Help with Repeated Measure ANOVA

silkysullivan5 · May 18, 2024, 2:52am

Please help! I am trying to run the following code:

tmp <- OVF %>%
group_by(Year) %>%
rstatix::anova_test(dv = K, wid = ID, within = Treatment) %>%
get_anova_table() %>%
adjust_pvalue(method = "bonferroni")

But I get the error below. There are no NAs in the selected data, but there are NAs in other columns, could that be the issue?

Error in mutate():
In argument: data = map(.data$data, .f, ...).
Caused by error in map():
In index: 1.
Caused by error in lm.fit():
! 0 (non-NA) cases
Run rlang::last_trace() to see where the error occurred.

vedoa · May 19, 2024, 6:28am

Hi @silkysullivan5 ,

can you share what OVF is - or point to the resource holding the data so others can replicate your code and the error.

silkysullivan5 · May 20, 2024, 11:10am

sorry - what is the best way to do that?

vedoa · May 20, 2024, 11:24am

Run

dput(OVF)

copy the output to your post.

silkysullivan5 · May 20, 2024, 11:31am

structure(list(ID = 55:108, Site = c("OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF"), X = c("OVFA", "OVFA", "OVFA",
"OVFA", "OVFA", "OVFA", "OVFA", "OVFA", "OVFA", "OVFD", "OVFD",
"OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFE",
"OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE",
"OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF",
"OVFF", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG",
"OVFG", "OVFG", "OVFH", "OVFH", "OVFH", "OVFH", "OVFH", "OVFH",
"OVFH", "OVFH", "OVFH"), Treatment = c("Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Runnel", "Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel"), Zone = c("Center",
"Center", "Center", "Down", "Down", "Down", "Up", "Up", "Up",
"Center", "Center", "Center", "Down", "Down", "Down", "Up", "Up",
"Up", "Center", "Center", "Center", "Down", "Down", "Down", "Up",
"Up", "Up", "Center", "Center", "Center", "Down", "Down", "Down",
"Up", "Up", "Up", "Center", "Center", "Center", "Down", "Down",
"Down", "Up", "Up", "Up", "Center", "Center", "Center", "Down",
"Down", "Down", "Up", "Up", "Up"), Meter = c(NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), Redox = c(-54.1, -119.8, -130, -44.6, -114.3, -148.9,
38, -41.2, -17.8, -132.9, -228.1, -133.7, -89.8, -139.1, -124,
-18.8, -104.6, -68.4, -193.6, -203, -215.5, -108.3, -206.6, -182.6,
-8.9, -117.8, -91.5, -239.1, -212.2, -204, -167.3, -130.6, -152.3,
-9.3, -90.8, -88.5, -267.5, -247.3, -163.6, -155.7, -124.4, -120,
-65.33, -94.9, -113.3, -238.6, -180.5, -150.6, -179.3, -104.5,
-164.8, -45.2, -78.8, -87.7), Redox.s.e. = c(31.1, 29.7, 51.2,
40.2, 22.8, 40.8, 37.1, 38.8, 64.9, 45.4, 23.7, 33.4, 20.4, 25.3,
24.6, 29.5, 22.8, 32.2, 22.3, 13.9, 11.9, 31.3, 15.7, 4.7, 31.2,
24.4, 22.5, 15.1, 31.6, 16, 46.2, 24.9, 21.8, 44.9, 21.2, 34,
14.1, 9.2, 15.5, 11.3, 14.1, 15.9, 45.7, 29.1, 21.6, 29.8, 45.5,
16.5, 54.9, 38.5, 26.6, 69.4, 39.1, 28.5), Moisture = c(86.7,
86.3, 87.1, 86.5, 87.5, 89, 86.4, 88.5, 87.6, 86.6, 86.4, 86.2,
83.1, 83.9, 81.2, 82.6, 83.5, 83.4, 84.5, 85.6, 82.4, 82.9, 85.1,
84.9, 83.9, 86.5, 84.9, 83.2, 87.2, 84.8, 80.4, 83, 83.2, 83.5,
84.8, 85, 85.9, 82.5, 83, 84.8, 77.8, 81.5, 82.9, 85.8, 86.1,
84.1, 83.1, 81.4, 82.4, 80.1, 81.9, 82.9, 86, 83.4), Moisture.s.e. = c(0.82,
0.88, 0.32, 0.93, 0.64, 0.53, 1.3, 0.94, 0.63, 1.3, 0.21, 1.2,
4.5, 1.4, 1.4, 1.3, 0.5, 1.9, 0.65, 0.7, 1.8, 1, 0.69, 1.1, 1.3,
0.4, 1.4, 1.2, 1.2, 2.1, 2.4, 1.5, 2.1, 2.7, 0.53, 1.5, 0.67,
2, 1, NA, 1.1, 1, 1.3, 0.43, 0.58, 1.1, 0.48, 1.5, 2.2, 1.1,
1.6, 0.52, 0.51, 1.9), OM = c(NA, 62.5, 45.1, NA, 68, 50.2, NA,
67.4, 47.7, NA, 57, 47.6, NA, 68.2, 52.7, NA, 43.1, 49.7, NA,
