This function performs analysis of variance (ANOVA) based on the anovakun version 4.8.9,
originally developed by Prof. Ryuta Iseki (Taisho University, Japan).
Usage
anovakun_(
dataset,
design,
...,
long = FALSE,
type2 = FALSE,
nopost = FALSE,
tech = FALSE,
data.frame = FALSE,
copy = FALSE,
holm = FALSE,
hc = FALSE,
s2r = FALSE,
s2d = FALSE,
fs1 = FALSE,
fs2r = FALSE,
fs2d = FALSE,
welch = FALSE,
criteria = FALSE,
lb = FALSE,
gg = FALSE,
hf = FALSE,
cm = FALSE,
auto = FALSE,
mau = FALSE,
har = FALSE,
iga = FALSE,
ciga = FALSE,
eta = FALSE,
peta = FALSE,
geta = NA,
eps = FALSE,
peps = FALSE,
geps = NA,
omega = FALSE,
omegana = FALSE,
pomega = FALSE,
gomega = NA,
gomegana = NA,
prep = FALSE,
nesci = FALSE,
besci = FALSE,
cilmd = FALSE,
cilm = FALSE,
cind = FALSE,
cin = FALSE,
ciml = FALSE,
cipaird = FALSE,
cipair = FALSE,
bgraph = c(NA, NA)
)Arguments
- dataset
A data frame containing the input data.
- design
A character string specifying the experimental design (e.g., "As", "ABs", "sA", "sAB", "AsB", etc.).
- ...
Numbers of levels of each factor.
- long
Logical. Whether the data is in long format.
- type2
Logical. Whether to use Type II sums of squares (default is FALSE for Type III).
- nopost
Logical. If TRUE, skips post hoc tests.
- tech
Logical. If TRUE, returns raw lists of results instead of printing.
- data.frame
Logical. If TRUE, returns the reformatted data frame.
- copy
Logical. If TRUE, copies the output to the clipboard.
- holm, ..., bgraph
Additional flags controlling specific analyses (see documentation).
Details
In this implementation, the code of anovakun version 4.8.9 has been included as-is with no modification
to its computational logic or output. However, for the purpose of maintainability and modularity,
internal helper functions have been separated into a different file.
Additionally, all original Japanese-language comments have been removed from the source code to maintain stylistic consistency with this package and to simplify documentation.
Redistribution and minor modifications of the original function are permitted under the terms indicated by the original author, provided that such modifications are clearly stated. This implementation complies with that policy.
For methodological details and rationale, please refer to the original documentation of anovakun version 4.8.9.
See also
The original documentation for anovakun by Prof. Ryuta Iseki is available at:
https://riseki.cloudfree.jp/?ANOVA%E5%90%9B
Examples
data_snakemr %>%
anovakun_("sABC", 2, 2, 5, long = TRUE)
#>
#> [ sABC-Type Design ]
#>
#> This output was generated by anovakun 4.8.9 under R version 4.3.2.
#> It was executed on Fri Jan 16 22:14:16 2026.
#>
#>
#> << DESCRIPTIVE STATISTICS >>
#>
#> ---------------------------------------------
#> shape face angle n Mean S.D.
