Skip to contents

Plot for Functional Two-Sample Test Results

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

The S3 methods autoplot.ftwosample() and plot.ftwosample() 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 'ftwosample'
autoplot(
  object,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylabel = "Functional Data",
  title = NULL,
  linewidth = 0.5,
  ...
)

# S3 method for class 'ftwosample'
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 ftwosample, 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.ftwosample() 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.ftwosample() function is a wrapper around autoplot.ftwosample() 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", B = 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]  40  49  50  55  56  57  61  62  64  65  66  67  68  69  70  71  72  73
#>  [19]  74  86  87  88  89  90  91  92  93  94  95 101 102 103 104 105 106 107
#>  [37] 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
#>  [55] 126 127 128 129 130 131 132 133 134 135 136 137 138 140 141 142 143 144
#>  [73] 145 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163
#>  [91] 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181
#> [109] 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199
#> [127] 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217
#> [145] 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235
#> [163] 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253
#> [181] 254 255 256 257 258 259 260 261 262 264 265 266 267 268 269 270 271 272
#> [199] 273 274 275 276 277 278 280 281 282 283 284 286 288 289 291 292 296 299
#> [217] 302 303 304 327

# Performing the IWT for two populations
IWT_result <- functional_two_sample_test(
  NASAtemp$paris, NASAtemp$milan,
  correction = "IWT", B = 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] 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
#>  [19] 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
#>  [37] 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153
#>  [55] 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171
#>  [73] 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189
#>  [91] 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207
#> [109] 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
#> [127] 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243
#> [145] 244 245 247 248 249 250 251 252 253 254 255 256 257 258 259 260 263 264
#> [163] 265 266 267 268 269 270 271 272 273 274 275