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Freidman test not working and may not be appropriate?

Time:05-03

I want to know if there is a difference between mean distance between pairs of individuals across months. This is a repeated measures situation with a non normally distributed response variable. So, I am trying to run a Friedman test, but I can't get it to work and do not understand the error. I have made sure each pair within month is unreplicated. Any advice? Thanks

dput(head(cohorts,10))
structure(list(DeerCow = structure(c(1L, 1L, 1L, 2L, 2L, 3L, 
3L, 3L, 4L, 4L), .Label = c("A82118_A632", "A82118_A633", "A82118_A635", 
"A82118_A636", "A82120_A629", "A82120_A630", "A82120_A631", "A82120_A633", 
"A82120_A634", "A82120_A637", "A82121_A628", "A82121_A629", "A82121_A630", 
"A82121_A631", "A82121_A633", "A82121_A634", "A82121_A637", "A82122_A632", 
"A82122_A633", "A82122_A635", "A82122_A636", "A82126_A629", "A82126_A630", 
"A82126_A631", "A82126_A633", "A82126_A634", "A82126_A637", "A82127_A628", 
"A82127_A632", "A82127_A633", "A82127_A636", "A82132_A628", "A82132_A629", 
"A82132_A630", "A82132_A631", "A82132_A632", "A82132_A633", "A82132_A636", 
"A82135_A629", "A82135_A630", "A82135_A631", "A82135_A632", "A82135_A633", 
"A82135_A634", "A82135_A635", "A82135_A636", "A82135_A637", "A82136_A628", 
"A82136_A630", "A82136_A631", "A82136_A632", "A82136_A634", "A82136_A636", 
"A82139_A628", "A82139_A629", "A82139_A630", "A82139_A631", "A82139_A633", 
"A82139_A634", "A82139_A637", "A82140_A628", "A82140_A629", "A82140_A630", 
"A82140_A631", "A82140_A632", "A82140_A633", "A82140_A634", "A82140_A636", 
"A82140_A637", "A82141_A632", "A82141_A633", "A82141_A635", "A82141_A636", 
"A82142_A628", "A82142_A630", "A82142_A631", "A82142_A632", "A82142_A633", 
"A82142_A634", "A82142_A636", "A82145_A628", "A82145_A630", "A82145_A631", 
"A82145_A632", "A82145_A633", "A82145_A634", "A82145_A636", "A82146_A628", 
"A82146_A629", "A82146_A631", "A82146_A632", "A82146_A633", "A82146_A634", 
"A82146_A635", "A82146_A636", "A82146_A637"), class = "factor"), 
    YearMonth = structure(c(1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L, 
    2L), .Label = c("2019-06", "2019-07", "2019-08", "2019-09"
    ), class = "factor"), Mean_dist_m = c(1612.14219548009, 1870.02550251614, 
    2108.91771044161, 2435.58097649919, 2909.78460371203, 3311.10358068401, 
    2661.09192488057, 2378.49371438137, 1336.20465251428, 1637.15035958527
    )), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -10L), groups = structure(list(DeerCow = structure(1:4, .Label = c("A82118_A632", 
"A82118_A633", "A82118_A635", "A82118_A636", "A82120_A629", "A82120_A630", 
"A82120_A631", "A82120_A633", "A82120_A634", "A82120_A637", "A82121_A628", 
"A82121_A629", "A82121_A630", "A82121_A631", "A82121_A633", "A82121_A634", 
"A82121_A637", "A82122_A632", "A82122_A633", "A82122_A635", "A82122_A636", 
"A82126_A629", "A82126_A630", "A82126_A631", "A82126_A633", "A82126_A634", 
"A82126_A637", "A82127_A628", "A82127_A632", "A82127_A633", "A82127_A636", 
"A82132_A628", "A82132_A629", "A82132_A630", "A82132_A631", "A82132_A632", 
"A82132_A633", "A82132_A636", "A82135_A629", "A82135_A630", "A82135_A631", 
"A82135_A632", "A82135_A633", "A82135_A634", "A82135_A635", "A82135_A636", 
"A82135_A637", "A82136_A628", "A82136_A630", "A82136_A631", "A82136_A632", 
"A82136_A634", "A82136_A636", "A82139_A628", "A82139_A629", "A82139_A630", 
"A82139_A631", "A82139_A633", "A82139_A634", "A82139_A637", "A82140_A628", 
"A82140_A629", "A82140_A630", "A82140_A631", "A82140_A632", "A82140_A633", 
"A82140_A634", "A82140_A636", "A82140_A637", "A82141_A632", "A82141_A633", 
"A82141_A635", "A82141_A636", "A82142_A628", "A82142_A630", "A82142_A631", 
"A82142_A632", "A82142_A633", "A82142_A634", "A82142_A636", "A82145_A628", 
"A82145_A630", "A82145_A631", "A82145_A632", "A82145_A633", "A82145_A634", 
"A82145_A636", "A82146_A628", "A82146_A629", "A82146_A631", "A82146_A632", 
"A82146_A633", "A82146_A634", "A82146_A635", "A82146_A636", "A82146_A637"
), class = "factor"), .rows = structure(list(1:3, 4:5, 6:8, 9:10), ptype = integer(0), class = c("vctrs_list_of", 
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -4L), .drop = TRUE))
hist(cohorts$Mean_dist_m)