#> ---------------------------------------------
#> human absent 0 24 0.8909 0.2040
#> human absent 40 24 1.0862 0.2460
#> human absent 80 24 1.2410 0.2888
#> human absent 120 24 1.4275 0.3710
#> human absent 160 24 1.7042 0.4039
#> human present 0 24 0.8332 0.1365
#> human present 40 24 0.9645 0.1900
#> human present 80 24 1.1502 0.2581
#> human present 120 24 1.3415 0.2987
#> human present 160 24 1.5974 0.3769
#>
#> snake absent 0 24 0.9350 0.2213
#> snake absent 40 24 1.2910 0.3269
#> snake absent 80 24 1.4150 0.3240
#> snake absent 120 24 1.6560 0.3528
#> snake absent 160 24 1.8818 0.4148
#> snake present 0 24 0.8433 0.1953
#> snake present 40 24 1.0460 0.2530
#> snake present 80 24 1.2166 0.3247
#> snake present 120 24 1.4268 0.3808
#> snake present 160 24 1.7307 0.4217
#> ---------------------------------------------
#>
#>
#> << SPHERICITY INDICES >>
#>
#> == Mendoza's Multisample Sphericity Test and Epsilons ==
#>
#> --------------------------------------------------------------------------------------
#> Effect Lambda approx.Chi df p LB GG HF CM
#> --------------------------------------------------------------------------------------
#> Global 0.0000 427.6417 189 0.0000 *** 0.0526 0.2017 0.2470 0.2416
#> shape 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> angle 0.0000 67.1279 9 0.0000 *** 0.2500 0.3866 0.4092 0.4003
#> shape x face 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> shape x angle 0.0001 18.2129 9 0.0333 * 0.2500 0.7541 0.8806 0.8615
#> face x angle 0.0294 6.5699 9 0.6828 ns 0.2500 0.8558 1.0236 1.0014
#> shape x face x angle 0.1462 3.5802 9 0.9370 ns 0.2500 0.9195 1.1163 1.0921
#> --------------------------------------------------------------------------------------
#> LB = lower.bound, GG = Greenhouse-Geisser
#> HF = Huynh-Feldt-Lecoutre, CM = Chi-Muller
#>
#>
#> << ANOVA TABLE >>
#>
#> --------------------------------------------------------------------------
#> Source SS df MS F-ratio p-value
#> --------------------------------------------------------------------------
#> s 34.5516 23 1.5022
#> --------------------------------------------------------------------------
#> shape 1.7442 1 1.7442 37.3117 0.0000 ***
#> s x shape 1.0752 23 0.0467
#> --------------------------------------------------------------------------
#> face 2.2803 1 2.2803 98.2323 0.0000 ***
#> s x face 0.5339 23 0.0232
#> --------------------------------------------------------------------------
#> angle 41.4920 4 10.3730 201.1569 0.0000 ***
#> s x angle 4.7441 92 0.0516
#> --------------------------------------------------------------------------
#> shape x face 0.2457 1 0.2457 9.7189 0.0048 **
#> s x shape x face 0.5816 23 0.0253
#> --------------------------------------------------------------------------
#> shape x angle 0.2827 4 0.0707 6.9114 0.0001 ***
#> s x shape x angle 0.9408 92 0.0102
#> --------------------------------------------------------------------------
#> face x angle 0.1578 4 0.0394 3.8702 0.0060 **
#> s x face x angle 0.9376 92 0.0102
#> --------------------------------------------------------------------------
#> shape x face x angle 0.0567 4 0.0142 1.3736 0.2492 ns
#> s x shape x face x angle 0.9488 92 0.0103
#> --------------------------------------------------------------------------
#> Total 90.5731 479 0.1891
#> +p < .10, *p < .05, **p < .01, ***p < .001
#>
#>
#> << POST ANALYSES >>
#>
#> < MULTIPLE COMPARISON for "angle" >
#>
#> == Shaffer's Modified Sequentially Rejective Bonferroni Procedure ==
#> == The factor < angle > is analysed as dependent means. ==
#> == Alpha level is 0.05. ==
#>
#> -----------------------------
#> angle n Mean S.D.
#> -----------------------------
#> 0 96 0.8756 0.1932
#> 40 96 1.0969 0.2818
#> 80 96 1.2557 0.3113
#> 120 96 1.4630 0.3660
#> 160 96 1.7285 0.4111
#> -----------------------------
#>
#> --------------------------------------------------------------
#> Pair Diff t-value df p adj.p
#> --------------------------------------------------------------
#> 80-160 -0.4728 17.7531 23 0.0000 0.0000 80 < 160 *
#> 40-160 -0.6316 16.3023 23 0.0000 0.0000 40 < 160 *
#> 0-160 -0.8529 15.6908 23 0.0000 0.0000 0 < 160 *
#> 40-120 -0.3660 14.6521 23 0.0000 0.0000 40 < 120 *
#> 0-120 -0.5873 13.7817 23 0.0000 0.0000 0 < 120 *
#> 120-160 -0.2656 11.4339 23 0.0000 0.0000 120 < 160 *
#> 80-120 -0.2072 10.7847 23 0.0000 0.0000 80 < 120 *
#> 0-80 -0.3801 10.6498 23 0.0000 0.0000 0 < 80 *
#> 0-40 -0.2213 8.8435 23 0.0000 0.0000 0 < 40 *
#> 40-80 -0.1588 8.7577 23 0.0000 0.0000 40 < 80 *
#> --------------------------------------------------------------
#>
#>
#> < SIMPLE EFFECTS for "shape x face" INTERACTION >
#>
#> --------------------------------------
#> shape face n Mean S.D.
