View source: R/dig_complement_contrasts.R
dig_complement_contrasts | R Documentation |
Complement contrast patterns identify conditions under which there is a significant difference in some numerical variable between elements that satisfy the identified condition and the rest of the data table.
(var | C) != (var | not C)
There is a statistically significant difference in variable var
between
group of elements that satisfy condition C
and a group of elements that
do not satisfy condition C
.
(life_expectancy | smoker) < (life_expectancy | non-smoker)
The life expectancy in people that smoke cigarettes is in average
significantly lower than in people that do not smoke.
The complement contrast is computed using a two-sample statistical test,
which is specified by the method
argument. The function computes the
complement contrast in all variables specified by the vars
argument.
Complement contrasts are computed based on sub-data corresponding
to conditions generated from the condition
columns and the rest of the
data table. Function #' dig_complement_contrasts()
supports crisp
conditions only, i.e., the condition columns in x
must be logical.
dig_complement_contrasts(
x,
condition = where(is.logical),
vars = where(is.numeric),
disjoint = var_names(colnames(x)),
min_length = 0L,
max_length = Inf,
min_support = 0,
max_support = 1 - min_support,
method = "t",
alternative = "two.sided",
h0 = if (method == "var") 1 else 0,
conf_level = 0.95,
max_p_value = 0.05,
t_var_equal = FALSE,
wilcox_exact = FALSE,
wilcox_correct = TRUE,
wilcox_tol_root = 1e-04,
wilcox_digits_rank = Inf,
max_results = Inf,
verbose = FALSE,
threads = 1L
)
x |
a matrix or data frame with data to search the patterns in. |
condition |
a tidyselect expression (see tidyselect syntax) specifying the columns to use as condition predicates |
vars |
a tidyselect expression (see tidyselect syntax) specifying the columns to use for computation of contrasts |
disjoint |
an atomic vector of size equal to the number of columns of |
min_length |
the minimum size (the minimum number of predicates) of the condition to be generated (must be greater or equal to 0). If 0, the empty condition is generated in the first place. |
max_length |
The maximum size (the maximum number of predicates) of the condition to be generated. If equal to Inf, the maximum length of conditions is limited only by the number of available predicates. |
min_support |
the minimum support of a condition to trigger the callback
function for it. The support of the condition is the relative frequency
of the condition in the dataset |
max_support |
the maximum support of a condition to trigger the callback
function for it. See argument |
method |
a character string indicating which contrast to compute.
One of |
alternative |
indicates the alternative hypothesis and must be one of
|
h0 |
a numeric value specifying the null hypothesis for the test. For
the |
conf_level |
a numeric value specifying the level of the confidence interval. The default value is 0.95. |
max_p_value |
the maximum p-value of a test for the pattern to be considered
significant. If the p-value of the test is greater than |
t_var_equal |
(used for the |
wilcox_exact |
(used for the |
wilcox_correct |
(used for the |
wilcox_tol_root |
(used for the |
wilcox_digits_rank |
(used for the |
max_results |
the maximum number of generated conditions to execute the
callback function on. If the number of found conditions exceeds
|
verbose |
a logical scalar indicating whether to print progress messages. |
threads |
the number of threads to use for parallel computation. |
A tibble with found patterns in rows. The following columns are always present:
condition |
the condition of the pattern as a character string
in the form |
support |
the support of the condition, i.e., the relative
frequency of the condition in the dataset |
var |
the name of the contrast variable. |
estimate |
the estimate value (see the underlying test. |
statistic |
the statistic of the selected test. |
p_value |
the p-value of the underlying test. |
n_x |
the number of rows in the sub-data corresponding to the condition. |
n_y |
the number of rows in the sub-data corresponding to the negation of the condition. |
conf_int_lo |
the lower bound of the confidence interval of the estimate. |
conf_int_hi |
the upper bound of the confidence interval of the estimate. |
alternative |
a character string indicating the alternative
hypothesis. The value must be one of |
method |
a character string indicating the method used for the test. |
comment |
a character string with additional information about the test (mainly error messages on failure). |
For the "t"
method, the following additional columns are also
present (see also t.test()
):
df |
the degrees of freedom of the t test. |
stderr |
the standard error of the mean difference. |
Michal Burda
dig_baseline_contrasts()
, dig_paired_baseline_contrasts()
,
dig()
, dig_grid()
,
stats::t.test()
, stats::wilcox.test()
, stats::var.test()
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