50.5, 54.6, NA, 46.9, 53, NA, 64.5, 58.2, NA, 29.5, 48.9, NA,
48.6, 51, NA, 66.8, 45.1, NA, 21, 55.3, NA, 39, 54.2, NA, 62.6,
43.9, NA, 39.6, 54.1, NA, 33.9, 48.5, NA, 61.4, 49.7), OM.s.e. = c(NA,
1.3, 5.7, NA, 2.3, 6.4, NA, 6.5, 12.5, NA, 5.3, 1.2, NA, 3.3,
4.4, NA, 2.7, 3.7, NA, 3, 5.8, NA, 4, 1.8, NA, 2.4, 9.3, NA,
1.5, 2.8, NA, 3.7, 5.3, NA, 2.8, 8.1, NA, 7, 4.6, NA, 6.9, 4.8,
NA, 17.1, 6.3, NA, 0.7, 7.1, NA, 3.9, 3.9, NA, 10.3, 3.7), Salinity = c(40.8,
25, 20.4, 34, 24.2, 21.7, 39.5, 16.5, 17, 40.6, 26.2, 26, 43.5,
26.3, 23, 34.5, 27.3, 25.7, 45.5, 18.7, 18.5, 35, 23.8, 18.4,
33.5, 21.7, 19.6, 31.5, 57, 26.6, 38, 26.8, 22.7, 34, 22.7, 23.3,
37.2, 40.4, 23.7, NA, 29, 24.3, 26.6, 15, 20, 33.7, 24, 23.2,
22, 29.5, 21.3, 33, 14, 25.7), Salinity.s.e. = c(5.8, 1.3, 2.4,
3.8, 1.2, 0.94, 5.5, 4.6, 7, 4.4, 1.1, 1.3, 8.5, 2.3, 4.3, 0.5,
1, 2.2, 1, 3.3, 3.4, NA, 2.4, 4.1, 0.5, 3, 2.7, 1, 1.6, 1, 2,
2, 2.7, NA, 1.1, 1.4, 2.8, 2.6, 1.6, NA, 0, 2.6, 2.1, NA, 6,
0.9, 4.9, 3.1, NA, 0.5, 4, NA, 3, 2.2), K = c(0.0119, 0.0104,
0.00982, 0.013, 0.00968, 0.00922, 0.00975, 0.0103, 0.00978, 0.0115,
0.00886, 0.00847, 0.011, 0.00887, 0.00916, 0.0109, 0.00993, 0.00863,
0.0123, 0.012, 0.00968, 0.0113, 0.0103, 0.0098, 0.014, 0.0118,
0.00885, 0.0107, 0.00936, 0.00847, 0.0106, 0.009, 0.00939, 0.0097,
0.0094, 0.0097, 0.0106, 0.00978, 0.008, 0.0122, 0.0111, 0.00926,
0.0119, 0.0098, 0.00921, 0.0104, 0.0101, 0.0106, 0.0097, 0.0097,
0.00899, 0.0119, 0.0116, 0.0107), K.s.e. = c(0.000476, 0.000375,
0.000487, 0.000878, 0.000279, 0.000236, 0.00085, 0.000454, 0.000375,
0.000608, 0.000219, 0.000193, 0.00041, 0.000213, 0.000318, 0.000986,
0.000301, 0.000256, 0.000717, 0.000996, 0.000342, 0.000781, 0.000481,
0.000247, 0.000957, 0.000728, 0.00031, 0.000568, 0.000256, 0.000208,
0.000516, 0.000297, 0.000363, 0.000649, 0.000268, 0.000325, 0.000204,
0.000191, 0.000202, 0.00095, 0.00061, 0.000304, 0.000921, 0.000216,
0.000292, 0.000698, 0.000433, 0.000546, NA, 0.000473, 0.000302,
0.000447, 0.000636, 0.000407), S = c(0.282, 0.283, 0.261, 0.289,
0.29, 0.272, 0.271, 0.257, 0.246, 0.253, 0.294, 0.238, 0.304,
0.309, 0.271, 0.293, 0.35, 0.298, 0.244, 0.324, 0.246, 0.256,
0.282, 0.23, 0.299, 0.311, 0.274, 0.257, 0.285, 0.25, 0.244,
0.297, 0.254, 0.249, 0.298, 0.293, 0.246, 0.332, 0.234, 0.231,
0.354, 0.262, 0.239, 0.333, 0.24, 0.193, 0.319, 0.227, 0.253,
0.326, 0.243, 0.204, 0.294, 0.252), Ss.e = c(0.00854, 0.0126,
0.00682, 0.0267, 0.0142, 0.00758, 0.0387, 0.0198, 0.00986, 0.00769,
0.00843, 0.00636, 0.0253, 0.011, 0.00597, 0.0131, 0.0141, 0.0112,
0.00864, 0.0172, 0.00633, 0.0118, 0.0145, 0.00561, 0.0164, 0.0119,
0.00532, 0.00736, 0.0106, 0.00612, 0.0102, 0.00727, 0.00534,
0.0186, 0.015, 0.00931, 0.00541, 0.0087, 0.00496, 0.0224, 0.0151,
0.00499, 0.00844, 0.00796, 0.011, 0.0257, 0.00784, 0.00917, NA,
0.0157, 0.00828, 0.00978, 0.0149, 0.015), Year = structure(c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L), levels = c("0", "1", "2"), class = "factor"),
Elevation = c(0.472, 0.472, 0.472, NA, NA, NA, NA, NA, NA,
0.399, 0.399, 0.399, NA, NA, NA, NA, NA, NA, 0.393, 0.393,
0.393, NA, NA, NA, NA, NA, NA, 0.382, 0.382, 0.382, NA, NA,
NA, NA, NA, NA, 0.272, 0.272, 0.272, NA, NA, NA, NA, NA,
NA, 0.29, 0.29, 0.29, NA, NA, NA, NA, NA, NA), Depth.of.Dieback = c(0.056,
0.056, 0.056, NA, NA, NA, NA, NA, NA, 0.058, 0.058, 0.058,
NA, NA, NA, NA, NA, NA, 0.098, 0.098, 0.098, NA, NA, NA,
NA, NA, NA, 0.067, 0.067, 0.067, NA, NA, NA, NA, NA, NA,
0.181, 0.181, 0.181, NA, NA, NA, NA, NA, NA, 0.142, 0.142,
0.142, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -54L), class = "data.frame")

