Compare variables between groups

The analyze function compare_vars() creates a layout element to summarize and compare one or more variables, using the S3 generic function s_summary() to calculate a list of summary statistics. A list of all available statistics for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table.

Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic.

compare_vars(
  lyt,
  vars,
  var_labels = vars,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  na_rm = TRUE,
  show_labels = "default",
  table_names = vars,
  section_div = NA_character_,
  .stats = c("n", "mean_sd", "count_fraction", "pval"),
  .stat_names_in = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_compare(x, ...)

# S3 method for class 'numeric'
s_compare(x, ...)

# S3 method for class 'factor'
s_compare(x, ...)

# S3 method for class 'character'
s_compare(x, ...)

# S3 method for class 'logical'
s_compare(x, ...)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

var_labels

(character)
variable labels.

na_str

(string)
string used to replace all NA or empty values in the output.

nested

(flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

...

additional arguments passed to s_compare(), including:

denom: (string) choice of denominator. Options are c("n", "N_col", "N_row"). For factor variables, can only be "n" (number of values in this row and column intersection).
.N_row: (numeric(1)) Row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting).
.N_col: (numeric(1)) Column-wise N (column count) for the full column being tabulated within.
verbose: (flag) Whether additional warnings and messages should be printed. Mainly used to print out information about factor casting. Defaults to TRUE. Used for character/factor variables only.

na_rm

(flag)
whether NA values should be removed from x prior to analysis.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

table_names

(character)
this can be customized in the case that the same vars are analyzed multiple times, to avoid warnings from rtables.

section_div

(string)
string which should be repeated as a section divider after each group defined by this split instruction, or NA_character_ (the default) for no section divider.

.stats

(character)
statistics to select for the table.

Options for numeric variables are: 'n', 'sum', 'mean', 'sd', 'se', 'mean_sd', 'mean_se', 'mean_ci', 'mean_sei', 'mean_sdi', 'mean_pval', 'median', 'mad', 'median_ci', 'quantiles', 'iqr', 'range', 'min', 'max', 'median_range', 'cv', 'geom_mean', 'geom_mean_ci', 'geom_cv', 'median_ci_3d', 'mean_ci_3d', 'geom_mean_ci_3d', 'pval'

Options for non-numeric variables are: 'n', 'count', 'count_fraction', 'count_fraction_fixed_dp', 'fraction', 'n_blq', 'pval_counts'

.stat_names_in

(character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Each element of the vector should be a name-value pair with name corresponding to a statistic specified in .stats and value the indentation for that statistic's row label.

x

(numeric)
vector of numbers we want to analyze.

Value

compare_vars() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout.

s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values.

Functions

compare_vars(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_compare(): S3 generic function to produce a comparison summary.
s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value.
s_compare(factor): Method for factor class. This uses the chi-squared test to calculate the p-value.
s_compare(character): Method for character class. This makes an automatic conversion to factor (with a warning) and then forwards to the method for factors.
s_compare(logical): Method for logical class. A chi-squared test is used. If missing values are not removed, then they are counted as FALSE.

Note

For factor variables, denom for factor proportions can only be n since the purpose is to compare proportions between columns, therefore a row-based proportion would not make sense. Proportion based on N_col would be difficult since we use counts for the chi-squared test statistic, therefore missing values should be accounted for as explicit factor levels.
If factor variables contain NA, these NA values are excluded by default. To include NA values set na.rm = FALSE and missing values will be displayed as an NA level. Alternatively, an explicit factor level can be defined for NA values during pre-processing via df_explicit_na() - the default na_level ("<Missing>") will also be excluded when na.rm is set to TRUE.
For character variables, automatic conversion to factor does not guarantee that the table will be generated correctly. In particular for sparse tables this very likely can fail. Therefore it is always better to manually convert character variables to factors during pre-processing.
For compare_vars(), the column split must define a reference group via ref_group so that the comparison is well defined.

