Mean and Standard Deviation.

Calculate mean and standard deviation, and mean, mean \(\pm\) one standard deviation, respectively.

# S4 method for numeric
mean_sd(x, na.rm = TRUE, ...)

# S4 method for matrix
mean_sd(x, na.rm = TRUE, ...)

# S4 method for hyperSpec
mean_sd(x, na.rm = TRUE, ...)

# S4 method for numeric
mean_pm_sd(x, na.rm = TRUE, ...)

# S4 method for matrix
mean_pm_sd(x, na.rm = TRUE, ...)

# S4 method for hyperSpec
mean_pm_sd(x, na.rm = TRUE, ...)

# S4 method for hyperSpec
mean(x, na.rm = TRUE, ...)

# S4 method for hyperSpec
quantile(x, probs = seq(0, 1, 0.5), na.rm = TRUE, names = "num", ...)

Arguments

x	a numeric vector
na.rm	handed to base::mean() and stats::sd()
...	ignored (needed to make function generic)
probs	the quantiles, see stats::quantile()
names	"pretty" results in percentages (like stats::quantile()'s names = TRUE), "num" results in the row names being as.character(probs) (good for ggplot2 getting the order of the quantiles right). Otherwise, no names are assigned.

Value

mean_sd returns a vector with two values (mean and standard deviation) of x.

mean_sd (matrix) returns a matrix with the mean spectrum in the first row and the standard deviation in the 2nd.

mean_sd returns a hyperSpec object with the mean spectrum in the first row and the standard deviation in the 2nd.

mean_pm_sd returns a vector with 3 values: mean - 1 sd, mean, mean + 1 sd

mean_pm_sd (matrix) returns a matrix containing mean - sd, mean, and mean + sd rows.

For hyperSpec objects, mean_pm_sd returns a hyperSpec object containing mean - sd, mean, and mean + sd spectra.

For hyperSpec object, mean returns a hyperSpec object containing the mean spectrum.

For hyperSpec object, quantile() returns a hyperSpec object containing the respective quantile spectra.

Details

These functions are provided for convenience.

See also

base::mean(), stats::sd()

base::mean(), stats::sd()

stats::quantile()

Author

C. Beleites

Examples

mean_sd(flu [, , 405 ~ 410])
#> hyperSpec object
#>    2 spectra
#>    1 data columns
#>    11 data points / spectrum
#> wavelength: lambda/nm [numeric] 405.0 405.5 ... 410 
#> data:  (2 rows x 1 columns)
#>    1. spc: I[fl]/"a.u." [matrix, array11] 113.90939 63.95161 ... 88.86944 

