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Plot for Functional Two-Sample Test Results

Source: R/plot-fts.R
plot.fts.Rd

The S3 methods autoplot.fts() and plot.fts() are methods for plotting results of functional two-sample tests. They visualize the functional data and the adjusted p-values obtained from the testing procedures for mean comparison of two groups. The plots highlight significant effects at two levels of significance, alpha1 and alpha2, using shaded areas.

Usage

# S3 method for class 'fts'
autoplot(
  object,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylabel = "Functional Data",
  title = NULL,
  linewidth = 0.5,
  ...
)

# S3 method for class 'fts'
plot(
  x,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylabel = "Functional Data",
  title = NULL,
  linewidth = 0.5,
  ...
)

Arguments

object, x

An object of class fts, usually a result of a call to functional_two_sample_test(), iwt2(), twt2(), fdr2(), pct2() or global2().

xrange

A length-2 numeric vector specifying the range of the x-axis for the plots. Defaults to c(0, 1). This should match the domain of the functional data.

alpha1

A numeric value specifying the first level of significance used to select and display significant effects. Defaults to alpha1 = 0.05.

alpha2

A numeric value specifying the second level of significance used to select and display significant effects. Defaults to alpha2 = 0.01.

ylabel

A string specifying the label of the y-axis of the functional data plot. Defaults to "Functional Data".

title

A string specifying the title of the functional data plot. Defaults to NULL in which case no title is displayed.

linewidth

A numeric value specifying the width of the line for the functional data plot. Note that the line width for the adjusted p-value plot will be twice this value. Defaults to linewidth = 0.5.

...

Other arguments passed to specific methods. Not used in this function.

Value

The autoplot.fts() function creates a ggplot object that displays the functional data and the adjusted p-values. The significant intervals at levels alpha1 and alpha2 are highlighted in the plots. The plot.fts() function is a wrapper around autoplot.fts() that prints the plot directly.

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.

See also

IWTimage() for the plot of p-values heatmaps (for IWT).

Examples

# Performing the TWT for two populations
TWT_result <- functional_two_sample_test(
  NASAtemp$paris, NASAtemp$milan,
  correction = "TWT", n_perm = 10L
)

# Plotting the results of the TWT
plot(
  TWT_result,
  xrange = c(0, 12),
  title = "TWT results for testing mean differences"
)


# Selecting the significant components at 5% level
which(TWT_result$adjusted_pval < 0.05)
#>   [1]  11  39  40  47  50  53  55  57  60  61  64  65  66  68  69  70  71  72
#>  [19]  73  76  77  78  80  81  83  87  88  89  90  91  92  93  94  95  96 101
#>  [37] 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
#>  [55] 120 121 122 123 124 125 126 128 129 130 131 132 133 134 135 136 137 138
#>  [73] 140 141 142 143 144 147 148 149 150 151 152 153 154 155 156 157 158 159
#>  [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] 214 215 216 217 218 219 220 222 223 224 225 226 227 228 229 230 231 232
#> [163] 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250
#> [181] 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268
#> [199] 269 270 271 272 273 274 275 276 281 282 286 287 289 290 291 292 294 295
#> [217] 304 324 327 330

# Performing the IWT for two populations
IWT_result <- functional_two_sample_test(
  NASAtemp$paris, NASAtemp$milan,
  correction = "IWT", n_perm = 10L
)

# Plotting the results of the IWT
plot(
  IWT_result,
  xrange = c(0, 12),
  title = "IWT results for testing mean differences"
)


# Selecting the significant components at 5% level
which(IWT_result$adjusted_pval < 0.05)
#>   [1]  73  87  88  89  90  91  92  93  94  95  96  97  99 100 101 102 103 104
#>  [19] 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122
#>  [37] 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
#>  [55] 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158
#>  [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] 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250
#> [163] 251 252 253 254 255 256 257 258 259 260 261 262 264 266 267 268 269 270
#> [181] 271