tab_significance_options {expss} | R Documentation |
Mark significant differences between columns in the table
Description
significance_cpct
conducts z-tests between column percent in the result of cross_cpct. Results are calculated with the same formula as in prop.test without continuity correction.significance_means
conducts t-tests between column means in the result of cross_mean_sd_n. Results are calculated with the same formula as in t.test.significance_cases
conducts chi-squared tests on the subtable of table with counts in the result of cross_cases. Results are calculated with the same formula as in chisq.test.significance_cell_chisq
compute cell chi-square test on table with column percent. The cell chi-square test looks at each table cell and tests whether it is significantly different from its expected value in the overall table. For example, if it is thought that variations in political opinions might depend on the respondent's age, this test can be used to detect which cells contribute significantly to that dependence. Unlike the chi-square test (significance_cases
), which is carried out on a whole set of rows and columns, the cell chi-square test is carried out independently on each table cell. Although the significance level of the cell chi-square test is accurate for any given cell, the cell tests cannot be used instead of the chi-square test carried out on the overall table. Their purpose is simply to point to the parts of the table where dependencies between row and column categories may exist.
For significance_cpct
and significance_means
there are three
type of comparisons which can be conducted simultaneously (argument
compare_type
):
subtable
provide comparisons between all columns inside each subtable.previous_column
is a comparison of each column of the subtable with the previous column. It is useful if columns are periods or survey waves.first_column
provides comparison the table first column with all other columns in the table.adjusted_first_column
is also comparison with the first column but with adjustment for common base. It is useful if the first column is total column and other columns are subgroups of this total. Adjustments are made according to algorithm in IBM SPSS Statistics Algorithms v20, p. 263. Note that with these adjustments t-tests between means are made with equal variance assumed (as withvar_equal = TRUE
).
By now there are no adjustments for multiple-response variables (results of mrset) in the table columns so significance tests are rather approximate for such cases. Also, there are functions for the significance testing in the sequence of custom tables calculations (see tables):
tab_last_sig_cpct
,tab_last_sig_means
andtab_last_sig_cpct
make the same tests as their analogs mentioned above. It is recommended to use them after appropriate statistic function: tab_stat_cpct, tab_stat_mean_sd_n and tab_stat_cases.tab_significance_options
With this function we can set significance options for the entire custom table creation sequence.tab_last_add_sig_labels
This function appliesadd_sig_labels
to the last calculated table - it adds labels (letters by default) for significance to columns header. It may be useful if you want to combine a table with significance with table without it.tab_last_round
This function rounds numeric columns in the last calculated table to specified number of digits. It is sometimes needed if you want to combine table with significance with table without it.
Usage
tab_significance_options(
data,
sig_level = 0.05,
min_base = 2,
delta_cpct = 0,
delta_means = 0,
correct = TRUE,
compare_type = "subtable",
bonferroni = FALSE,
subtable_marks = "greater",
inequality_sign = "both" %in% subtable_marks,
sig_labels = LETTERS,
sig_labels_previous_column = c("v", "^"),
