`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 with`var_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`

and`tab_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 applies`add_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.

```
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()
)

`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.

- data
data.frame/intermediate_table for

`tab_*`

functions.- sig_level
numeric. Significance level - by default it equals to

`0.05`

.- min_base
numeric. Significance test will be conducted if both columns have bases greater or equal to

`min_base`

. By default, it equals to`2`

.- 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_cpct`

should be equals 5, not 0.05.- 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

`significance_cases`

and`significance_cell_chisq`

. For details see chisq.test.`TRUE`

by default.- compare_type
Type of compare between columns. By default, it is

`subtable`

- comparisons will be conducted between columns of each subtable. Other possible values are:`first_column`

,`adjusted_first_column`

and`previous_column`

. We can conduct several tests simultaneously.- bonferroni
logical.

`FALSE`

by default. Should we use Bonferroni adjustment by the number of comparisons in each row?- subtable_marks
character. One of "greater", "both" or "less". By deafult we mark only values which are significantly greater than some other columns. For

`significance_cell_chisq`

default is "both".We can change this behavior by setting an argument to`less`

or`both`

.- inequality_sign
logical. FALSE if

`subtable_marks`

is "less" or "greater". Should we show`>`

or`<`

before significance marks of subtable comparisons.- 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

`v`

) and the second means greater (`^`

).- 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

`-`

) and the second means 'greater' (`+`

).- 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

`<`

) and the second means 'greater' (`>`

). Only for`significance_cell_chisq`

.- 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_column_marker`

. If the search is failed, we use the sum of each rows as row total. With "sum_row" option we always sum each row to get margin. Note that in this case result for multiple response variables in banners may be incorrect. With "first_column" option we use table first column as row margin for all subtables. In this case result for the subtables with incomplete bases may be incorrect. Only for`significance_cell_chisq`

.- 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.

`FALSE`

by default. Should we treat`NA`

's as zero cases?- var_equal
a logical variable indicating whether to treat the two variances as being equal. For details see t.test.

- mode
character. One of

`replace`

(default) or`append`

. In the first case the previous result in the sequence of table calculation will be replaced with result of significance testing. In the second case result of the significance testing will be appended to sequence of table calculation.- 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

`tab_*`

. Ignored if the`mode`

is equals to`replace`

.- total_column_marker
character. Mark for total columns in the subtables. "#" by default.

- x
table (class

`etable`

): result of cross_cpct with proportions and bases for`significance_cpct`

, result of cross_mean_sd_n with means, standard deviations and valid N for`significance_means`

, and result of cross_cases with counts and bases for`significance_cases`

.- 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

cross_cpct, cross_cases, cross_mean_sd_n, tables, compare_proportions, compare_means, prop.test, t.test, chisq.test