vedoa · May 20, 2024, 1:53pm

The problem you have is that the id is unique for every treatment in every year so there is no actual repeated measure - so the modeling kind of makes no sense in this regard.

When the package tries to do a factorial design it fails since it can't build it - leading to an empty model fit - leading to a erro with all NAs.

The below code will work since i "artificially" make ids that are repeating per treatment and year (OVF is the above data frame you provided. ovf is the new one where i created the newID) faking repeated measures.

library(rstatix)
library(dplyr)

ovf <- OVF %>% group_by(Year, Treatment) %>%  
  mutate(newID = 1:n()) %>% 
  convert_as_factor(newID, Treatment) %>%  
  select(c(newID, Treatment, K, Year))

ovf %>%
  group_by(Year) %>%
  anova_test(dv = K, wid = newID, within = Treatment) %>%
  get_anova_table() %>%
  adjust_pvalue(method = "bonferroni")

dromano · May 22, 2024, 12:09am

From your data, it seems that, for example, you're trying to compare the effect of using runnels at the OVFA locations to not using them at the OFVD locations (since they're at similar elevations?), in which case, maybe it makes sense to consider OVFA and OVFD a single observational unit, and do the same for the other triples of locations. If so, here is something you could try:

original data, saved as "ovf" table

ovf <- 
structure(list(ID = 55:108, Site = c("OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF"), X = c("OVFA", "OVFA", "OVFA",
"OVFA", "OVFA", "OVFA", "OVFA", "OVFA", "OVFA", "OVFD", "OVFD",
"OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFE",
"OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE",
"OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF",
"OVFF", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG",
"OVFG", "OVFG", "OVFH", "OVFH", "OVFH", "OVFH", "OVFH", "OVFH",
"OVFH", "OVFH", "OVFH"), Treatment = c("Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Runnel", "Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel"), Zone = c("Center",
"Center", "Center", "Down", "Down", "Down", "Up", "Up", "Up",
"Center", "Center", "Center", "Down", "Down", "Down", "Up", "Up",
"Up", "Center", "Center", "Center", "Down", "Down", "Down", "Up",
"Up", "Up", "Center", "Center", "Center", "Down", "Down", "Down",
"Up", "Up", "Up", "Center", "Center", "Center", "Down", "Down",
"Down", "Up", "Up", "Up", "Center", "Center", "Center", "Down",
"Down", "Down", "Up", "Up", "Up"), Meter = c(NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), Redox = c(-54.1, -119.8, -130, -44.6, -114.3, -148.9,
38, -41.2, -17.8, -132.9, -228.1, -133.7, -89.8, -139.1, -124,
-18.8, -104.6, -68.4, -193.6, -203, -215.5, -108.3, -206.6, -182.6,
-8.9, -117.8, -91.5, -239.1, -212.2, -204, -167.3, -130.6, -152.3,
-9.3, -90.8, -88.5, -267.5, -247.3, -163.6, -155.7, -124.4, -120,
-65.33, -94.9, -113.3, -238.6, -180.5, -150.6, -179.3, -104.5,
-164.8, -45.2, -78.8, -87.7), Redox.s.e. = c(31.1, 29.7, 51.2,
40.2, 22.8, 40.8, 37.1, 38.8, 64.9, 45.4, 23.7, 33.4, 20.4, 25.3,
24.6, 29.5, 22.8, 32.2, 22.3, 13.9, 11.9, 31.3, 15.7, 4.7, 31.2,
24.4, 22.5, 15.1, 31.6, 16, 46.2, 24.9, 21.8, 44.9, 21.2, 34,
14.1, 9.2, 15.5, 11.3, 14.1, 15.9, 45.7, 29.1, 21.6, 29.8, 45.5,
16.5, 54.9, 38.5, 26.6, 69.4, 39.1, 28.5), Moisture = c(86.7,
86.3, 87.1, 86.5, 87.5, 89, 86.4, 88.5, 87.6, 86.6, 86.4, 86.2,
83.1, 83.9, 81.2, 82.6, 83.5, 83.4, 84.5, 85.6, 82.4, 82.9, 85.1,
84.9, 83.9, 86.5, 84.9, 83.2, 87.2, 84.8, 80.4, 83, 83.2, 83.5,
84.8, 85, 85.9, 82.5, 83, 84.8, 77.8, 81.5, 82.9, 85.8, 86.1,
84.1, 83.1, 81.4, 82.4, 80.1, 81.9, 82.9, 86, 83.4), Moisture.s.e. = c(0.82,
0.88, 0.32, 0.93, 0.64, 0.53, 1.3, 0.94, 0.63, 1.3, 0.21, 1.2,
4.5, 1.4, 1.4, 1.3, 0.5, 1.9, 0.65, 0.7, 1.8, 1, 0.69, 1.1, 1.3,
0.4, 1.4, 1.2, 1.2, 2.1, 2.4, 1.5, 2.1, 2.7, 0.53, 1.5, 0.67,
