0322962294132709)), beta_1 = list(`50` = c(`25%` = 0.0120626033466085, 
`50%` = 0.0017021299049178, `75%` = 0.00978118814587825), `100` = c(`25%` = 0.00338466332476295, 
`50%` = 7.46446842248005e-06, `75%` = 0.0037767462745677), `200` = c(`25%` = 0.00125677406659053, 
`50%` = 4.41521154233016e-05, `75%` = 0.00133801259948108), `500` = c(`25%` = 0.000315465752873667, 
`50%` = 1.30850267132665e-05, `75%` = 0.0003114951811094), `1000` = c(`25%` = 0.000119877019459924, 
`50%` = 5.46893714759022e-07, `75%` = 0.000117584995672271), 
    `2000` = c(`25%` = 4.14058369635484e-05, `50%` = 9.97860400531181e-07, 
    `75%` = 4.36402038799244e-05), `5000` = c(`25%` = 9.47781604621056e-06, 
    `50%` = 9.22661139490799e-07, `75%` = 1.12907100797699e-05
    ))))