Two-Way Repeated Measures ANOVA: Error in lm.fit()... 0 non-na cases (rstatix)

Question

I am trying to run a Two-Way Repeated Measures ANOVA using the R Statix package based on the description from here however I am running into the subject error with my dataset. The data consist of 10 replicate biomass measurements recorded at each of 8 Locations across 5 months (10 reps x 8 locations x 5 months = 400 observations).

head(biomass)

However, when I run this:

library(rstatix)

res.aov <- anova_test(
  data = biomass, dv = Biomass, wid = ID,
  within = c(Location, SampleMonth)
)

I get the error:

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases

I've looked at similar posts here and here that seem to indicate that the error is caused by NA in the lm.

Based on the first post above, I tried to run:

lm(Biomass~Location:SampleMonth, data =biomass)

and I noticed that one Location:SampleMonth value shows an NA coefficient (LocationMPE:SampleMonth10 ). However, if I remove all the data from this Location and try again, all that happens is that another Location:SampleMonth coefficient becomes NA. This seems to occur with whatever combination (even just 2) locations.

Am I doing something wrong?

Below is a dput() of my entire dataset:

biomass <- structure(list(ID = structure(1:400, .Label = c("1", "2", "3", 
    "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", 
    "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", 
    "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", 
    "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", 
    "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", 
    "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", 
    "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", 
    "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", 
    "93", "94", "95", "96", "97", "98", "99", "100", "101", "102", 
    "103", "104", "105", "106", "107", "108", "109", "110", "111", 
    "112", "113", "114", "115", "116", "117", "118", "119", "120", 
    "121", "122", "123", "124", "125", "126", "127", "128", "129", 
    "130", "131", "132", "133", "134", "135", "136", "137", "138", 
    "139", "140", "141", "142", "143", "144", "145", "146", "147", 
    "148", "149", "150", "151", "152", "153", "154", "155", "156", 
    "157", "158", "159", "160", "161", "162", "163", "164", "165", 
    "166", "167", "168", "169", "170", "171", "172", "173", "174", 
    "175", "176", "177", "178", "179", "180", "181", "182", "183", 
    "184", "185", "186", "187", "188", "189", "190", "191", "192", 
    "193", "194", "195", "196", "197", "198", "199", "200", "201", 
    "202", "203", "204", "205", "206", "207", "208", "209", "210", 
    "211", "212", "213", "214", "215", "216", "217", "218", "219", 
    "220", "221", "222", "223", "224", "225", "226", "227", "228", 
    "229", "230", "231", "232", "233", "234", "235", "236", "237", 
    "238", "239", "240", "241", "242", "243", "244", "245", "246", 
    "247", "248", "249", "250", "251", "252", "253", "254", "255", 
    "256", "257", "258", "259", "260", "261", "262", "263", "264", 
    "265", "266", "267", "268", "269", "270", "271", "272", "273", 
    "274", "275", "276", "277", "278", "279", "280", "281", "282", 
    "283", "284", "285", "286", "287", "288", "289", "290", "291", 
    "292", "293", "294", "295", "296", "297", "298", "299", "300", 
    "301", "302", "303", "304", "305", "306", "307", "308", "309", 
    "310", "311", "312", "313", "314", "315", "316", "317", "318", 
    "319", "320", "321", "322", "323", "324", "325", "326", "327", 
    "328", "329", "330", "331", "332", "333", "334", "335", "336", 
    "337", "338", "339", "340", "341", "342", "343", "344", "345", 