Examples

# `compare_vars()` in `rtables` pipelines

## Default output within a `rtables` pipeline.
lyt <- basic_table() %>%
  split_cols_by("ARMCD", ref_group = "ARM B") %>%
  compare_vars(c("AGE", "SEX"))
build_table(lyt, tern_ex_adsl)
#>                                  ARM A        ARM B        ARM C   
#> ———————————————————————————————————————————————————————————————————
#> AGE                                                                
#>   n                                69           73           58    
#>   Mean (SD)                    34.1 (6.8)   35.8 (7.1)   36.1 (7.4)
#>   p-value (t-test)               0.1446                    0.8212  
#> SEX                                                                
#>   n                                69           73           58    
#>   F                            38 (55.1%)   40 (54.8%)   32 (55.2%)
#>   M                            31 (44.9%)   33 (45.2%)   26 (44.8%)
#>   p-value (chi-squared test)     1.0000                    1.0000  

## Select and format statistics output.
lyt <- basic_table() %>%
  split_cols_by("ARMCD", ref_group = "ARM C") %>%
  compare_vars(
    vars = "AGE",
    .stats = c("mean_sd", "pval"),
    .formats = c(mean_sd = "xx.x, xx.x"),
    .labels = c(mean_sd = "Mean, SD")
  )
build_table(lyt, df = tern_ex_adsl)
#>                      ARM A       ARM B       ARM C  
#> ————————————————————————————————————————————————————
#> Mean, SD           34.1, 6.8   35.8, 7.1   36.1, 7.4
#> p-value (t-test)    0.1176      0.8212              

# `s_compare.numeric`

## Usual case where both this and the reference group vector have more than 1 value.
s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE)
#> $n
#>  n 
#> 10 
#> 
#> $sum
#>      sum 
#> 51.27191 
#> 
#> $mean
#>     mean 
#> 5.127191 
#> 
#> $sd
#>       sd 
#> 1.226119 
#> 
#> $se
#>       se 
#> 0.387733 
#> 
#> $mean_sd
#>     mean       sd 
#> 5.127191 1.226119 
#> 
#> $mean_se
#>     mean       se 
#> 5.127191 0.387733 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>    4.250078    6.004304 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>     4.739458     5.514924 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>     3.901071     6.353310 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_ci_3d
#>        mean mean_ci_lwr mean_ci_upr 
#>    5.127191    4.250078    6.004304 
#> attr(,"label")
#> [1] "Mean (95% CI)"
#> 
#> $mean_pval
#>      p_value 
#> 3.353908e-07 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#>   median 
#> 5.024369 
#> 
#> $mad
#>           mad 
#> -4.440892e-16 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>      4.638779      5.862086 
#> attr(,"conf_level")
#> [1] 0.9785156
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $median_ci_3d
#>        median median_ci_lwr median_ci_upr 
#>      5.024369      4.638779      5.862086 
#> attr(,"label")
#> [1] "Median (95% CI)"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>      4.756763      5.549828 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#>       iqr 
#> 0.7930643 
#> 
#> $range
#>      min      max 
#> 2.725885 7.682557 
#> 
#> $min
#>      min 
#> 2.725885 
#> 
#> $max
#>      max 
#> 7.682557 
#> 
#> $median_range
#>   median      min      max 
#> 5.024369 2.725885 7.682557 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#>       cv 
#> 23.91406 
#> 
#> $geom_mean
#> geom_mean 
#>  4.985435 
#> 
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>    4.144463    5.997052 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#>  geom_cv 
#> 26.26258 
#> 
#> $geom_mean_ci_3d
#>   geom_mean mean_ci_lwr mean_ci_upr 
#>    4.985435    4.144463    5.997052 
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#> 
#> $pval
#> [1] 2.25779e-08
#> 