2, 1, NA, 1.1, 1, 1.3, 0.43, 0.58, 1.1, 0.48, 1.5, 2.2, 1.1,
1.6, 0.52, 0.51, 1.9), OM = c(NA, 62.5, 45.1, NA, 68, 50.2, NA,
67.4, 47.7, NA, 57, 47.6, NA, 68.2, 52.7, NA, 43.1, 49.7, NA,
50.5, 54.6, NA, 46.9, 53, NA, 64.5, 58.2, NA, 29.5, 48.9, NA,
48.6, 51, NA, 66.8, 45.1, NA, 21, 55.3, NA, 39, 54.2, NA, 62.6,
43.9, NA, 39.6, 54.1, NA, 33.9, 48.5, NA, 61.4, 49.7), OM.s.e. = c(NA,
1.3, 5.7, NA, 2.3, 6.4, NA, 6.5, 12.5, NA, 5.3, 1.2, NA, 3.3,
4.4, NA, 2.7, 3.7, NA, 3, 5.8, NA, 4, 1.8, NA, 2.4, 9.3, NA,
1.5, 2.8, NA, 3.7, 5.3, NA, 2.8, 8.1, NA, 7, 4.6, NA, 6.9, 4.8,
NA, 17.1, 6.3, NA, 0.7, 7.1, NA, 3.9, 3.9, NA, 10.3, 3.7), Salinity = c(40.8,
25, 20.4, 34, 24.2, 21.7, 39.5, 16.5, 17, 40.6, 26.2, 26, 43.5,
26.3, 23, 34.5, 27.3, 25.7, 45.5, 18.7, 18.5, 35, 23.8, 18.4,
33.5, 21.7, 19.6, 31.5, 57, 26.6, 38, 26.8, 22.7, 34, 22.7, 23.3,
37.2, 40.4, 23.7, NA, 29, 24.3, 26.6, 15, 20, 33.7, 24, 23.2,
22, 29.5, 21.3, 33, 14, 25.7), Salinity.s.e. = c(5.8, 1.3, 2.4,
3.8, 1.2, 0.94, 5.5, 4.6, 7, 4.4, 1.1, 1.3, 8.5, 2.3, 4.3, 0.5,
1, 2.2, 1, 3.3, 3.4, NA, 2.4, 4.1, 0.5, 3, 2.7, 1, 1.6, 1, 2,
2, 2.7, NA, 1.1, 1.4, 2.8, 2.6, 1.6, NA, 0, 2.6, 2.1, NA, 6,
0.9, 4.9, 3.1, NA, 0.5, 4, NA, 3, 2.2), K = c(0.0119, 0.0104,
0.00982, 0.013, 0.00968, 0.00922, 0.00975, 0.0103, 0.00978, 0.0115,
0.00886, 0.00847, 0.011, 0.00887, 0.00916, 0.0109, 0.00993, 0.00863,
0.0123, 0.012, 0.00968, 0.0113, 0.0103, 0.0098, 0.014, 0.0118,
0.00885, 0.0107, 0.00936, 0.00847, 0.0106, 0.009, 0.00939, 0.0097,
0.0094, 0.0097, 0.0106, 0.00978, 0.008, 0.0122, 0.0111, 0.00926,
0.0119, 0.0098, 0.00921, 0.0104, 0.0101, 0.0106, 0.0097, 0.0097,
0.00899, 0.0119, 0.0116, 0.0107), K.s.e. = c(0.000476, 0.000375,
0.000487, 0.000878, 0.000279, 0.000236, 0.00085, 0.000454, 0.000375,
0.000608, 0.000219, 0.000193, 0.00041, 0.000213, 0.000318, 0.000986,
0.000301, 0.000256, 0.000717, 0.000996, 0.000342, 0.000781, 0.000481,
0.000247, 0.000957, 0.000728, 0.00031, 0.000568, 0.000256, 0.000208,
0.000516, 0.000297, 0.000363, 0.000649, 0.000268, 0.000325, 0.000204,
0.000191, 0.000202, 0.00095, 0.00061, 0.000304, 0.000921, 0.000216,
0.000292, 0.000698, 0.000433, 0.000546, NA, 0.000473, 0.000302,
0.000447, 0.000636, 0.000407), S = c(0.282, 0.283, 0.261, 0.289,
0.29, 0.272, 0.271, 0.257, 0.246, 0.253, 0.294, 0.238, 0.304,
0.309, 0.271, 0.293, 0.35, 0.298, 0.244, 0.324, 0.246, 0.256,
0.282, 0.23, 0.299, 0.311, 0.274, 0.257, 0.285, 0.25, 0.244,
0.297, 0.254, 0.249, 0.298, 0.293, 0.246, 0.332, 0.234, 0.231,
0.354, 0.262, 0.239, 0.333, 0.24, 0.193, 0.319, 0.227, 0.253,
0.326, 0.243, 0.204, 0.294, 0.252), Ss.e = c(0.00854, 0.0126,
0.00682, 0.0267, 0.0142, 0.00758, 0.0387, 0.0198, 0.00986, 0.00769,
0.00843, 0.00636, 0.0253, 0.011, 0.00597, 0.0131, 0.0141, 0.0112,
0.00864, 0.0172, 0.00633, 0.0118, 0.0145, 0.00561, 0.0164, 0.0119,
0.00532, 0.00736, 0.0106, 0.00612, 0.0102, 0.00727, 0.00534,
0.0186, 0.015, 0.00931, 0.00541, 0.0087, 0.00496, 0.0224, 0.0151,
0.00499, 0.00844, 0.00796, 0.011, 0.0257, 0.00784, 0.00917, NA,
0.0157, 0.00828, 0.00978, 0.0149, 0.015), Year = structure(c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L), levels = c("0", "1", "2"), class = "factor"),
Elevation = c(0.472, 0.472, 0.472, NA, NA, NA, NA, NA, NA,
0.399, 0.399, 0.399, NA, NA, NA, NA, NA, NA, 0.393, 0.393,
0.393, NA, NA, NA, NA, NA, NA, 0.382, 0.382, 0.382, NA, NA,
NA, NA, NA, NA, 0.272, 0.272, 0.272, NA, NA, NA, NA, NA,
NA, 0.29, 0.29, 0.29, NA, NA, NA, NA, NA, NA), Depth.of.Dieback = c(0.056,
0.056, 0.056, NA, NA, NA, NA, NA, NA, 0.058, 0.058, 0.058,
NA, NA, NA, NA, NA, NA, 0.098, 0.098, 0.098, NA, NA, NA,
NA, NA, NA, 0.067, 0.067, 0.067, NA, NA, NA, NA, NA, NA,
0.181, 0.181, 0.181, NA, NA, NA, NA, NA, NA, 0.142, 0.142,
0.142, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -54L), class = "data.frame")