mean_sd(flu$spc)
#>            405     405.5       406     406.5       407     407.5       408
#> mean 113.90939 120.08039 123.88961 128.63661 132.78711 138.26267 142.52667
#> sd    63.95161  66.68462  68.53711  71.32635  73.27403  76.26193  79.05704
#>          408.5       409     409.5       410     410.5      411    411.5
#> mean 146.35206 151.43228 156.09817 161.82439 165.86478 171.5021 176.3894
#> sd    81.11082  83.44635  86.21839  88.86944  90.86983  93.5278  97.0369
#>            412    412.5      413    413.5      414    414.5      415  415.5
#> mean 180.51522 185.6836 190.1309 195.8358 200.4855 204.6093 210.0138 214.64
#> sd    99.25456 102.7686 104.5448 107.9088 110.2483 111.9924 115.4464 118.26
#>           416    416.5      417    417.5      418    418.5      419    419.5
#> mean 219.1380 224.5761 227.9237 232.8236 237.7171 241.7527 246.4742 250.5289
#> sd   120.7447 123.9144 126.1828 128.7551 129.8156 132.8904 135.4853 137.1431
#>           420    420.5      421    421.5      422    422.5      423    423.5
#> mean 254.6781 258.4790 264.0061 267.9860 273.0221 277.4778 281.3341 285.0475
#> sd   139.3884 141.3262 144.7961 146.7983 149.8890 152.0896 154.3166 156.1366
#>           424    424.5      425    425.5      426    426.5      427    427.5
#> mean 290.1924 294.2909 298.3123 302.2134 307.5095 312.6365 316.1736 319.8145
#> sd   159.9184 162.8058 164.8594 165.1881 168.6816 172.6384 173.9014 175.6463
#>           428    428.5      429    429.5      430    430.5      431    431.5
#> mean 325.3752 328.9191 332.6454 336.7873 340.4211 344.5639 349.2603 351.5914
#> sd   178.6666 181.0399 183.3152 185.6277 186.6223 189.7741 192.6296 194.1671
#>           432    432.5      433    433.5      434    434.5      435    435.5
#> mean 354.2620 358.1013 361.8068 363.6526 365.7068 366.8592 369.8816 372.5533
#> sd   195.5245 196.7954 198.6743 200.5592 200.9523 201.5470 203.8599 204.7726
#>           436    436.5      437    437.5      438    438.5      439    439.5
#> mean 374.8713 375.8997 378.4399 378.6621 380.7669 382.7149 382.8039 383.6267
#> sd   206.1331 207.0960 208.1450 208.7843 210.1876 210.3305 210.1147 211.5687
#>           440    440.5      441    441.5      442    442.5      443    443.5
#> mean 383.4117 384.9124 384.9061 386.2123 386.0671 386.8118 387.1116 387.9868
#> sd   210.9692 211.1969 212.3513 210.9188 210.6814 211.5056 213.4941 212.2282
#>           444    444.5      445    445.5      446    446.5      447    447.5
#> mean 388.2645 386.9496 387.7631 387.6842 388.9684 388.8592 387.6974 387.5131
#> sd   213.8395 213.8715 212.7600 212.7473 214.0275 214.1388 213.7061 213.5430
#>           448    448.5      449    449.5      450    450.5      451    451.5
#> mean 387.8354 387.7904 388.0669 388.3706 388.3181 388.0719 386.9387 387.4204
#> sd   213.4142 212.5135 213.5708 211.6471 212.2151 212.5157 211.6122 212.4420
#>           452    452.5      453    453.5      454    454.5      455    455.5
#> mean 387.2329 385.1973 385.5947 385.0308 384.9724 385.6444 383.5018 382.0557
#> sd   213.0798 212.6830 211.5552 211.7677 211.6549 211.7323 208.8900 209.5972
#>           456    456.5      457    457.5      458    458.5      459    459.5
#> mean 380.9131 379.6323 379.4236 376.7526 376.1533 373.7147 371.8777 369.0225
#> sd   209.3029 208.5275 207.9218 207.2801 206.3606 205.1782 204.3870 202.7046
#>           460    460.5      461    461.5      462    462.5      463    463.5
#> mean 367.3478 364.0053 363.2114 361.1244 358.5899 354.8971 352.0969 349.0689
#> sd   202.7634 199.7319 199.7003 197.7912 196.5260 195.9355 193.7476 190.8617
#>           464    464.5      465    465.5      466    466.5      467    467.5
#> mean 347.7439 344.6271 340.4968 338.4603 335.9258 332.6344 329.3642 325.6729
#> sd   190.1415 188.8953 186.8400 185.6940 185.2942 183.5705 181.6199 178.8888
#>           468    468.5      469    469.5      470    470.5      471    471.5
#> mean 322.0702 319.5687 314.7917 312.4208 310.4492 305.3859 302.4756 299.4622
#> sd   176.0399 176.1274 173.3872 172.4460 170.4795 166.8526 166.5719 163.6277
#>           472    472.5      473    473.5      474    474.5      475    475.5
#> mean 296.7000 292.2196 288.3032 286.7105 283.4853 280.3511 276.9047 274.8581
#> sd   162.6267 161.2979 158.8969 157.3042 155.7632 153.7710 151.6023 150.2532
#>           476    476.5      477    477.5      478    478.5     479    479.5
#> mean 271.2237 269.0067 266.5739 263.3924 260.4180 258.1373 255.791 253.8093
#> sd   148.9218 148.0351 146.8918 144.1210 143.0114 142.0281 140.028 140.1034
#>           480    480.5      481    481.5      482    482.5      483    483.5
#> mean 249.5182 248.2199 245.3162 241.8370 240.8851 238.1019 235.3928 232.0592
#> sd   137.6791 135.9537 134.2462 131.9646 131.4004 130.0671 129.0721 128.4586
#>           484    484.5      485    485.5      486    486.5      487    487.5
#> mean 230.1664 227.5049 224.4877 221.6313 219.4647 215.9441 213.1634 210.1469
#> sd   127.6313 124.3675 123.2050 121.3864 120.2081 118.3317 116.9249 114.8779
#>           488    488.5      489    489.5      490    490.5      491    491.5
#> mean 208.6204 205.8629 203.6631 200.3850 198.2890 195.3428 192.3032 189.3392
#> sd   114.6096 113.3597 111.3558 109.7793 109.5708 106.8053 105.5473 103.6283
#>           492    492.5      493     493.5       494     494.5       495
#> mean 186.5108 183.8356 180.6879 178.78053 175.72864 173.40081 170.52147
#> sd   102.6725 101.4147  99.9830  98.97507  97.29897  95.44527  93.76262