sig_labels_first_column = c("-", "+"),
sig_labels_chisq = c("<", ">"),
keep = c("percent", "cases", "means", "sd", "bases"),
row_margin = c("auto", "sum_row", "first_column"),
total_marker = "#",
total_row = 1,
digits = get_expss_digits(),
na_as_zero = FALSE,
var_equal = FALSE,
mode = c("replace", "append"),
as_spss = FALSE
)
tab_last_sig_cpct(
data,
sig_level = 0.05,
delta_cpct = 0,
min_base = 2,
compare_type = "subtable",
bonferroni = FALSE,
subtable_marks = c("greater", "both", "less"),
inequality_sign = "both" %in% subtable_marks,
sig_labels = LETTERS,
sig_labels_previous_column = c("v", "^"),
sig_labels_first_column = c("-", "+"),
keep = c("percent", "bases"),
na_as_zero = FALSE,
total_marker = "#",
total_row = 1,
digits = get_expss_digits(),
as_spss = FALSE,
mode = c("replace", "append"),
label = NULL
)
tab_last_sig_means(
data,
sig_level = 0.05,
delta_means = 0,
min_base = 2,
compare_type = "subtable",
bonferroni = FALSE,
subtable_marks = c("greater", "both", "less"),
inequality_sign = "both" %in% subtable_marks,
sig_labels = LETTERS,
sig_labels_previous_column = c("v", "^"),
sig_labels_first_column = c("-", "+"),
keep = c("means", "sd", "bases"),
var_equal = FALSE,
digits = get_expss_digits(),
mode = c("replace", "append"),
label = NULL
)
tab_last_sig_cases(
data,
sig_level = 0.05,
min_base = 2,
correct = TRUE,
keep = c("cases", "bases"),
total_marker = "#",
total_row = 1,
digits = get_expss_digits(),
mode = c("replace", "append"),
label = NULL
)
tab_last_sig_cell_chisq(
data,
sig_level = 0.05,
min_base = 2,
subtable_marks = c("both", "greater", "less"),
sig_labels_chisq = c("<", ">"),
correct = TRUE,
keep = c("percent", "bases", "none"),
row_margin = c("auto", "sum_row", "first_column"),
total_marker = "#",
total_row = 1,
total_column_marker = "#",
digits = get_expss_digits(),
mode = c("replace", "append"),
label = NULL
)
tab_last_round(data, digits = get_expss_digits())
tab_last_add_sig_labels(data, sig_labels = LETTERS)
significance_cases(
x,
sig_level = 0.05,
min_base = 2,
correct = TRUE,
keep = c("cases", "bases"),
total_marker = "#",
total_row = 1,
digits = get_expss_digits()
)
significance_cell_chisq(
x,
sig_level = 0.05,
min_base = 2,
subtable_marks = c("both", "greater", "less"),
sig_labels_chisq = c("<", ">"),
correct = TRUE,
keep = c("percent", "bases", "none"),
row_margin = c("auto", "sum_row", "first_column"),
total_marker = "#",
total_row = 1,
total_column_marker = "#",
digits = get_expss_digits()
)
cell_chisq(cases_matrix, row_base, col_base, total_base, correct)
significance_cpct(
x,
sig_level = 0.05,
delta_cpct = 0,
min_base = 2,
compare_type = "subtable",
bonferroni = FALSE,
subtable_marks = c("greater", "both", "less"),
inequality_sign = "both" %in% subtable_marks,
sig_labels = LETTERS,
sig_labels_previous_column = c("v", "^"),
sig_labels_first_column = c("-", "+"),
keep = c("percent", "bases"),
na_as_zero = FALSE,
total_marker = "#",
total_row = 1,
digits = get_expss_digits(),
as_spss = FALSE
)
add_sig_labels(x, sig_labels = LETTERS)
significance_means(
x,
sig_level = 0.05,
delta_means = 0,
min_base = 2,
compare_type = "subtable",
bonferroni = FALSE,
subtable_marks = c("greater", "both", "less"),
inequality_sign = "both" %in% subtable_marks,
sig_labels = LETTERS,
sig_labels_previous_column = c("v", "^"),
sig_labels_first_column = c("-", "+"),
keep = c("means", "sd", "bases"),
var_equal = FALSE,
digits = get_expss_digits()
)
Arguments
data |
data.frame/intermediate_table for |
sig_level |
numeric. Significance level - by default it equals to |
min_base |
numeric. Significance test will be conducted if both
columns have bases greater or equal to |
delta_cpct |
numeric. Minimal delta between percent for which we mark
significant differences (in percent points) - by default it equals to zero.