#> --------------------------------------
#> human absent 120 1.2700 0.4159
#> human present 120 1.1774 0.3773
#> snake absent 120 1.4358 0.4610
#> snake present 120 1.2527 0.4443
#> --------------------------------------
#>
#> --------------------------------------------------------------------------------
#> Effect Lambda approx.Chi df p LB GG HF CM
#> --------------------------------------------------------------------------------
#> shape at absent 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> shape at present 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face at human 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face at snake 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> --------------------------------------------------------------------------------
#> LB = lower.bound, GG = Greenhouse-Geisser
#> HF = Huynh-Feldt-Lecoutre, CM = Chi-Muller
#>
#> --------------------------------------------------------------------
#> Source SS df MS F-ratio p-value
#> --------------------------------------------------------------------
#> shape at absent 1.6497 1 1.6497 67.7233 0.0000 ***
#> s x shape at absent 0.5603 23 0.0244
#> --------------------------------------------------------------------
#> shape at present 0.3403 1 0.3403 7.1376 0.0136 *
#> s x shape at present 1.0965 23 0.0477
#> --------------------------------------------------------------------
#> face at human 0.5144 1 0.5144 17.0083 0.0004 ***
#> s x face at human 0.6957 23 0.0302
#> --------------------------------------------------------------------
#> face at snake 2.0116 1 2.0116 110.2117 0.0000 ***
#> s x face at snake 0.4198 23 0.0183
#> --------------------------------------------------------------------
#> +p < .10, *p < .05, **p < .01, ***p < .001
#>
#> < SIMPLE EFFECTS for "shape x angle" INTERACTION >
#>
#> ------------------------------------
#> shape angle n Mean S.D.
#> ------------------------------------
#> human 0 48 0.8621 0.1741
#> human 40 48 1.0254 0.2260
#> human 80 48 1.1956 0.2748
#> human 120 48 1.3845 0.3360
#> human 160 48 1.6508 0.3902
#> snake 0 48 0.8892 0.2116
#> snake 40 48 1.1685 0.3146
#> snake 80 48 1.3158 0.3362
#> snake 120 48 1.5414 0.3812
#> snake 160 48 1.8062 0.4208
#> ------------------------------------
#>
#> ---------------------------------------------------------------------------------
#> Effect Lambda approx.Chi df p LB GG HF CM
#> ---------------------------------------------------------------------------------
#> shape at 0 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> shape at 40 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> shape at 80 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> shape at 120 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> shape at 160 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> angle at human 0.0000 57.2646 9 0.0000 *** 0.2500 0.4415 0.4755 0.4652
#> angle at snake 0.0000 54.4022 9 0.0000 *** 0.2500 0.4211 0.4506 0.4409
#> ---------------------------------------------------------------------------------
#> LB = lower.bound, GG = Greenhouse-Geisser
#> HF = Huynh-Feldt-Lecoutre, CM = Chi-Muller
#>
#> -------------------------------------------------------------------
#> Source SS df MS F-ratio p-value
#> -------------------------------------------------------------------
