| grim_explain | R Documentation |
grim_explain() tests a single mean like grim() does, but
also lays out the calculation in plain words: which true means the
reported one could stand for, which sums of the underlying integers
follow from them, and whether any of these sums is possible.
The text is built from the numbers that decide the test itself, so it
always agrees with grim().
grim_explain(
x,
n,
digits_x,
items_averaged,
data_type,
x_meaning = c("mean", "percentage"),
rounding = "up_or_down",
threshold = 5,
symmetric = FALSE
)
x |
Numeric. The reported mean or percentage. |
n |
Integer. The reported sample size. |
digits_x |
Integer. The number of decimal places in |
items_averaged |
Integer. The number of items averaged into each respondent's score: 1 for a single item or the percentage of participants with some answer, 4 for the mean of a four-item scale, 10 for the average percent correct on a ten-question test. Not the number of scale points. No default. |
data_type |
String. The kind of data that
If |
x_meaning |
String. What |
rounding |
String. The rounding method assumed to have produced |
threshold |
Numeric. For |
symmetric |
Logical. If |
An object of class scrutiny_explain: a list with the result as
consistency (TRUE, FALSE, or NA), the explanation's paragraphs,
and the test and its inputs. It prints as the explanation.
grimmer_explain() and debit_explain() for the other tests,
and explain_map() for mapper output.
grim_explain(
x = 5.19,
n = 28,
digits_x = 2,
items_averaged = 1,
data_type = "integer"
)
grim_explain(
x = 5.19,
n = 32,
digits_x = 2,
items_averaged = 1,
data_type = "integer"
)
grim_explain(
x = 71,
n = 43,
digits_x = 0,
items_averaged = 1,
data_type = "integer",
x_meaning = "percentage"
)
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