mean_sd(flu)
#> hyperSpec object
#>    2 spectra
#>    1 data columns
#>    181 data points / spectrum
#> wavelength: lambda/nm [numeric] 405.0 405.5 ... 495 
#> data:  (2 rows x 1 columns)
#>    1. spc: I[fl]/"a.u." [matrix, array181] 113.90939 63.95161 ... 93.76262 

mean_pm_sd(flu$c)
#> mean.minus.sd          mean  mean.plus.sd 
#>    0.08145857    0.17500000    0.26854143 

mean_pm_sd(flu$spc)
#>                 405     405.5      406     406.5       407     407.5       408
#> mean - sd  49.95778  53.39577  55.3525  57.31027  59.51308  62.00074  63.46963
#> mean      113.90939 120.08039 123.8896 128.63661 132.78711 138.26267 142.52667
#> mean + sd 177.86100 186.76501 192.4267 199.96296 206.06114 214.52459 221.58371
#>               408.5       409     409.5       410     410.5       411     411.5
#> mean - sd  65.24124  67.98593  69.87977  72.95495  74.99495  77.97425  79.35249
#> mean      146.35206 151.43228 156.09817 161.82439 165.86478 171.50206 176.38939
#> mean + sd 227.46287 234.87862 242.31656 250.69383 256.73461 265.02986 273.42629
#>                 412    412.5       413     413.5       414     414.5       415
#> mean - sd  81.26066  82.9150  85.58614  87.92704  90.23721  92.61698  94.56743
#> mean      180.51522 185.6836 190.13094 195.83583 200.48550 204.60933 210.01383
#> mean + sd 279.76979 288.4521 294.67575 303.74462 310.73379 316.60168 325.46024
#>               415.5       416    416.5      417    417.5      418    418.5
#> mean - sd  96.37996  98.39334 100.6617 101.7409 104.0685 107.9015 108.8622
#> mean      214.64000 219.13800 224.5761 227.9237 232.8236 237.7171 241.7527
#> mean + sd 332.90004 339.88266 348.4904 354.1065 361.5787 367.5328 374.6431
#>                419    419.5      420    420.5      421    421.5      422
#> mean - sd 110.9890 113.3858 115.2896 117.1528 119.2100 121.1877 123.1330
#> mean      246.4742 250.5289 254.6781 258.4790 264.0061 267.9860 273.0221
#> mean + sd 381.9595 387.6720 394.0665 399.8052 408.8021 414.7843 422.9111
#>              422.5      423    423.5      424    424.5      425    425.5
#> mean - sd 125.3882 127.0175 128.9109 130.2740 131.4851 133.4529 137.0253
#> mean      277.4778 281.3341 285.0475 290.1924 294.2909 298.3123 302.2134
#> mean + sd 429.5674 435.6506 441.1841 450.1108 457.0967 463.1717 467.4016
#>                426    426.5      427    427.5      428    428.5      429
#> mean - sd 138.8279 139.9981 142.2722 144.1681 146.7086 147.8792 149.3303
#> mean      307.5095 312.6365 316.1736 319.8145 325.3752 328.9191 332.6454
#> mean + sd 476.1911 485.2749 490.0750 495.4608 504.0418 509.9590 515.9606
#>              429.5      430    430.5      431    431.5      432    432.5
#> mean - sd 151.1596 153.7988 154.7898 156.6307 157.4243 158.7375 161.3059
#> mean      336.7873 340.4211 344.5639 349.2603 351.5914 354.2620 358.1013
#> mean + sd 522.4151 527.0434 534.3380 541.8899 545.7585 549.7865 554.8966
#>                433    433.5      434    434.5      435    435.5      436
#> mean - sd 163.1325 163.0934 164.7544 165.3122 166.0218 167.7807 168.7382
