2.2 Nested summary of high protein status
2.2.1 By cow:
There are 72 cows that had at least one instance of milk with high protein content. Every cow (all 79) had at least one instance of milk with low protein content.
rm_covsum_nested(data = Milk, id = c("Cow"), covs = c("protein", "Time", "Diet", "Yard"), maincov = "High_Protein")
## Warning in (function (data, covs, maincov = NULL, id = NULL, digits = 1, : Use this function at your own risk. Please check output.
## Order of nested ids matter. For example, in c('id1','id2') id1 should be nested within id2, etc.
## Warning in (function (data, covs, maincov = NULL, id = NULL, digits = 1, : Unnested p-value and statistical test is incorrect for nested data, but is kept for comparison to nested p-value.
## Nested p-value derived from anova(afex::mixed(maincov ~ cov + (1|id1:id2:...idn), family=binomial, data, method='LRT')).
Full Sample (n=79) | N (n=79) | Y (n=72) | Unnested p-value | Unnested Effect Size | Unnested StatTest | Nested p-value | |
---|---|---|---|---|---|---|---|
protein | <0.001 | 0.86 | Wilcoxon Rank Sum, Wilcoxon r |
Did not converge; quasi or complete category separation |
|||
Mean (sd) | 3.4 (0.2) | 3.3 (0.1) | 3.8 (0.1) | ||||
Median (Min,Max) | 3.4 (2.9, 3.9) | 3.3 (2.9, 3.5) | 3.8 (3.6, 4.2) | ||||
Time | 0.001 | 0.26 | Wilcoxon Rank Sum, Wilcoxon r | 0.024 | |||
Mean (sd) | 9.0 (1.1) | 9.2 (1.5) | 7.4 (4.3) | ||||
Median (Min,Max) | 10.0 (7.1, 10.4) | 9.4 (2.5, 12.5) | 7.7 (1.0, 18.0) | ||||
Diet | 0.89 | 0.039 | Chi Sq, Cramer’s V | <0.001 | |||
barley | 25 (32) | 25 (32) | 24 (33) | ||||
barley+lupins | 27 (34) | 27 (34) | 26 (36) | ||||
lupins | 27 (34) | 27 (34) | 22 (31) | ||||
Yard | 1.00 | 0.014 | Chi Sq, Cramer’s V | 0.68 | |||
1 | 19 (24) | 19 (24) | 17 (24) | ||||
2 | 11 (14) | 11 (14) | 10 (14) | ||||
3 | 17 (22) | 17 (22) | 15 (21) | ||||
4 | 11 (14) | 11 (14) | 10 (14) | ||||
5 | 21 (27) | 21 (27) | 20 (28) |
2.2.2 By cow nested in yard:
Notice that this summary is the same as by cow, since each cow stays in only one yard for this example data.
rm_covsum_nested(data = Milk, id = c("Cow", "Yard"), covs = c("protein", "Time", "Diet"), maincov = "High_Protein")
Full Sample (n=79) | N (n=79) | Y (n=72) | Unnested p-value | Unnested Effect Size | Unnested StatTest | Nested p-value | |
---|---|---|---|---|---|---|---|
protein | <0.001 | 0.86 | Wilcoxon Rank Sum, Wilcoxon r |
Did not converge; quasi or complete category separation |
|||
Mean (sd) | 3.4 (0.2) | 3.3 (0.1) | 3.8 (0.1) | ||||
Median (Min,Max) | 3.4 (2.9, 3.9) | 3.3 (2.9, 3.5) | 3.8 (3.6, 4.2) | ||||
Time | 0.001 | 0.26 | Wilcoxon Rank Sum, Wilcoxon r | 0.024 | |||
Mean (sd) | 9.0 (1.1) | 9.2 (1.5) | 7.4 (4.3) | ||||
Median (Min,Max) | 10.0 (7.1, 10.4) | 9.4 (2.5, 12.5) | 7.7 (1.0, 18.0) | ||||
Diet | 0.89 | 0.039 | Chi Sq, Cramer’s V | <0.001 | |||
barley | 25 (32) | 25 (32) | 24 (33) | ||||
barley+lupins | 27 (34) | 27 (34) | 26 (36) | ||||
lupins | 27 (34) | 27 (34) | 22 (31) |