Невозможно применить код обратного хода из подмножества ко всем данным

Question

Мне дали код для проверки различий в SEX в моем df, подмножество, которое я дал, содержит SPECIES с M, F и U в столбце SEX, но я хочу проверить только разницу M против F.Некоторые SPECIES также имеют только один SEX, поэтому один уровень

Следующий код работает для следующего подмножества данных, содержащего, на всех SPECIES, M, F, а некоторые с U

subset %>%
  select(-RING, -AGE, -FAT) %>%
  gather(variable, value, -SPECIES, -SEX) %>%
  group_by(SPECIES, variable) %>%
  nest() %>% 
  mutate(
    chi_sq_results = map(data, ~ chisq.test(.x$SEX, .x$value)),
    tidied = map(chi_sq_results, tidy)
  ) %>%
  unnest(tidied, .drop = TRUE)


> dput(subset)
structure(list(RING = c("H8309", "K617", "A264905", "A358705", 
"A432721", "O59461", "O92094", "O92095"), SPECIES = c("ACCIPITER NISUS", 
"ACCIPITER NISUS", "ACROCEPHALUS SCIRPACEUS", "ACROCEPHALUS SCIRPACEUS", 
"ACROCEPHALUS SCIRPACEUS", "AEGITHALOS CAUDATUS", "AEGITHALOS CAUDATUS", 
"AEGITHALOS CAUDATUS"), SEX = c("M", "F", "F", "M", "U", "M", 
"F", "U"), AGE = c(5L, 4L, 4L, 3L, 4L, 4L, 2L, 2L), FAT = c(0L, 
0L, 2L, 2L, 6L, 0L, 0L, 0L), WEIGHT = c(141, 2885, 118, 11, 145, 
64, 68, 7), WING = c(199, 232, 645, 66, 63, 57, 56, 58), WINGPRI = c(117L, 
167L, NA, 50L, 48L, 42L, 38L, 44L), BEAK = c(192, 204, NA, 182, 
16, 82, 796, 878), TARSUS = c(52, 622, NA, 22, 219, 138, 1654, 
1785)), .Names = c("RING", "SPECIES", "SEX", "AGE", "FAT", "WEIGHT", 
"WING", "WINGPRI", "BEAK", "TARSUS"), row.names = c(NA, -8L), class = c("tbl_df", 
"tbl", "data.frame"), spec = structure(list(cols = structure(list(
    RING = structure(list(), class = c("collector_character", 
    "collector")), SPECIES = structure(list(), class = c("collector_character", 
    "collector")), SEX = structure(list(), class = c("collector_character", 
    "collector")), AGE = structure(list(), class = c("collector_integer", 
    "collector")), FAT = structure(list(), class = c("collector_integer", 
    "collector")), WEIGHT = structure(list(), class = c("collector_number", 
    "collector")), WING = structure(list(), class = c("collector_number", 
    "collector")), WINGPRI = structure(list(), class = c("collector_integer", 
    "collector")), BEAK = structure(list(), class = c("collector_number", 
    "collector")), TARSUS = structure(list(), class = c("collector_number", 
    "collector"))), .Names = c("RING", "SPECIES", "SEX", "AGE", 
"FAT", "WEIGHT", "WING", "WINGPRI", "BEAK", "TARSUS")), default = structure(list(), class = c("collector_guess", 
"collector"))), .Names = c("cols", "default"), class = "col_spec"))

НоКогда я пытаюсь применить этот же код к другому подмножеству без U, он не работает, он также не работает в полной df.Я полагаю, что это не работает на полном df, потому что некоторые SPECIES имеют только один уровень в столбце SEX, например.SPECIES x имеет только F (женщины)

Я получаю эту ошибку:

Error in mutate_impl(.data, dots) : 
  Evaluation error: 'x' and 'y' must have at least 2 levels.