As @vedoa did, first create a column that contains IDs for the observational units:

library(tidyverse)
library(rstatix)

ovf_small <- 
  ovf |> 
  # limit to columns of relevant to post
  select(X, Treatment, Zone, Year, K) |> 
  # consider pairs of X values at similar elevation as representing an
  # observational unit
  mutate(
    WID = 
      case_when(
        # regular expression "last letter is _ or _":  (_|_)$
        str_detect(X, '(A|D)$') ~ 'AD',
        str_detect(X, '(E|F)$') ~ 'EF',
        # assign remaining X values to 'GH'
        TRUE ~ 'GH'
      )
  ) |> 
  # convert from data frame to tibble for easier inspection
  as_tibble()

Inspect ovf_small:

ovf_small
#> # A tibble: 54 × 6
#>    X     Treatment Zone   Year        K WID  
#>    <chr> <chr>     <chr>  <fct>   <dbl> <chr>
#>  1 OVFA  Runnel    Center 0     0.0119  AD   
#>  2 OVFA  Runnel    Center 1     0.0104  AD   
#>  3 OVFA  Runnel    Center 2     0.00982 AD   
#>  4 OVFA  Runnel    Down   0     0.013   AD   
#>  5 OVFA  Runnel    Down   1     0.00968 AD   
#>  6 OVFA  Runnel    Down   2     0.00922 AD   
#>  7 OVFA  Runnel    Up     0     0.00975 AD   
#>  8 OVFA  Runnel    Up     1     0.0103  AD   
#>  9 OVFA  Runnel    Up     2     0.00978 AD   
#> 10 OVFD  Reference Center 0     0.0115  AD   
#> # ℹ 44 more rows

Apply anova_test():

ovf_small |> 
  anova_test(
    dv = K,
    wid = WID,
    # factors associated with each observational unit
    within = c(Zone, Treatment, Year)
  )

View output:

#> ANOVA Table (type III tests)
#> 
#> $ANOVA
#>                Effect DFn DFd      F     p p<.05   ges
#> 1                Zone   2   4  0.703 0.547       0.032
#> 2           Treatment   1   2  4.547 0.167       0.223
#> 3                Year   2   4 50.609 0.001     * 0.532
#> 4      Zone:Treatment   2   4  1.529 0.321       0.097
#> 5           Zone:Year   4   8  0.304 0.867       0.024
#> 6      Treatment:Year   2   4  0.177 0.844       0.019
#> 7 Zone:Treatment:Year   4   8  0.370 0.824       0.035
#> 
#> $`Mauchly's Test for Sphericity`
#>           Effect     W     p p<.05
#> 1           Zone 0.528 0.727      
#> 2           Year 0.449 0.670      
#> 3 Zone:Treatment 0.097 0.312      
#> 4 Treatment:Year 0.006 0.080      
#> 
#> $`Sphericity Corrections`
#>           Effect   GGe     DF[GG] p[GG] p[GG]<.05   HFe     DF[HF] p[HF]
#> 1           Zone 0.679 1.36, 2.72 0.515           1.620 3.24, 6.48 0.547
#> 2           Year 0.645 1.29, 2.58 0.009         * 1.313 2.63, 5.25 0.001
#> 3 Zone:Treatment 0.526  1.05, 2.1 0.341           0.608 1.22, 2.43 0.338
#> 4 Treatment:Year 0.502    1, 2.01 0.716           0.507 1.01, 2.03 0.718
#>   p[HF]<.05
#> 1          
#> 2         *
#> 3          
#> 4

