expss (version 0.8.7)

tab_significance_options: Mark significant differences between columns of the table

Description

• significance_cpct conducts z-tests between column percent in the result of cro_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 cro_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 cro_cases. Results are calculated with the same formula as in chisq.test without continuity correction.

There are four type of comparisons which can be conducted simultaneously (argument compare_type). subtable provide comparison between all columns inside each subtable. previous_column is comparison of each column in the subtable with previous column. It is useful if columns are periods or waves of survey. first_column provide comparison of table first column with all other columns in the table. adjusted_first_column is comparison with first column but with adjustment for common base. It is useful if first column is total column and other columns are subgroup 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 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 there 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 entire custom table creation sequence.

• tab_last_add_sig_labels This function applies add_sig_labels to last calculated table - it add labels (letters by default) for significance to columns header. It may be useful if you want to combine 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, 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("-",
  "+"), keep = c("percent", "cases", "means", "sd", "bases"),
  total_marker = "#", total_row = 1, digits = get_expss_digits(),
  na_as_zero = FALSE, var_equal = FALSE, mode = c("replace", "append"))
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(),
  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, keep = c("cases",
  "bases"), total_marker = "#", total_row = 1,
  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, keep = c("cases",
  "bases"), total_marker = "#", total_row = 1,
  digits = get_expss_digits())
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())
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 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 than 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 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.

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 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. We can change this behavior by setting 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 difference with 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 difference with first column of the table. First mark means 'lower' (by default it is -) and the second means 'greater' (+).

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.

total_marker

character. Mark of total rows in table.

total_row

integer/character. In case of several totals per subtable it is number or name of total row for significance calculation.

digits

an integer indicating how much digits after decimal separator will be shown in the final table.

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.

label

character. Label for the statistic in the tab_*. Ignored if the mode is equals to replace.

x

table (class etable): result of cro_cpct with proportions and bases for significance_cpct, result of cro_mean_sd_n with means, standard deviations and valid N for significance_means, and result of cro_cases with counts and bases for significance_cases.

Value

tab_last_* functions return objects of class intermediate_table. Use tab_pivot to get final result - object of class etable. Other functions return object of class etable with marks of significant differences between columns.

See Also

cro_cpct, cro_cases, cro_mean_sd_n, tables, compare_proportions, compare_means, prop.test, t.test, chisq.test

Examples

# NOT RUN {
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"
)

mtcars_table = calculate(mtcars,
                   cro_cpct(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 = calculate(mtcars,
                   cro_mean_sd_n(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 = calculate(mtcars,
                   cro_cases(list(cyl, gear),
                            list(total(), vs, am))
                         )
                         
significance_cases(mtcars_cases)    

# 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()       
# }