Подмножество без U:

> dput(subsetU)
structure(list(RING = c("H8309", "K617", "A264905", "A358705", 
"O59461", "O92094"), SPECIES = c("ACCIPITER NISUS", "ACCIPITER NISUS", 
"ACROCEPHALUS SCIRPACEUS", "ACROCEPHALUS SCIRPACEUS", "AEGITHALOS CAUDATUS", 
"AEGITHALOS CAUDATUS"), SEX = c("M", "F", "F", "M", "M", "F"), 
    AGE = c(5L, 4L, 4L, 3L, 4L, 2L), FAT = c(0L, 0L, 2L, 2L, 
    0L, 0L), WEIGHT = c(141, 2885, 118, 11, 64, 68), WING = c(199, 
    232, 645, 66, 57, 56), WINGPRI = c(117L, 167L, NA, 50L, 42L, 
    38L), BEAK = c(192, 204, NA, 182, 82, 796), TARSUS = c(52, 
    622, NA, 22, 138, 1654)), .Names = c("RING", "SPECIES", "SEX", 
"AGE", "FAT", "WEIGHT", "WING", "WINGPRI", "BEAK", "TARSUS"), row.names = c(NA, 
-6L), class = c("tbl_df", "tbl", "data.frame"))

Надеемся, что данные воспроизводимы, этов первый раз я делаю это

Спасибо

Answer 1

Для параметрически зависимых переменных я бы использовал t-критерий (или критерий Уилкоксона, если предположения для t-критерия не соблюдаются), если вы сравниваете 2 группы.Если вы хотите сравнить более двух групп (например, мужскую, женскую и U-категорию, о которых вы упомянули), я бы использовал одностороннюю анову (или Крускала-Уоллиса, если допущения относительно ановой неудачи).

set.seed(123)

# Dataframe with 2 Variables.
# one is group
# the other one is some sort of continous value choosen randomly from a normal distribution
# for group 1 the values are taken from a ND with mean = 10 and standard deviation = 5
# for group 2 the values are taken from a ND with mean = 30 and standard deviation = 5
# so there should be a clear difference between group 1 and 2
test = data.frame("group" = rep(c(1,2), 50))
test$value = NA
test$value[which(test$group == 1)] = rnorm(50, 10, 5)
test$value[which(test$group == 2)] = rnorm(50, 30, 5)

# A quick look on the data
plot(test$group, test$value)

# t.test to check if there are differences
t.test(formula = value ~ group, data = test)

# > t.test(formula = value ~ group, data = test)
# 
# Welch Two Sample t-test
# 
# data:  value by group
# t = -22.452, df = 97.951, p-value < 0.00000000000000022
# alternative hypothesis: true difference in means is not equal to 0
# 95 percent confidence interval:
#   -22.37724 -18.74280
# sample estimates:
#   mean in group 1 mean in group 2 
# 10.17202        30.73204 

# mean group 1 is around 10 and in group 2 around 30 (as we expected)
# it is very unlikely that this is just a random coincidence. Chances therefore would be only 0.00000000000000022


# lets try this with the chisquare test
chisq.test(test$group, test$value)

# > chisq.test(test$group, test$value)
# 
# Pearson's Chi-squared test
# 
# data:  test$group and test$value
# X-squared = 100, df = 99, p-value = 0.453
# 
# Warning message:
# In chisq.test(test$group, test$value) :
# Chi-Quadrat-Approximation kann inkorrekt sein

# We get a warning (which translates to english as something like Chi-Square-Approximation might be incorrect)
# this is caused because of the many value-cases that are just present in one and not in the other group.
# chisquare needs at least 5 (if i remember right) values per group X value combination to work.
# But still, we get a p-value which indicates, that there is no significant difference (but don't forget the warning)
# Table to show the many value-cases beeing present just in one group
table(test$value, test$group)

# lets split the values by mean and label the ones below the mean as 1 and those above as 2
# and run the chisquare test again.
test$value2 = 1
test$value2[which(test$value > mean(test$value))] = 2
chisq.test(test$group, test$value2)
table(test$value2, test$group)
# now its working and it tells that there is some significant difference in the distribution of 
# value below and above mean between both groups.