^{Created on 2024-05-21 with reprex v2.0.2}

Full reprex

ovf <- 
structure(list(ID = 55:108, Site = c("OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF", "OVF",
"OVF", "OVF", "OVF", "OVF", "OVF"), X = c("OVFA", "OVFA", "OVFA",
"OVFA", "OVFA", "OVFA", "OVFA", "OVFA", "OVFA", "OVFD", "OVFD",
"OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFD", "OVFE",
"OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE", "OVFE",
"OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF", "OVFF",
"OVFF", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG", "OVFG",
"OVFG", "OVFG", "OVFH", "OVFH", "OVFH", "OVFH", "OVFH", "OVFH",
"OVFH", "OVFH", "OVFH"), Treatment = c("Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Runnel", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Reference", "Reference", "Reference",
"Reference", "Reference", "Runnel", "Runnel", "Runnel", "Runnel",
"Runnel", "Runnel", "Runnel", "Runnel", "Runnel"), Zone = c("Center",
"Center", "Center", "Down", "Down", "Down", "Up", "Up", "Up",
"Center", "Center", "Center", "Down", "Down", "Down", "Up", "Up",
"Up", "Center", "Center", "Center", "Down", "Down", "Down", "Up",
"Up", "Up", "Center", "Center", "Center", "Down", "Down", "Down",
"Up", "Up", "Up", "Center", "Center", "Center", "Down", "Down",
"Down", "Up", "Up", "Up", "Center", "Center", "Center", "Down",
"Down", "Down", "Up", "Up", "Up"), Meter = c(NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), Redox = c(-54.1, -119.8, -130, -44.6, -114.3, -148.9,
38, -41.2, -17.8, -132.9, -228.1, -133.7, -89.8, -139.1, -124,
-18.8, -104.6, -68.4, -193.6, -203, -215.5, -108.3, -206.6, -182.6,
-8.9, -117.8, -91.5, -239.1, -212.2, -204, -167.3, -130.6, -152.3,
-9.3, -90.8, -88.5, -267.5, -247.3, -163.6, -155.7, -124.4, -120,
-65.33, -94.9, -113.3, -238.6, -180.5, -150.6, -179.3, -104.5,
-164.8, -45.2, -78.8, -87.7), Redox.s.e. = c(31.1, 29.7, 51.2,
40.2, 22.8, 40.8, 37.1, 38.8, 64.9, 45.4, 23.7, 33.4, 20.4, 25.3,
24.6, 29.5, 22.8, 32.2, 22.3, 13.9, 11.9, 31.3, 15.7, 4.7, 31.2,
24.4, 22.5, 15.1, 31.6, 16, 46.2, 24.9, 21.8, 44.9, 21.2, 34,
14.1, 9.2, 15.5, 11.3, 14.1, 15.9, 45.7, 29.1, 21.6, 29.8, 45.5,
16.5, 54.9, 38.5, 26.6, 69.4, 39.1, 28.5), Moisture = c(86.7,
86.3, 87.1, 86.5, 87.5, 89, 86.4, 88.5, 87.6, 86.6, 86.4, 86.2,
83.1, 83.9, 81.2, 82.6, 83.5, 83.4, 84.5, 85.6, 82.4, 82.9, 85.1,
84.9, 83.9, 86.5, 84.9, 83.2, 87.2, 84.8, 80.4, 83, 83.2, 83.5,
84.8, 85, 85.9, 82.5, 83, 84.8, 77.8, 81.5, 82.9, 85.8, 86.1,
84.1, 83.1, 81.4, 82.4, 80.1, 81.9, 82.9, 86, 83.4), Moisture.s.e. = c(0.82,
0.88, 0.32, 0.93, 0.64, 0.53, 1.3, 0.94, 0.63, 1.3, 0.21, 1.2,
4.5, 1.4, 1.4, 1.3, 0.5, 1.9, 0.65, 0.7, 1.8, 1, 0.69, 1.1, 1.3,
0.4, 1.4, 1.2, 1.2, 2.1, 2.4, 1.5, 2.1, 2.7, 0.53, 1.5, 0.67,
2, 1, NA, 1.1, 1, 1.3, 0.43, 0.58, 1.1, 0.48, 1.5, 2.2, 1.1,
1.6, 0.52, 0.51, 1.9), OM = c(NA, 62.5, 45.1, NA, 68, 50.2, NA,
67.4, 47.7, NA, 57, 47.6, NA, 68.2, 52.7, NA, 43.1, 49.7, NA,
50.5, 54.6, NA, 46.9, 53, NA, 64.5, 58.2, NA, 29.5, 48.9, NA,
48.6, 51, NA, 66.8, 45.1, NA, 21, 55.3, NA, 39, 54.2, NA, 62.6,
43.9, NA, 39.6, 54.1, NA, 33.9, 48.5, NA, 61.4, 49.7), OM.s.e. = c(NA,
1.3, 5.7, NA, 2.3, 6.4, NA, 6.5, 12.5, NA, 5.3, 1.2, NA, 3.3,
4.4, NA, 2.7, 3.7, NA, 3, 5.8, NA, 4, 1.8, NA, 2.4, 9.3, NA,
