Plot method for fdatest results on two-population test

plot method for class "fdatest2". Plotting function creating a graphical output of the testing procedures for the mean comparison of two groups: functional data and adjusted p-values are plotted.

Usage

# S3 method for class 'fdatest2'
plot(
  x,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylab = "Functional Data",
  main = NULL,
  lwd = 0.5,
  col = c(1, 2),
  ylim = NULL,
  type = "l",
  ...
)

Arguments

x: The object to be plotted. An object of class "fdatest2", usually, a result of a call to fdatest2, IWT2, TWT2, FDR2, or PCT2.
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 fdatest 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 TWT for two populations
TWT.result <- fdatest2(
  NASAtemp$paris, NASAtemp$milan, 
  method = "TWT", B = 10L
)
#> 
#> ── Threshold-wise tests ────────────────────────────────────────────────────────

# Plotting the results of the TWT
plot(
  TWT.result, 
  xrange = c(0, 12), 
  main = 'TWT results for testing mean differences'
)



# Selecting the significant components at 5% level
which(TWT.result$adjusted_pval < 0.05)
#>   [1]  41  45  49  50  55  57  58  61  62  63  64  65  69  70  71  72  73  74
#>  [19]  76  77  78  79  80  81  83  87  88  89  90  91  92  93  94  95  96 100
#>  [37] 101 102 103 104 105 106 107 113 114 115 116 117 118 119 120 121 122 123
#>  [55] 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141
#>  [73] 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
#>  [91] 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177
#> [109] 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
#> [127] 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213
#> [145] 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231
#> [163] 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249
#> [181] 250 251 252 253 254 255 256 257 258 259 260 261 262 264 265 266 267 268
#> [199] 269 271 272 273 274 275 276 281 282 283 286 287 288 289 290 291 295 299
#> [217] 324

# Performing the IWT for two populations
IWT.result <- fdatest2(
  NASAtemp$paris, NASAtemp$milan, 
  method = "IWT", B = 10L
)
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#> ── Creating the p-value matrix: end of row 235 out of 365 ──────────────────────
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#> ── Creating the p-value matrix: end of row 236 out of 365 ──────────────────────
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#> ── Creating the p-value matrix: end of row 344 out of 365 ──────────────────────
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#> ── Creating the p-value matrix: end of row 345 out of 365 ──────────────────────
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#> ── Creating the p-value matrix: end of row 346 out of 365 ──────────────────────
#> 
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#> 
#> ── Creating the p-value matrix: end of row 348 out of 365 ──────────────────────
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#> 
#> ── Creating the p-value matrix: end of row 350 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 351 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 352 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 353 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 354 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 355 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 356 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 357 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 358 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 359 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 360 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 361 out of 365 ──────────────────────
#> 
#> ── Creating the p-value matrix: end of row 362 out of 365 ──────────────────────
#> 
#> ── 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 = 'IWT results for testing mean differences'
)



# Selecting the significant components at 5% level
which(IWT.result$adjusted_pval < 0.05)
#>   [1]  87  88  89  90  91  92  93  94  95  96  97 100 101 102 103 104 105 106
#>  [19] 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124
#>  [37] 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142
#>  [55] 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
#>  [73] 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178
#>  [91] 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
#> [109] 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214
#> [127] 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232
#> [145] 233 234 235 236 237 238 239 240 241 242 243 244 245 247 248 249 250 251
#> [163] 252 253 254 255 256 257 258 259 260 261 262 264 265 266 267 269 270 271
#> [181] 272