## If one group has not more than 1 value, then p-value is not calculated.
s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE)
#> $n
#>  n 
#> 10 
#> 
#> $sum
#>      sum 
#> 50.71578 
#> 
#> $mean
#>     mean 
#> 5.071578 
#> 
#> $sd
#>       sd 
#> 1.105832 
#> 
#> $se
#>        se 
#> 0.3496948 
#> 
#> $mean_sd
#>     mean       sd 
#> 5.071578 1.105832 
#> 
#> $mean_se
#>      mean        se 
#> 5.0715780 0.3496948 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>    4.280513    5.862643 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>     4.721883     5.421273 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>     3.965746     6.177410 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_ci_3d
#>        mean mean_ci_lwr mean_ci_upr 
#>    5.071578    4.280513    5.862643 
#> attr(,"label")
#> [1] "Mean (95% CI)"
#> 
#> $mean_pval
#>      p_value 
#> 1.511204e-07 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#>   median 
#> 5.260423 
#> 
#> $mad
#> mad 
#>   0 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>      3.529264      6.318293 
#> attr(,"conf_level")
#> [1] 0.9785156
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $median_ci_3d
#>        median median_ci_lwr median_ci_upr 
#>      5.260423      3.529264      6.318293 
#> attr(,"label")
#> [1] "Median (95% CI)"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>      4.024149      6.065057 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#>      iqr 
#> 2.040908 
#> 
#> $range
#>      min      max 
#> 3.300549 6.337320 
#> 
#> $min
#>      min 
#> 3.300549 
#> 
#> $max
#>     max 
#> 6.33732 
#> 
#> $median_range
#>   median      min      max 
#> 5.260423 3.300549 6.337320 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#>      cv 
#> 21.8045 
#> 
#> $geom_mean
#> geom_mean 
#>  4.952266 
#> 
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>    4.181833    5.864639 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#>  geom_cv 
#> 23.97201 
#> 
#> $geom_mean_ci_3d
#>   geom_mean mean_ci_lwr mean_ci_upr 
#>    4.952266    4.181833    5.864639 
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#> 
#> $pval
#> character(0)
#> 

## Empty numeric does not fail, it returns NA-filled items and no p-value.
s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE)
#> $n
#> n 
#> 0 
#> 
#> $sum
#> sum 
#>  NA 
#> 
#> $mean
#> mean 
#>   NA 
#> 
#> $sd
#> sd 
#> NA 
#> 
#> $se
#> se 
#> NA 
#> 
#> $mean_sd
#> mean   sd 
#>   NA   NA 
#> 
#> $mean_se
#> mean   se 
#>   NA   NA 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_ci_3d
#>        mean mean_ci_lwr mean_ci_upr 
#>          NA          NA          NA 
#> attr(,"label")
#> [1] "Mean (95% CI)"
#> 
#> $mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#> median 
#>     NA 
#> 
#> $mad
#> mad 
#>  NA 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $median_ci_3d
#>        median median_ci_lwr median_ci_upr 
#>            NA            NA            NA 
#> attr(,"label")
#> [1] "Median (95% CI)"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>            NA            NA 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#> iqr 
#>  NA 
#> 
#> $range
#> min max 
#>  NA  NA 
#> 
#> $min
#> min 
#>  NA 
#> 
#> $max
#> max 
#>  NA 
#> 
#> $median_range
#> median    min    max 
#>     NA     NA     NA 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#> cv 
#> NA 
#> 
#> $geom_mean
#> geom_mean 
#>       NaN 
#> 
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#> geom_cv 
#>      NA 
#> 
#> $geom_mean_ci_3d
#>   geom_mean mean_ci_lwr mean_ci_upr 
#>         NaN          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#> 
#> $pval
#> character(0)
#> 

# `s_compare.factor`

## Basic usage:
x <- factor(c("a", "a", "b", "c", "a"))
y <- factor(c("a", "b", "c"))
s_compare(x = x, .ref_group = y, .in_ref_col = FALSE)
#> $n
#> $n$n
#> n 
#> 5 
#> 
#> 
#> $count
#> $count$a
#> count 
#>     3 
#> 
#> $count$b
#> count 
#>     1 
#> 
#> $count$c
#> count 
#>     1 
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction_fixed_dp$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction_fixed_dp$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $fraction
#> $fraction$a
#>   num denom 
#>     3     5 
#> 
#> $fraction$b
#>   num denom 
#>     1     5 
#> 
#> $fraction$c
#>   num denom 
#>     1     5 
#> 
#> 
#> $n_blq
#> $n_blq$n_blq
#> n_blq 
#>     0 
#> 
#> 
#> $pval_counts
#> [1] 0.7659283
#> 