#> shape at 0 0.0177 1 0.0177 2.9972 0.0968 +
#> s x shape at 0 0.1355 23 0.0059
#> -------------------------------------------------------------------
#> shape at 40 0.4917 1 0.4917 35.0968 0.0000 ***
#> s x shape at 40 0.3222 23 0.0140
#> -------------------------------------------------------------------
#> shape at 80 0.3467 1 0.3467 24.8161 0.0000 ***
#> s x shape at 80 0.3213 23 0.0140
#> -------------------------------------------------------------------
#> shape at 120 0.5911 1 0.5911 27.1479 0.0000 ***
#> s x shape at 120 0.5007 23 0.0218
#> -------------------------------------------------------------------
#> shape at 160 0.5798 1 0.5798 18.1126 0.0003 ***
#> s x shape at 160 0.7362 23 0.0320
#> -------------------------------------------------------------------
#> angle at human 18.2016 4 4.5504 155.5901 0.0000 ***
#> s x angle at human 2.6906 92 0.0292
#> -------------------------------------------------------------------
#> angle at snake 23.5732 4 5.8933 181.0704 0.0000 ***
#> s x angle at snake 2.9943 92 0.0325
#> -------------------------------------------------------------------
#> +p < .10, *p < .05, **p < .01, ***p < .001
#>
#>
#> < MULTIPLE COMPARISON for "angle at human" >
#>
#> == Shaffer's Modified Sequentially Rejective Bonferroni Procedure ==
#> == The factor < angle at human > is analysed as dependent means. ==
#> == Alpha level is 0.05. ==
#>
#> --------------------------------------------------------------
#> Pair Diff t-value df p adj.p
#> --------------------------------------------------------------
#> 0-160 -0.7888 14.4987 23 0.0000 0.0000 0 < 160 *
#> 80-160 -0.4552 13.9691 23 0.0000 0.0000 80 < 160 *
#> 40-160 -0.6255 13.6709 23 0.0000 0.0000 40 < 160 *
#> 0-120 -0.5224 12.3736 23 0.0000 0.0000 0 < 120 *
#> 40-120 -0.3591 11.2590 23 0.0000 0.0000 40 < 120 *
#> 0-80 -0.3336 10.5190 23 0.0000 0.0000 0 < 80 *
#> 80-120 -0.1889 9.0888 23 0.0000 0.0000 80 < 120 *
#> 40-80 -0.1703 8.8257 23 0.0000 0.0000 40 < 80 *
#> 120-160 -0.2663 8.4701 23 0.0000 0.0000 120 < 160 *
#> 0-40 -0.1633 7.5100 23 0.0000 0.0000 0 < 40 *
#> --------------------------------------------------------------
#>
#>
#> < MULTIPLE COMPARISON for "angle at snake" >
#>
#> == Shaffer's Modified Sequentially Rejective Bonferroni Procedure ==
#> == The factor < angle at snake > is analysed as dependent means. ==
#> == Alpha level is 0.05. ==
#>
#> --------------------------------------------------------------
#> Pair Diff t-value df p adj.p
#> --------------------------------------------------------------
#> 80-160 -0.4904 17.1014 23 0.0000 0.0000 80 < 160 *
#> 40-160 -0.6377 16.7296 23 0.0000 0.0000 40 < 160 *
#> 0-160 -0.9171 15.3224 23 0.0000 0.0000 0 < 160 *
#> 40-120 -0.3729 14.9307 23 0.0000 0.0000 40 < 120 *
#> 0-120 -0.6522 13.0827 23 0.0000 0.0000 0 < 120 *
#> 120-160 -0.2648 10.5039 23 0.0000 0.0000 120 < 160 *
#> 0-80 -0.4266 9.8567 23 0.0000 0.0000 0 < 80 *
#> 80-120 -0.2256 9.7885 23 0.0000 0.0000 80 < 120 *
#> 0-40 -0.2793 8.4577 23 0.0000 0.0000 0 < 40 *
#> 40-80 -0.1473 6.8479 23 0.0000 0.0000 40 < 80 *
#> --------------------------------------------------------------
#>
#> < SIMPLE EFFECTS for "face x angle" INTERACTION >
#>
#> --------------------------------------
#> face angle n Mean S.D.