    "346", "347", "348", "349", "350", "351", "352", "353", "354", 
    "355", "356", "357", "358", "359", "360", "361", "362", "363", 
    "364", "365", "366", "367", "368", "369", "370", "371", "372", 
    "373", "374", "375", "376", "377", "378", "379", "380", "381", 
    "382", "383", "384", "385", "386", "387", "388", "389", "390", 
    "391", "392", "393", "394", "395", "396", "397", "398", "399", 
    "400"), class = "factor"), Location = structure(c(6L, 7L, 5L, 
    8L, 6L, 6L, 8L, 5L, 3L, 8L, 7L, 7L, 8L, 6L, 8L, 1L, 8L, 4L, 8L, 
    8L, 1L, 3L, 6L, 3L, 7L, 3L, 2L, 8L, 6L, 6L, 4L, 3L, 4L, 3L, 6L, 
    2L, 2L, 3L, 3L, 7L, 2L, 1L, 4L, 7L, 8L, 6L, 4L, 8L, 7L, 4L, 4L, 
    8L, 3L, 5L, 4L, 8L, 5L, 4L, 8L, 3L, 3L, 2L, 7L, 2L, 7L, 4L, 5L, 
    3L, 2L, 8L, 4L, 4L, 5L, 1L, 4L, 7L, 7L, 6L, 2L, 7L, 3L, 3L, 4L, 
    5L, 7L, 4L, 4L, 1L, 6L, 1L, 3L, 1L, 1L, 1L, 2L, 6L, 1L, 8L, 2L, 
    5L, 6L, 5L, 3L, 4L, 4L, 8L, 1L, 1L, 8L, 1L, 5L, 5L, 8L, 8L, 8L, 
    6L, 8L, 7L, 8L, 7L, 3L, 5L, 4L, 7L, 5L, 4L, 5L, 1L, 5L, 1L, 6L, 
    2L, 8L, 4L, 5L, 4L, 3L, 7L, 8L, 4L, 7L, 7L, 6L, 2L, 2L, 5L, 8L, 
    5L, 1L, 2L, 7L, 8L, 7L, 8L, 8L, 1L, 4L, 6L, 4L, 2L, 1L, 7L, 2L, 
    8L, 5L, 6L, 1L, 4L, 1L, 8L, 1L, 7L, 3L, 1L, 2L, 2L, 2L, 1L, 6L, 
    5L, 2L, 3L, 1L, 5L, 4L, 4L, 4L, 2L, 1L, 5L, 7L, 3L, 7L, 5L, 8L, 
    8L, 7L, 2L, 1L, 3L, 5L, 1L, 2L, 3L, 8L, 2L, 1L, 3L, 7L, 7L, 7L, 
    7L, 1L, 2L, 4L, 7L, 3L, 8L, 4L, 7L, 2L, 7L, 1L, 8L, 5L, 4L, 7L, 
    6L, 8L, 5L, 5L, 3L, 5L, 3L, 1L, 6L, 4L, 6L, 8L, 7L, 6L, 1L, 4L, 
    7L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 6L, 2L, 3L, 2L, 7L, 5L, 4L, 3L, 
    3L, 2L, 1L, 1L, 2L, 8L, 6L, 1L, 1L, 7L, 4L, 5L, 5L, 5L, 7L, 6L, 
    5L, 8L, 3L, 7L, 5L, 5L, 2L, 1L, 6L, 3L, 6L, 1L, 4L, 2L, 8L, 1L, 
    2L, 7L, 7L, 6L, 5L, 2L, 7L, 4L, 7L, 4L, 5L, 5L, 5L, 6L, 5L, 3L, 
    1L, 8L, 4L, 1L, 5L, 2L, 4L, 6L, 3L, 3L, 4L, 2L, 5L, 7L, 8L, 5L, 
    6L, 1L, 8L, 2L, 1L, 7L, 1L, 5L, 4L, 1L, 6L, 6L, 3L, 3L, 4L, 3L, 
    2L, 8L, 5L, 2L, 3L, 6L, 6L, 5L, 2L, 5L, 7L, 6L, 6L, 3L, 8L, 6L, 
    7L, 3L, 4L, 2L, 2L, 6L, 5L, 4L, 3L, 3L, 1L, 6L, 1L, 4L, 6L, 2L, 
    6L, 4L, 6L, 2L, 7L, 8L, 5L, 4L, 3L, 5L, 1L, 2L, 3L, 6L, 2L, 3L, 
    8L, 7L, 6L, 3L, 8L, 6L, 3L, 8L, 6L, 6L, 3L, 4L, 6L), .Label = c("BR", 
    "HBY", "MBB", "MCB", "MEB", "MEL", "MPD", "MPE"), class = "factor"), 
        SampleMonth = structure(c(1L, 1L, 5L, 3L, 2L, 1L, 1L, 4L, 
        2L, 4L, 4L, 2L, 4L, 1L, 5L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 3L, 5L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 
        1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 3L, 1L, 5L, 3L, 4L, 5L, 
        1L, 5L, 5L, 1L, 5L, 5L, 5L, 3L, 1L, 1L, 1L, 1L, 1L, 5L, 4L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 5L, 4L, 
        3L, 1L, 5L, 2L, 5L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 5L, 1L, 
        1L, 2L, 1L, 3L, 1L, 1L, 4L, 4L, 5L, 1L, 5L, 3L, 5L, 1L, 1L, 
        3L, 4L, 2L, 1L, 4L, 2L, 4L, 1L, 1L, 2L, 5L, 4L, 1L, 2L, 2L, 
        1L, 3L, 1L, 1L, 5L, 2L, 4L, 5L, 1L, 1L, 5L, 3L, 2L, 1L, 2L, 
        4L, 3L, 5L, 1L, 1L, 3L, 5L, 2L, 5L, 3L, 4L, 1L, 2L, 4L, 4L, 
        5L, 5L, 5L, 5L, 3L, 1L, 4L, 2L, 2L, 3L, 2L, 1L, 4L, 2L, 5L, 
        4L, 4L, 2L, 2L, 5L, 1L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 3L, 5L, 
        4L, 3L, 2L, 3L, 4L, 2L, 4L, 5L, 5L, 5L, 1L, 3L, 3L, 3L, 3L, 
        4L, 3L, 2L, 1L, 2L, 3L, 3L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 3L, 
        2L, 4L, 4L, 5L, 3L, 2L, 1L, 3L, 4L, 5L, 5L, 1L, 1L, 4L, 2L, 
        5L, 4L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 4L, 
        3L, 3L, 5L, 4L, 4L, 2L, 2L, 2L, 5L, 5L, 5L, 2L, 2L, 3L, 2L, 
        4L, 4L, 5L, 4L, 5L, 5L, 4L, 4L, 3L, 4L, 5L, 3L, 2L, 5L, 4L, 