1.5, 2.8, NA, 3.7, 5.3, NA, 2.8, 8.1, NA, 7, 4.6, NA, 6.9, 4.8,
NA, 17.1, 6.3, NA, 0.7, 7.1, NA, 3.9, 3.9, NA, 10.3, 3.7), Salinity = c(40.8,
25, 20.4, 34, 24.2, 21.7, 39.5, 16.5, 17, 40.6, 26.2, 26, 43.5,
26.3, 23, 34.5, 27.3, 25.7, 45.5, 18.7, 18.5, 35, 23.8, 18.4,
33.5, 21.7, 19.6, 31.5, 57, 26.6, 38, 26.8, 22.7, 34, 22.7, 23.3,
37.2, 40.4, 23.7, NA, 29, 24.3, 26.6, 15, 20, 33.7, 24, 23.2,
22, 29.5, 21.3, 33, 14, 25.7), Salinity.s.e. = c(5.8, 1.3, 2.4,
3.8, 1.2, 0.94, 5.5, 4.6, 7, 4.4, 1.1, 1.3, 8.5, 2.3, 4.3, 0.5,
1, 2.2, 1, 3.3, 3.4, NA, 2.4, 4.1, 0.5, 3, 2.7, 1, 1.6, 1, 2,
2, 2.7, NA, 1.1, 1.4, 2.8, 2.6, 1.6, NA, 0, 2.6, 2.1, NA, 6,
0.9, 4.9, 3.1, NA, 0.5, 4, NA, 3, 2.2), K = c(0.0119, 0.0104,
0.00982, 0.013, 0.00968, 0.00922, 0.00975, 0.0103, 0.00978, 0.0115,
0.00886, 0.00847, 0.011, 0.00887, 0.00916, 0.0109, 0.00993, 0.00863,
0.0123, 0.012, 0.00968, 0.0113, 0.0103, 0.0098, 0.014, 0.0118,
0.00885, 0.0107, 0.00936, 0.00847, 0.0106, 0.009, 0.00939, 0.0097,
0.0094, 0.0097, 0.0106, 0.00978, 0.008, 0.0122, 0.0111, 0.00926,
0.0119, 0.0098, 0.00921, 0.0104, 0.0101, 0.0106, 0.0097, 0.0097,
0.00899, 0.0119, 0.0116, 0.0107), K.s.e. = c(0.000476, 0.000375,
0.000487, 0.000878, 0.000279, 0.000236, 0.00085, 0.000454, 0.000375,
0.000608, 0.000219, 0.000193, 0.00041, 0.000213, 0.000318, 0.000986,
0.000301, 0.000256, 0.000717, 0.000996, 0.000342, 0.000781, 0.000481,
0.000247, 0.000957, 0.000728, 0.00031, 0.000568, 0.000256, 0.000208,
0.000516, 0.000297, 0.000363, 0.000649, 0.000268, 0.000325, 0.000204,
0.000191, 0.000202, 0.00095, 0.00061, 0.000304, 0.000921, 0.000216,
0.000292, 0.000698, 0.000433, 0.000546, NA, 0.000473, 0.000302,
0.000447, 0.000636, 0.000407), S = c(0.282, 0.283, 0.261, 0.289,
0.29, 0.272, 0.271, 0.257, 0.246, 0.253, 0.294, 0.238, 0.304,
0.309, 0.271, 0.293, 0.35, 0.298, 0.244, 0.324, 0.246, 0.256,
0.282, 0.23, 0.299, 0.311, 0.274, 0.257, 0.285, 0.25, 0.244,
0.297, 0.254, 0.249, 0.298, 0.293, 0.246, 0.332, 0.234, 0.231,
0.354, 0.262, 0.239, 0.333, 0.24, 0.193, 0.319, 0.227, 0.253,
0.326, 0.243, 0.204, 0.294, 0.252), Ss.e = c(0.00854, 0.0126,
0.00682, 0.0267, 0.0142, 0.00758, 0.0387, 0.0198, 0.00986, 0.00769,
0.00843, 0.00636, 0.0253, 0.011, 0.00597, 0.0131, 0.0141, 0.0112,
0.00864, 0.0172, 0.00633, 0.0118, 0.0145, 0.00561, 0.0164, 0.0119,
0.00532, 0.00736, 0.0106, 0.00612, 0.0102, 0.00727, 0.00534,
0.0186, 0.015, 0.00931, 0.00541, 0.0087, 0.00496, 0.0224, 0.0151,
0.00499, 0.00844, 0.00796, 0.011, 0.0257, 0.00784, 0.00917, NA,
0.0157, 0.00828, 0.00978, 0.0149, 0.015), Year = structure(c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L), levels = c("0", "1", "2"), class = "factor"),
Elevation = c(0.472, 0.472, 0.472, NA, NA, NA, NA, NA, NA,
0.399, 0.399, 0.399, NA, NA, NA, NA, NA, NA, 0.393, 0.393,
0.393, NA, NA, NA, NA, NA, NA, 0.382, 0.382, 0.382, NA, NA,
NA, NA, NA, NA, 0.272, 0.272, 0.272, NA, NA, NA, NA, NA,
NA, 0.29, 0.29, 0.29, NA, NA, NA, NA, NA, NA), Depth.of.Dieback = c(0.056,
0.056, 0.056, NA, NA, NA, NA, NA, NA, 0.058, 0.058, 0.058,
NA, NA, NA, NA, NA, NA, 0.098, 0.098, 0.098, NA, NA, NA,
NA, NA, NA, 0.067, 0.067, 0.067, NA, NA, NA, NA, NA, NA,
0.181, 0.181, 0.181, NA, NA, NA, NA, NA, NA, 0.142, 0.142,
0.142, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -54L), class = "data.frame")