Note that, for example, for minimal 5 percent point difference
|
delta_means |
numeric. Minimal delta between means for which we mark significant differences - by default it equals to zero. |
correct |
logical indicating whether to apply continuity correction when
computing the test statistic for 2 by 2 tables. Only for
|
compare_type |
Type of compare between columns. By default, it is
|
bonferroni |
logical. |
subtable_marks |
character. One of "greater", "both" or "less". By
deafult we mark only values which are significantly greater than some other
columns. For |
inequality_sign |
logical. FALSE if |
sig_labels |
character vector. Labels for marking differences between columns of subtable. |
sig_labels_previous_column |
a character vector with two elements. Labels
for marking a difference with the previous column. First mark means 'lower' (by
default it is |
sig_labels_first_column |
a character vector with two elements. Labels
for marking a difference with the first column of the table. First mark means
'lower' (by default it is |
sig_labels_chisq |
a character vector with two labels
for marking a difference with row margin of the table. First mark means
'lower' (by default it is |
keep |
character. One or more from "percent", "cases", "means", "bases", "sd" or "none". This argument determines which statistics will remain in the table after significance marking. |
row_margin |
character. One of values "auto" (default), "sum_row", or
"first_column". If it is "auto" we try to find total column in the subtable
by |
total_marker |
character. Total rows mark in the table. "#" by default. |
total_row |
integer/character. In the case of the several totals per subtable it is a number or name of total row for the significance calculation. |
digits |
an integer indicating how much digits after decimal separator |
na_as_zero |
logical. |
var_equal |
a logical variable indicating whether to treat the two variances as being equal. For details see t.test. |
mode |
character. One of |
as_spss |
a logical. FALSE by default. If TRUE, proportions which are equal to zero or one will be ignored. Also will be ignored categories with bases less than 2. |
label |
character. Label for the statistic in the |
total_column_marker |
character. Mark for total columns in the subtables. "#" by default. |
x |
table (class |
cases_matrix |
numeric matrix with counts size R*C |
row_base |
numeric vector with row bases, length R |
col_base |
numeric vector with col bases, length C |
total_base |
numeric single value, total base |
Value
tab_last_*
functions return objects of class
intermediate_table
. Use tab_pivot to get the final result -
etable
object. Other functions return etable
object with
significant differences.
See Also
cross_cpct, cross_cases, cross_mean_sd_n, tables, compare_proportions, compare_means, prop.test, t.test, chisq.test
Examples
data(mtcars)
mtcars = apply_labels(mtcars,
mpg = "Miles/(US) gallon",
cyl = "Number of cylinders",
disp = "Displacement (cu.in.)",
hp = "Gross horsepower",
drat = "Rear axle ratio",
wt = "Weight (lb/1000)",
qsec = "1/4 mile time",
vs = "Engine",
vs = c("V-engine" = 0,
"Straight engine" = 1),
am = "Transmission",
am = c("Automatic" = 0,
"Manual"=1),
gear = "Number of forward gears",
carb = "Number of carburetors"
)
## Not run:
mtcars_table = cross_cpct(mtcars,
list(cyl, gear),
list(total(), vs, am)
)
significance_cpct(mtcars_table)
# comparison with first column
significance_cpct(mtcars_table, compare_type = "first_column")
# comparison with first column and inside subtable
significance_cpct(mtcars_table,
compare_type = c("first_column", "subtable"))
# only significance marks
significance_cpct(mtcars_table, keep = "none")
# means
mtcars_means = cross_mean_sd_n(mtcars,
list(mpg, wt, hp),
list(total(), vs, cyl))
)
significance_means(mtcars_means)
# mark values which are less and greater
significance_means(mtcars_means, subtable_marks = "both")
# chi-squared test
mtcars_cases = cross_cases(mtcars,
list(cyl, gear),
list(total(), vs, am)
)
significance_cases(mtcars_cases)
# cell chi-squared test
# increase number of cases to avoid warning about chi-square approximation
mtcars2 = add_rows(mtcars, mtcars, mtcars)
tbl = cross_cpct(mtcars2, gear, am)
significance_cell_chisq(tbl)
# table with multiple variables
tbl = cross_cpct(mtcars2, list(gear, cyl), list(total(), am, vs))
significance_cell_chisq(tbl, sig_level = .0001)
# custom tables with significance
mtcars %>%
tab_significance_options(subtable_marks = "both") %>%
tab_cells(mpg, hp) %>%
tab_cols(total(), vs, am) %>%
tab_stat_mean_sd_n() %>%
tab_last_sig_means(keep = "means") %>%
tab_cells(cyl, gear) %>%
tab_stat_cpct() %>%
tab_last_sig_cpct() %>%
tab_pivot()
# Overcomplicated examples - we move significance marks to
# separate columns. Columns with statistics remain numeric
mtcars %>%
tab_significance_options(keep = "none",
sig_labels = NULL,
subtable_marks = "both",
mode = "append") %>%
tab_cols(total(), vs, am) %>%
tab_cells(mpg, hp) %>%
tab_stat_mean_sd_n() %>%
tab_last_sig_means() %>%
tab_last_hstack("inside_columns") %>%
tab_cells(cyl, gear) %>%
tab_stat_cpct() %>%
tab_last_sig_cpct() %>%
tab_last_hstack("inside_columns") %>%
tab_pivot(stat_position = "inside_rows") %>%
drop_empty_columns()
## End(Not run)