        5L, 5L, 3L, 3L, 5L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 2L, 
        5L, 5L, 2L, 3L, 4L, 5L, 2L, 4L, 5L, 2L, 2L, 3L, 3L, 4L, 4L, 
        2L, 3L, 2L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 4L, 4L, 3L, 2L, 2L, 
        3L, 4L, 2L, 5L, 2L, 2L, 3L, 5L, 2L, 2L, 3L, 2L, 4L, 3L, 5L, 
        2L, 2L, 4L, 3L, 5L, 5L, 4L, 3L, 3L, 2L, 4L, 3L, 5L, 4L, 3L, 
        2L, 2L, 3L, 2L, 2L, 3L, 4L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, 4L, 
        4L, 4L, 5L, 4L, 4L, 4L, 3L, 5L, 3L, 5L, 4L, 5L, 4L, 5L, 4L, 
        3L, 4L, 4L, 3L, 3L, 3L, 5L, 5L, 3L, 5L, 2L, 4L, 3L, 4L, 4L, 
        3L, 4L), .Label = c("06", "07", "08", "09", "10"), class = "factor"), 
        Biomass = c(0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 9.99, 19.99, 36.99, 39.99, 50, 50, 51, 60, 
        60, 69.99, 80, 81, 84, 86.99, 89.99, 100, 100, 100, 100.99, 
        117, 119.99, 119.99, 119.99, 125, 129.99, 130, 139.99, 140, 
        150, 150, 152.99, 159.99, 169.99, 170, 179.99, 184.99, 189.99, 
        189.99, 192.99, 199.99, 199.99, 210, 219.99, 219.99, 230, 
        230, 235, 248, 250, 269.99, 269.99, 269.99, 276, 279.99, 
        280, 282, 289.99, 289.99, 300, 300, 300, 304, 310, 310, 319.99, 
        319.99, 326, 329.99, 330, 335.99, 339.99, 339.99, 340, 350, 
        350, 360, 362, 369.99, 369.99, 369.99, 375, 379.99, 380, 
        383, 384, 390, 390, 399.99, 400, 419.99, 420, 423.99, 429.99, 
        430, 430, 430, 435.99, 440, 450, 459.99, 459.99, 465, 469.99, 
        479.99, 480, 480, 482, 484, 490, 490, 499.99, 500, 509.99, 
        519.99, 529.99, 530, 544.99, 556.99, 559.99, 560, 569.99, 
        569.99, 573, 579, 590, 590, 597, 609.99, 609.99, 609.99, 
        610, 610, 630, 630, 640, 650, 659.99, 659.99, 659.99, 667, 
        670, 680, 680, 680, 690, 700, 702, 709.99, 709.99, 709.99, 
        719.99, 719.99, 729.99, 730, 739.99, 740, 744, 745.99, 749.99, 
        750, 752, 756.99, 758, 759.99, 759.99, 760, 769.99, 769.99, 
        769.99, 780, 789.99, 789.99, 789.99, 790, 794, 809.99, 810, 
        810, 810, 819.99, 829.99, 829.99, 830, 840, 840, 850, 869, 
        870, 870, 880, 880, 890, 890, 890, 898.99, 900, 900, 909.99, 
        910, 919.99, 920, 930, 939.99, 940, 949.99, 950, 950, 959.99, 
        960, 970, 979.99, 979.99, 980, 980.99, 989.99, 990, 997.99, 
        1000, 1019.99, 1020, 1020, 1029.99, 1030, 1030, 1040, 1049.99, 
        1050, 1050, 1059.99, 1069.99, 1080, 1100, 1100, 1120, 1130, 
        1130.99, 1139.99, 1153, 1158, 1170, 1170, 1177, 1180, 1180, 
        1180, 1180, 1189.99, 1190, 1200, 1219.99, 1220, 1240, 1259.99, 
        1259.99, 1279, 1279.99, 1280.99, 1300, 1309.99, 1310, 1316, 
        1320, 1330, 1339.99, 1339.99, 1340, 1345, 1359.99, 1360, 
        1369, 1370, 1379.99, 1400, 1410, 1420, 1429.99, 1430.99, 
        1440, 1440, 1449, 1450, 1459.99, 1468.99, 1470, 1474, 1479.99, 
        1490, 1490, 1490, 1499.99, 1510, 1520, 1529.99, 1540, 1560, 
        1560, 1570, 1570, 1579.99, 1580, 1580, 1590, 1590, 1594, 
        1620, 1630, 1630, 1639.99, 1650, 1650, 1680, 1715, 1719.99, 
        1720, 1729.99, 1733, 1770, 1779.99, 1808, 1819.99, 1837, 
        1840, 1860, 1900, 1909.99, 1910, 1929.99, 1940, 1954, 2020, 
        2040, 2050, 2069.99, 2090, 2100, 2102.99, 2159.99, 2179.99, 
        2249.99, 2279.99, 2289.99, 2290, 2334, 2366, 2380, 2399.99, 
        2400, 2420, 2450, 2500, 2520, 2550, 2589.99, 2589.99, 2599.99, 
        2600, 2620, 2660, 2713, 2739.99, 2830, 2920, 2959.99, 3010, 
        3029.99, 3129.99, 3150, 3190, 3250, 3310, 3440, 3490, 3650, 
        3740, 3809.99, 3850, 3920, 5799.99, 5898.72, 6680, 6970)), class = c("spec_tbl_df", 
    "tbl_df", "tbl", "data.frame"), row.names = c(NA, -400L))