## Management of NA values.
x <- explicit_na(factor(c("a", "a", "b", "c", "a", NA, NA)))
y <- explicit_na(factor(c("a", "b", "c", NA)))
s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na_rm = TRUE)
#> $n
#> $n$n
#> n 
#> 5 
#> 
#> 
#> $count
#> $count$a
#> count 
#>     3 
#> 
#> $count$b
#> count 
#>     1 
#> 
#> $count$c
#> count 
#>     1 
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction_fixed_dp$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction_fixed_dp$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $fraction
#> $fraction$a
#>   num denom 
#>     3     5 
#> 
#> $fraction$b
#>   num denom 
#>     1     5 
#> 
#> $fraction$c
#>   num denom 
#>     1     5 
#> 
#> 
#> $n_blq
#> $n_blq$n_blq
#> n_blq 
#>     0 
#> 
#> 
#> $pval_counts
#> [1] 0.7659283
#> 
s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na_rm = FALSE)
#> $n
#> $n$n
#> n 
#> 7 
#> 
#> 
#> $count
#> $count$a
#> count 
#>     3 
#> 
#> $count$b
#> count 
#>     1 
#> 
#> $count$c
#> count 
#>     1 
#> 
#> $count$`<Missing>`
#> count 
#>     2 
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#>     count         p 
#> 3.0000000 0.4285714 
#> 
#> $count_fraction$b
#>     count         p 
#> 1.0000000 0.1428571 
#> 
#> $count_fraction$c
#>     count         p 
#> 1.0000000 0.1428571 
#> 
#> $count_fraction$`<Missing>`
#>     count         p 
#> 2.0000000 0.2857143 
#> 
#> 
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#>     count         p 
#> 3.0000000 0.4285714 
#> 
#> $count_fraction_fixed_dp$b
#>     count         p 
#> 1.0000000 0.1428571 
#> 
#> $count_fraction_fixed_dp$c
#>     count         p 
#> 1.0000000 0.1428571 
#> 
#> $count_fraction_fixed_dp$`<Missing>`
#>     count         p 
#> 2.0000000 0.2857143 
#> 
#> 
#> $fraction
#> $fraction$a
#>   num denom 
#>     3     7 
#> 
#> $fraction$b
#>   num denom 
#>     1     7 
#> 
#> $fraction$c
#>   num denom 
#>     1     7 
#> 
#> $fraction$`<Missing>`
#>   num denom 
#>     2     7 
#> 
#> 
#> $n_blq
#> $n_blq$n_blq
#> n_blq 
#>     0 
#> 
#> 
#> $pval_counts
#> [1] 0.9063036
#> 

# `s_compare.character`

## Basic usage:
x <- c("a", "a", "b", "c", "a")
y <- c("a", "b", "c")
s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = "x", verbose = FALSE)
#> $n
#> $n$n
#> n 
#> 5 
#> 
#> 
#> $count
#> $count$a
#> count 
#>     3 
#> 
#> $count$b
#> count 
#>     1 
#> 
#> $count$c
#> count 
#>     1 
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction_fixed_dp$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction_fixed_dp$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $fraction
#> $fraction$a
#>   num denom 
#>     3     5 
#> 
#> $fraction$b
#>   num denom 
#>     1     5 
#> 
#> $fraction$c
#>   num denom 
#>     1     5 
#> 
#> 
#> $n_blq
#> $n_blq$n_blq
#> n_blq 
#>     0 
#> 
#> 
#> $pval_counts
#> [1] 0.7659283
#> 