#> mean      361.8068 363.6526 365.7068 366.8592 369.8816 372.5533 374.8713
#> mean + sd 560.4812 564.2118 566.6591 568.4062 573.7415 577.3258 581.0044
#>              436.5      437    437.5      438    438.5      439    439.5
#> mean - sd 168.8037 170.2950 169.8777 170.5793 172.3844 172.6892 172.0580
#> mean      375.8997 378.4399 378.6621 380.7669 382.7149 382.8039 383.6267
#> mean + sd 582.9957 586.5849 587.4464 590.9546 593.0454 592.9186 595.1954
#>                440    440.5      441    441.5      442    442.5      443
#> mean - sd 172.4425 173.7155 172.5548 175.2936 175.3857 175.3063 173.6175
#> mean      383.4117 384.9124 384.9061 386.2123 386.0671 386.8118 387.1116
#> mean + sd 594.3809 596.1093 597.2574 597.1311 596.7484 598.3174 600.6056
#>              443.5      444    444.5      445    445.5      446    446.5
#> mean - sd 175.7587 174.4250 173.0780 175.0031 174.9369 174.9409 174.7203
#> mean      387.9868 388.2645 386.9496 387.7631 387.6842 388.9684 388.8592
#> mean + sd 600.2150 602.1040 600.8211 600.5230 600.4315 602.9959 602.9980
#>                447    447.5      448    448.5      449    449.5      450
#> mean - sd 173.9914 173.9701 174.4212 175.2769 174.4961 176.7235 176.1030
#> mean      387.6974 387.5131 387.8354 387.7904 388.0669 388.3706 388.3181
#> mean + sd 601.4035 601.0562 601.2497 600.3040 601.6376 600.0177 600.5332
#>              450.5      451    451.5      452    452.5      453    453.5
#> mean - sd 175.5562 175.3266 174.9784 174.1531 172.5142 174.0396 173.2631
#> mean      388.0719 386.9387 387.4204 387.2329 385.1973 385.5947 385.0308
#> mean + sd 600.5876 598.5509 599.8624 600.3128 597.8803 597.1499 596.7985
#>                454    454.5      455    455.5      456    456.5      457
#> mean - sd 173.3176 173.9120 174.6118 172.4585 171.6102 171.1048 171.5018
#> mean      384.9724 385.6444 383.5018 382.0557 380.9131 379.6323 379.4236
#> mean + sd 596.6273 597.3767 592.3918 591.6530 590.2161 588.1597 587.3454
#>              457.5      458    458.5      459    459.5      460    460.5
#> mean - sd 169.4725 169.7926 168.5365 167.4907 166.3179 164.5844 164.2734
#> mean      376.7526 376.1533 373.7147 371.8777 369.0225 367.3478 364.0053
#> mean + sd 584.0326 582.5139 578.8929 576.2647 571.7271 570.1112 563.7373
#>                461    461.5      462    462.5      463    463.5      464
#> mean - sd 163.5111 163.3332 162.0640 158.9616 158.3493 158.2073 157.6023
#> mean      363.2114 361.1244 358.5899 354.8971 352.0969 349.0689 347.7439
#> mean + sd 562.9118 558.9157 555.1159 550.8326 545.8446 539.9306 537.8854
#>              464.5      465    465.5      466    466.5      467    467.5
#> mean - sd 155.7317 153.6568 152.7663 150.6317 149.0639 147.7443 146.7840
#> mean      344.6271 340.4968 338.4603 335.9258 332.6344 329.3642 325.6729
#> mean + sd 533.5224 527.3368 524.1543 521.2200 516.2048 510.9840 504.5617
#>                468    468.5      469    469.5      470    470.5      471
#> mean - sd 146.0302 143.4413 141.4045 139.9748 139.9697 138.5334 135.9037