friedman.test(y=cohorts$Mean_dist_m, groups=cohorts$YearMonth, blocks=cohorts$DeerCow)

Error in friedman.test.default(y = cohorts$Mean_dist_m, groups = cohorts$YearMonth,  : 
  not an unreplicated complete block design

CodePudding user response:

If you look at the code, the part that is throwing the error is:

if (any(table(groups, blocks) != 1)) 
        stop("not an unreplicated complete block design")

If you look at your data and do the appropriate table() operation, you get:

table(cohorts$YearMonth, cohorts$DeerCow)
#         A82118_A632 A82118_A633 A82118_A635 A82118_A636 A82120_A629 A82120_A630 A82120_A631 A82120_A633
# 2019-06           1           1           1           1           0           0           0           0
# 2019-07           1           1           1           1           0           0           0           0
# 2019-08           1           0           1           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82120_A634 A82120_A637 A82121_A628 A82121_A629 A82121_A630 A82121_A631 A82121_A633 A82121_A634
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82121_A637 A82122_A632 A82122_A633 A82122_A635 A82122_A636 A82126_A629 A82126_A630 A82126_A631
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82126_A633 A82126_A634 A82126_A637 A82127_A628 A82127_A632 A82127_A633 A82127_A636 A82132_A628
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82132_A629 A82132_A630 A82132_A631 A82132_A632 A82132_A633 A82132_A636 A82135_A629 A82135_A630
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82135_A631 A82135_A632 A82135_A633 A82135_A634 A82135_A635 A82135_A636 A82135_A637 A82136_A628
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82136_A630 A82136_A631 A82136_A632 A82136_A634 A82136_A636 A82139_A628 A82139_A629 A82139_A630
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82139_A631 A82139_A633 A82139_A634 A82139_A637 A82140_A628 A82140_A629 A82140_A630 A82140_A631
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82140_A632 A82140_A633 A82140_A634 A82140_A636 A82140_A637 A82141_A632 A82141_A633 A82141_A635
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82141_A636 A82142_A628 A82142_A630 A82142_A631 A82142_A632 A82142_A633 A82142_A634 A82142_A636
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82145_A628 A82145_A630 A82145_A631 A82145_A632 A82145_A633 A82145_A634 A82145_A636 A82146_A628
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0
# 
#         A82146_A629 A82146_A631 A82146_A632 A82146_A633 A82146_A634 A82146_A635 A82146_A636 A82146_A637
# 2019-06           0           0           0           0           0           0           0           0
# 2019-07           0           0           0           0           0           0           0           0
# 2019-08           0           0           0           0           0           0           0           0
# 2019-09           0           0           0           0           0           0           0           0

The output is so long because you still have all the factor levels attached. If you use droplevels(), you'll see that you still don't pass the test:

table(droplevels(cohorts$YearMonth), droplevels(cohorts$DeerCow))
#         A82118_A632 A82118_A633 A82118_A635 A82118_A636
# 2019-06           1           1           1           1
# 2019-07           1           1           1           1
# 2019-08           1           0           1           0

If you filter the data to exclude 2019-08, then you pass the test and the Friedman.test() function works.

tmp <- subset(cohorts, YearMonth != "2019-08")
table(droplevels(tmp$YearMonth), droplevels(tmp$DeerCow))

#         A82118_A632 A82118_A633 A82118_A635 A82118_A636
# 2019-06           1           1           1           1
# 2019-07           1           1           1           1

friedman.test(y=tmp$Mean_dist_m, groups=droplevels(tmp$YearMonth), blocks=droplevels(tmp$DeerCow))

#       Friedman rank sum test
# 
# data:  tmp$Mean_dist_m, droplevels(tmp$YearMonth) and droplevels(tmp$DeerCow)
# Friedman chi-squared = 1, df = 1, p-value = 0.3173

The key is to look at the cross-tabulation of group and block variables and if there are any values in the table that are note 1, then your design does not meet the requirements of Friedman.test().

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