## Note that missing values handling can make a large difference:
x <- c("a", "a", "b", "c", "a", NA)
y <- c("a", "b", "c", rep(NA, 20))
s_compare(x,
  .ref_group = y, .in_ref_col = FALSE,
  .var = "x", verbose = FALSE
)
#> $n
#> $n$n
#> n 
#> 5 
#> 
#> 
#> $count
#> $count$a
#> count 
#>     3 
#> 
#> $count$b
#> count 
#>     1 
#> 
#> $count$c
#> count 
#>     1 
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction_fixed_dp$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction_fixed_dp$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $fraction
#> $fraction$a
#>   num denom 
#>     3     5 
#> 
#> $fraction$b
#>   num denom 
#>     1     5 
#> 
#> $fraction$c
#>   num denom 
#>     1     5 
#> 
#> 
#> $n_blq
#> $n_blq$n_blq
#> n_blq 
#>     0 
#> 
#> 
#> $pval_counts
#> [1] 0.7659283
#> 
s_compare(x,
  .ref_group = y, .in_ref_col = FALSE, .var = "x",
  na.rm = FALSE, verbose = FALSE
)
#> $n
#> $n$n
#> n 
#> 5 
#> 
#> 
#> $count
#> $count$a
#> count 
#>     3 
#> 
#> $count$b
#> count 
#>     1 
#> 
#> $count$c
#> count 
#>     1 
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count     p 
#>   3.0   0.6 
#> 
#> $count_fraction_fixed_dp$b
#> count     p 
#>   1.0   0.2 
#> 
#> $count_fraction_fixed_dp$c
#> count     p 
#>   1.0   0.2 
#> 
#> 
#> $fraction
#> $fraction$a
#>   num denom 
#>     3     5 
#> 
#> $fraction$b
#>   num denom 
#>     1     5 
#> 
#> $fraction$c
#>   num denom 
#>     1     5 
#> 
#> 
#> $n_blq
#> $n_blq$n_blq
#> n_blq 
#>     0 
#> 
#> 
#> $pval_counts
#> [1] 0.7659283
#> 

# `s_compare.logical`

## Basic usage:
x <- c(TRUE, FALSE, TRUE, TRUE)
y <- c(FALSE, FALSE, TRUE)
s_compare(x, .ref_group = y, .in_ref_col = FALSE)
#> $n
#> n 
#> 4 
#> 
#> $count
#> count 
#>     3 
#> 
#> $count_fraction
#>    count fraction 
#>     3.00     0.75 
#> 
#> $count_fraction_fixed_dp
#>    count fraction 
#>     3.00     0.75 
#> 
#> $fraction
#>   num denom 
#>     3     4 
#> 
#> $n_blq
#> n_blq 
#>     0 
#> 
#> $pval_counts
#> [1] 0.2702894
#> 

## Management of NA values.
x <- c(NA, TRUE, FALSE)
y <- c(NA, NA, NA, NA, FALSE)
s_compare(x, .ref_group = y, .in_ref_col = FALSE, na_rm = TRUE)
#> $n
#> n 
#> 2 
#> 
#> $count
#> count 
#>     1 
#> 
#> $count_fraction
#>    count fraction 
#>      1.0      0.5 
#> 
#> $count_fraction_fixed_dp
#>    count fraction 
#>      1.0      0.5 
#> 
#> $fraction
#>   num denom 
#>     1     2 
#> 
#> $n_blq
#> n_blq 
#>     0 
#> 
#> $pval_counts
#> [1] 0.3864762
#> 
s_compare(x, .ref_group = y, .in_ref_col = FALSE, na_rm = FALSE)
#> $n
#> n 
#> 3 
#> 
#> $count
#> count 
#>     1 
#> 
#> $count_fraction
#>     count  fraction 
#> 1.0000000 0.3333333 
#> 
#> $count_fraction_fixed_dp
#>     count  fraction 
#> 1.0000000 0.3333333 
#> 
#> $fraction
#>   num denom 
#>     1     3 
#> 
#> $n_blq
#> n_blq 
#>     0 
#> 
#> $pval_counts
#> [1] 0.1675463
#>

Arguments

Value

Functions

Note

See also

Examples