#> mean      322.0702 319.5687 314.7917 312.4208 310.4492 305.3859 302.4756
#> mean + sd 498.1101 495.6961 488.1790 484.8668 480.9288 472.2385 469.0476
#>              471.5      472    472.5      473    473.5      474    474.5
#> mean - sd 135.8345 134.0733 130.9217 129.4063 129.4063 127.7221 126.5801
#> mean      299.4622 296.7000 292.2196 288.3032 286.7105 283.4853 280.3511
#> mean + sd 463.0900 459.3267 453.5175 447.2001 444.0147 439.2485 434.1220
#>                475    475.5      476    476.5      477    477.5      478
#> mean - sd 125.3025 124.6049 122.3018 120.9717 119.6821 119.2714 117.4066
#> mean      276.9047 274.8581 271.2237 269.0067 266.5739 263.3924 260.4180
#> mean + sd 428.5070 425.1113 420.1455 417.0418 413.4656 407.5134 403.4294
#>              478.5     479    479.5      480    480.5      481    481.5
#> mean - sd 116.1092 115.763 113.7058 111.8390 112.2662 111.0700 109.8724
#> mean      258.1373 255.791 253.8093 249.5182 248.2199 245.3162 241.8370
#> mean + sd 400.1654 395.819 393.9127 387.1973 384.1735 379.5624 373.8016
#>                482    482.5      483    483.5      484    484.5      485
#> mean - sd 109.4847 108.0348 106.3207 103.6006 102.5350 103.1374 101.2827
#> mean      240.8851 238.1019 235.3928 232.0592 230.1664 227.5049 224.4877
#> mean + sd 372.2856 368.1690 364.4648 360.5178 357.7977 351.8724 347.6928
#>              485.5       486     486.5       487     487.5       488     488.5
#> mean - sd 100.2449  99.25656  97.61239  96.23855  95.26908  94.01085  92.50318
#> mean      221.6313 219.46467 215.94411 213.16344 210.14694 208.62042 205.86292
#> mean + sd 343.0178 339.67277 334.27583 330.08834 325.02481 323.22998 319.22265
#>                 489     489.5       490     490.5       491     491.5       492
#> mean - sd  92.30722  90.60566  88.71826  88.53754  86.75587  85.71093  83.83838
#> mean      203.66306 200.38500 198.28903 195.34281 192.30317 189.33922 186.51083
#> mean + sd 315.01889 310.16434 307.85980 302.14807 297.85047 292.96751 289.18329
#>               492.5       493     493.5       494     494.5       495
#> mean - sd  82.42095  80.70487  79.80546  78.42967  77.95554  76.75885
#> mean      183.83564 180.68786 178.78053 175.72864 173.40081 170.52147
#> mean + sd 285.25032 280.67086 277.75559 273.02761 268.84607 264.28409

mean_pm_sd(flu)
#> hyperSpec object
#>    3 spectra
#>    1 data columns
#>    181 data points / spectrum
#> wavelength: lambda/nm [numeric] 405.0 405.5 ... 495 
#> data:  (3 rows x 1 columns)
#>    1. spc: I[fl]/"a.u." [matrix, array181] 49.95778 113.90939 ... 264.2841 

plot(mean(faux_cell))

plot(quantile(faux_cell))

flu_quantiles <- quantile(flu)
rownames(flu_quantiles)
#> [1] "0"   "0.5" "1"  
flu_quantiles$..
#>     filename  c
#> 0       <NA> NA
#> 0.5     <NA> NA
#> 1       <NA> NA

flu_pretty_quantiles <- quantile(flu, names = "pretty")
rownames(flu_pretty_quantiles)
#> [1] "  0 %" " 50 %" "100 %"
flu_pretty_quantiles$..
#>       filename  c
#>   0 %     <NA> NA
#>  50 %     <NA> NA
#> 100 %     <NA> NA