#> --------------------------------------
#> absent 0 48 0.9130 0.2117
#> absent 40 48 1.1886 0.3043
#> absent 80 48 1.3280 0.3161
#> absent 120 48 1.5418 0.3763
#> absent 160 48 1.7930 0.4148
#> present 0 48 0.8383 0.1667
#> present 40 48 1.0053 0.2252
#> present 80 48 1.1834 0.2921
#> present 120 48 1.3841 0.3413
#> present 160 48 1.6640 0.4013
#> --------------------------------------
#>
#> -----------------------------------------------------------------------------------
#> Effect Lambda approx.Chi df p LB GG HF CM
#> -----------------------------------------------------------------------------------
#> face at 0 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face at 40 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face at 80 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face at 120 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> face at 160 1.0000 -0.0000 0 1.0000 1.0000 1.0000 1.0000
#> angle at absent 0.0000 34.2839 9 0.0001 *** 0.2500 0.5152 0.5666 0.5543
#> angle at present 0.0000 61.8081 9 0.0000 *** 0.2500 0.4009 0.4263 0.4170
#> -----------------------------------------------------------------------------------
#> LB = lower.bound, GG = Greenhouse-Geisser
#> HF = Huynh-Feldt-Lecoutre, CM = Chi-Muller
#>
#> ---------------------------------------------------------------------
#> Source SS df MS F-ratio p-value
#> ---------------------------------------------------------------------
#> face at 0 0.1339 1 0.1339 25.2253 0.0000 ***
#> s x face at 0 0.1221 23 0.0053
#> ---------------------------------------------------------------------
#> face at 40 0.8069 1 0.8069 44.6931 0.0000 ***
#> s x face at 40 0.4152 23 0.0181
#> ---------------------------------------------------------------------
#> face at 80 0.5017 1 0.5017 41.7537 0.0000 ***
#> s x face at 80 0.2764 23 0.0120
#> ---------------------------------------------------------------------
#> face at 120 0.5963 1 0.5963 39.8364 0.0000 ***
#> s x face at 120 0.3443 23 0.0150
#> ---------------------------------------------------------------------
#> face at 160 0.3993 1 0.3993 29.2877 0.0000 ***
#> s x face at 160 0.3135 23 0.0136
#> ---------------------------------------------------------------------
#> angle at absent 21.6257 4 5.4064 171.1057 0.0000 ***
#> s x angle at absent 2.9069 92 0.0316
#> ---------------------------------------------------------------------
#> angle at present 20.0241 4 5.0060 165.9741 0.0000 ***
#> s x angle at present 2.7749 92 0.0302
#> ---------------------------------------------------------------------
#> +p < .10, *p < .05, **p < .01, ***p < .001
#>
#>
#> < MULTIPLE COMPARISON for "angle at absent" >
#>
#> == Shaffer's Modified Sequentially Rejective Bonferroni Procedure ==
#> == The factor < angle at absent > is analysed as dependent means. ==
#> == Alpha level is 0.05. ==
#>
#> --------------------------------------------------------------
#> Pair Diff t-value df p adj.p
#> --------------------------------------------------------------
#> 40-160 -0.6044 16.2689 23 0.0000 0.0000 40 < 160 *
#> 0-160 -0.8801 15.4199 23 0.0000 0.0000 0 < 160 *
#> 80-160 -0.4650 14.5773 23 0.0000 0.0000 80 < 160 *
#> 0-120 -0.6288 13.9024 23 0.0000 0.0000 0 < 120 *
#> 40-120 -0.3532 12.9035 23 0.0000 0.0000 40 < 120 *
#> 0-80 -0.4150 10.4741 23 0.0000 0.0000 0 < 80 *
#> 120-160 -0.2513 9.4778 23 0.0000 0.0000 120 < 160 *
#> 80-120 -0.2138 8.3919 23 0.0000 0.0000 80 < 120 *
#> 0-40 -0.2756 7.4953 23 0.0000 0.0000 0 < 40 *
#> 40-80 -0.1394 6.6371 23 0.0000 0.0000 40 < 80 *
#> --------------------------------------------------------------
#>
#>
#> < MULTIPLE COMPARISON for "angle at present" >
#>
#> == Shaffer's Modified Sequentially Rejective Bonferroni Procedure ==
#> == The factor < angle at present > is analysed as dependent means. ==
#> == Alpha level is 0.05. ==
#>
#> --------------------------------------------------------------
#> Pair Diff t-value df p adj.p
#> --------------------------------------------------------------
#> 80-160 -0.4806 17.8211 23 0.0000 0.0000 80 < 160 *
#> 0-160 -0.8258 14.9215 23 0.0000 0.0000 0 < 160 *
#> 40-160 -0.6588 14.7136 23 0.0000 0.0000 40 < 160 *
#> 0-120 -0.5459 12.1325 23 0.0000 0.0000 0 < 120 *
#> 40-120 -0.3789 11.0116 23 0.0000 0.0000 40 < 120 *
#> 120-160 -0.2799 9.8885 23 0.0000 0.0000 120 < 160 *
#> 0-80 -0.3452 9.6747 23 0.0000 0.0000 0 < 80 *
#> 80-120 -0.2007 9.6236 23 0.0000 0.0000 80 < 120 *
#> 0-40 -0.1670 8.7929 23 0.0000 0.0000 0 < 40 *
#> 40-80 -0.1782 6.7604 23 0.0000 0.0000 40 < 80 *
#> --------------------------------------------------------------
#>
#> output is over --------------------///
#>