Hi.. what are you trying to test for? Differences between location or differences between month? — StupidWolf, Feb 19 '20 at 20:10
You get the error because you specify ID wrongly. So you have 10 individuals at each site, measured across 5 months. Are these the same individuals? If they are you need to specify the IDs for these individuals — StupidWolf, Feb 19 '20 at 20:31
Hi StupidWolf, I'm interested in testing both differences in location and differences by Month, and the interaction (Location x Month). Though I'm no statistician so maybe this is not the right way. Re the ID: they aren't the same 10 individuals, it's just 10 random samples were taken at each Location at each month and were measured for biomass (i.e. they are essentially replicates). — DarwinsBeard, Feb 19 '20 at 22:10
Hi @DarwinsBeard, ok what you need is a One-way or Two-way ANOVA, because with repeated measures, you want to partition the variation coming from individual effect, for example different patients or different samples have a different starting biomass.. — StupidWolf, Feb 19 '20 at 22:35

score 2 · Accepted Answer · answered Feb 19 '20 at 22:42

First of all, for the lm, you get NAs because you did not specify the main term:

fit = lm(Biomass ~ SampleMonth*Location,data=biomass)
table(is.na(coefficients(fit)))

FALSE 
   40

When you do a two way repeated anova, you have an experiment or study that is a repeated measure, something like many patients undergoing a treatment over a period of time etc. In your case, if there is no "repeated measure", or common individuals that underwent the same treatment, then you can use the conventional anova.

If you are interested in effects in time period and sites:

anova_test(Biomass ~ SampleMonth+Location,data=biomass)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)

       Effect DFn DFd      F        p p<.05   ges
1 SampleMonth   4 388 18.712 4.45e-14     * 0.162
2    Location   7 388  2.969 5.00e-03     * 0.051

The above assumes a common mean (or effect) for each month or each location. You can have a more complicated model which test whether there are variations in the Month effect across locations:

anova_test(Biomass ~ SampleMonth*Location,data=biomass)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)

                Effect DFn DFd      F        p p<.05   ges
1          SampleMonth   4 360 19.507 1.53e-14     * 0.178
2             Location   7 360  3.095 4.00e-03     * 0.057
3 SampleMonth:Location  28 360  1.588 3.20e-02     * 0.110

Here is just the non-formula syntax for the Biomass~SampleMonth*Location model: ```anova_test(dv = Biomass, between = c(Location, SampleMonth), data = biomass)``` — xilliam, Feb 19 '20 at 22:57
hi @DarwinsBeard, glad it works out for you. You can also use the other formulation provided in the comment above by xilliam :) — StupidWolf, Feb 21 '20 at 07:57

Two-Way Repeated Measures ANOVA: Error in lm.fit()... 0 non-na cases (rstatix)

1 Answers1