Plot method for IWT results on one-population test

plot method for class "IWT1". Plotting function creating a graphical output of the IWT for the test of the mean of one population: functional data and IWT-adjusted p-values are plotted.

Usage

# S3 method for class 'IWT1'
plot(
  x,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylab = "Functional Data",
  main = NULL,
  lwd = 1,
  col = 1,
  ylim = NULL,
  type = "l",
  ...
)

Arguments

x: The object to be plotted. An object of class "IWT1", usually, a result of a call to IWT1.
xrange: Range of the x axis.
alpha1: First level of significance used to select and display significant effects. Default is alpha1 = 0.05.
alpha2: Second level of significance used to select and display significant effects. Default is alpha1 = 0.01. alpha1 and alpha2 are s.t. alpha2 < alpha1. Otherwise the two values are switched.
ylab: Label of y axis of the plot of functional data. Default is "Functional Data".
main: Plot title.
lwd: Line width for the plot of the adjusted p-value function. Default is lwd=1.
col: Colors for the plot of functional data. Default is col = 1.
ylim: Range of the y axis. Default is NULL, giving a plot with automatic range for functional data.
type: line type for the plot of the adjusted p-value function. Default is type='l'.
...: Additional plotting arguments that can be used with function plot, such as graphical parameters (see par).

Value

No value returned. The function produces a graphical output of the IWT results: the plot of the functional data and the one of the adjusted p-values. The portions of the domain selected as significant by the test at level alpha1 and alpha2 are highlighted in the plot of the adjusted p-value function and in the one of functional data by gray areas (light and dark gray, respectively).

References

Pini, A., & Vantini, S. (2017). Interval-wise testing for functional data. Journal of Nonparametric Statistics, 29(2), 407-424

Pini, A., Vantini, S., Colosimo, B. M., & Grasso, M. (2018). Domain‐selective functional analysis of variance for supervised statistical profile monitoring of signal data. Journal of the Royal Statistical Society: Series C (Applied Statistics) 67(1), 55-81.

Abramowicz, K., Hager, C. K., Pini, A., Schelin, L., Sjostedt de Luna, S., & Vantini, S. (2018). Nonparametric inference for functional‐on‐scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament. Scandinavian Journal of Statistics 45(4), 1036-1061.

Examples

# Importing the NASA temperatures data set
data(NASAtemp)

# Performing the IWT for one population
IWT.result <- IWT1(NASAtemp$paris, mu = 4, B = 10L)
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#> ── creating the p-value matrix: end of row 352 out of 365 ──────────────────────
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#> ── creating the p-value matrix: end of row 356 out of 365 ──────────────────────
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#> ── creating the p-value matrix: end of row 357 out of 365 ──────────────────────
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#> ── creating the p-value matrix: end of row 361 out of 365 ──────────────────────
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#> ── creating the p-value matrix: end of row 362 out of 365 ──────────────────────
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#> ── creating the p-value matrix: end of row 363 out of 365 ──────────────────────
#> 
#> ── creating the p-value matrix: end of row 364 out of 365 ──────────────────────
#> 
#> ── creating the p-value matrix: end of row 365 out of 365 ──────────────────────
#> 
#> ── Interval-Wise Testing completed ─────────────────────────────────────────────

# Plotting the results of the IWT
plot(IWT.result, xrange = c(0, 12), main = 'Paris temperatures')



# Plotting the p-value heatmap
IWTimage(IWT.result, abscissa_range = c(0, 12))


# Selecting the significant components at 5% level
which(IWT.result$adjusted_pval < 0.05)
#>   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
#>  [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
#>  [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
#>  [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
#>  [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
#>  [91]  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 259 260 261 262 263 264
#> [145] 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282
#> [163] 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300
#> [181] 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318
#> [199] 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
#> [217] 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354
#> [235] 355 356 357 358 359 360 361 362 363 364 365