library(tidyverse)
library(rstatix)

ovf_small <- 
  ovf |> 
  # limit to columns of relevant to post
  select(X, Treatment, Zone, Year, K) |> 
  # consider pairs of X values at similar elevation as representing an
  # observational unit
  mutate(
    WID = 
      case_when(
        # regular expression "last letter is _ or _":  (_|_)$
        str_detect(X, '(A|D)$') ~ 'AD',
        str_detect(X, '(E|F)$') ~ 'EF',
        # assign remaining X values to 'GH'
        TRUE ~ 'GH'
      )
  ) |> 
  # convert from data frame to tibble for easier inspection
  as_tibble()
    
ovf_small
#> # A tibble: 54 × 6
#>    X     Treatment Zone   Year        K WID  
#>    <chr> <chr>     <chr>  <fct>   <dbl> <chr>
#>  1 OVFA  Runnel    Center 0     0.0119  AD   
#>  2 OVFA  Runnel    Center 1     0.0104  AD   
#>  3 OVFA  Runnel    Center 2     0.00982 AD   
#>  4 OVFA  Runnel    Down   0     0.013   AD   
#>  5 OVFA  Runnel    Down   1     0.00968 AD   
#>  6 OVFA  Runnel    Down   2     0.00922 AD   
#>  7 OVFA  Runnel    Up     0     0.00975 AD   
#>  8 OVFA  Runnel    Up     1     0.0103  AD   
#>  9 OVFA  Runnel    Up     2     0.00978 AD   
#> 10 OVFD  Reference Center 0     0.0115  AD   
#> # ℹ 44 more rows

ovf_small |> 
  anova_test(
    dv = K,
    wid = WID,
    # factors associated with each observational unit
    within = c(Zone, Treatment, Year)
  )
#> ANOVA Table (type III tests)
#> 
#> $ANOVA
#>                Effect DFn DFd      F     p p<.05   ges
#> 1                Zone   2   4  0.703 0.547       0.032
#> 2           Treatment   1   2  4.547 0.167       0.223
#> 3                Year   2   4 50.609 0.001     * 0.532
#> 4      Zone:Treatment   2   4  1.529 0.321       0.097
#> 5           Zone:Year   4   8  0.304 0.867       0.024
#> 6      Treatment:Year   2   4  0.177 0.844       0.019
#> 7 Zone:Treatment:Year   4   8  0.370 0.824       0.035
#> 
#> $`Mauchly's Test for Sphericity`
#>           Effect     W     p p<.05
#> 1           Zone 0.528 0.727      
#> 2           Year 0.449 0.670      
#> 3 Zone:Treatment 0.097 0.312      
#> 4 Treatment:Year 0.006 0.080      
#> 
#> $`Sphericity Corrections`
#>           Effect   GGe     DF[GG] p[GG] p[GG]<.05   HFe     DF[HF] p[HF]
#> 1           Zone 0.679 1.36, 2.72 0.515           1.620 3.24, 6.48 0.547
#> 2           Year 0.645 1.29, 2.58 0.009         * 1.313 2.63, 5.25 0.001
#> 3 Zone:Treatment 0.526  1.05, 2.1 0.341           0.608 1.22, 2.43 0.338
#> 4 Treatment:Year 0.502    1, 2.01 0.716           0.507 1.01, 2.03 0.718
#>   p[HF]<.05
#> 1          
#> 2         *
#> 3          
#> 4

^{Created on 2024-05-21 with reprex v2.0.2}

silkysullivan5 · June 3, 2024, 6:08pm

Thank you. But I'm trying to compare all three reference to all three treatment. The question is:

Is there a treatment effect on decomposition. I have Y0 for both treatments, then Y1 and Y2. It seemed like a repeated measures test would be good here.

dromano · June 3, 2024, 8:54pm

I'm not sure I understand — could you say more? The code I shared does represent a repeated measures test.

Again, I'm not sure I understand — there are only two treatments: "runnels" and "reference". In your view, what constitutes a natural observational unit that is being measured? And could you list a representative sample of the comparisons you would like to make?

silkysullivan5 · June 3, 2024, 9:58pm

Hi -

It's a BACI Design.

I have two treatments: "Impounded" and "runneled".
I have 3 years: "before", "Y1", and "Y2"
There are a number of dependent variables, but for this example I am using Redox.

I ended up doing a mixed anova with the following code:

res.aov <- LB %>%
group_by(Zone) %>%
anova_test(dv = Redox, wid = ID, between = Treatment, within = Year) %>%
get_anova_table() %>%
adjust_pvalue(method = "none")

If I'm interpreting it correctly, a significant interaction term indicates that runnels had an affect on Redox potential.

Thanks for your help!

dromano · June 4, 2024, 2:42pm

You're welcome, but I'm not clear — are you asking for more help? If so, could you share data from the table LB?

silkysullivan5 · June 6, 2024, 12:07pm

Yes I do still need help. if you could just change "LB" table to "OVF" (the one I shared earlier). Looking for the best statistics for a BACI design with two treatment years.

dromano · June 6, 2024, 12:34pm

Have asked at Cross Validated, Stack Exchange's statistics site? There may be folks here could help you, but Cross Validated is probably a better place to ask about statistical approaches.

system · September 4, 2024, 12:35pm

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