Continuous-outcome linear-model table

Builds APA-style summary tables from a series of simple linear models for one or many continuous outcomes selected with tidyselect syntax.

A single predictor is supplied with by, and each selected numeric outcome is fit as lm(outcome ~ by, ...). When by is categorical, the function returns a model-based mean-comparison table with fitted means by level derived from the linear model, plus an optional single difference for dichotomous predictors. When by is numeric, the table reports the slope and its confidence interval.

Multiple output formats are available via output: a printed ASCII table ("default"), a plain wide data.frame ("data.frame"), a raw long data.frame ("long"), or rendered outputs ("tinytable", "gt", "flextable", "excel", "clipboard", "word").

Usage

table_continuous_lm(
  data,
  select = dplyr::everything(),
  by,
  exclude = NULL,
  regex = FALSE,
  weights = NULL,
  vcov = c("classical", "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5"),
  contrast = c("auto", "none"),
  statistic = FALSE,
  p_value = TRUE,
  show_n = TRUE,
  show_weighted_n = FALSE,
  effect_size = c("none", "f2"),
  r2 = c("r2", "adj_r2", "none"),
  ci = TRUE,
  labels = NULL,
  ci_level = 0.95,
  digits = 2,
  fit_digits = 2,
  effect_size_digits = 2,
  decimal_mark = ".",
  output = c("default", "data.frame", "long", "tinytable", "gt", "flextable", "excel",
    "clipboard", "word"),
  excel_path = NULL,
  excel_sheet = "Linear models",
  clipboard_delim = "\t",
  word_path = NULL,
  verbose = FALSE
)

Arguments

data

A data.frame.

select

Outcome columns to include. If regex = FALSE, use tidyselect syntax or a character vector of column names (default: dplyr::everything()). If regex = TRUE, provide a regular expression pattern (character string).

by

A single predictor column. Accepts an unquoted column name or a single character column name. The predictor can be numeric, logical, character, or factor.

exclude

Columns to exclude from select. Supports tidyselect syntax and character vectors of column names.

regex

Logical. If FALSE (the default), uses tidyselect helpers. If TRUE, the select argument is treated as a regular expression.

weights

Optional case weights. Accepts NULL, an unquoted numeric column name, a single character column name, or a numeric vector of length nrow(data).

vcov

Variance estimator used for uncertainty estimates. One of "classical" (default), "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", or "HC5".

contrast

Contrast display for categorical predictors. One of:

"auto": show a single reference contrast only when by has exactly two levels.
"none": suppress difference columns for categorical predictors.

statistic

Logical. If TRUE, includes a test-statistic column in the wide and rendered outputs. Defaults to FALSE.

p_value

Logical. If TRUE, includes a p column in the wide and rendered outputs. Defaults to TRUE.

show_n

Logical. If TRUE, includes an unweighted n column in the wide and rendered outputs. Defaults to TRUE.

show_weighted_n

Logical. If TRUE and weights is supplied, includes a Weighted n column equal to the sum of case weights in the analytic sample. Defaults to FALSE.

effect_size

Character. Effect-size column to include in the wide and rendered outputs. One of "none" (the default) or "f2".

r2

Character. Fit statistic to include in the wide and rendered outputs. One of "r2" (the default), "adj_r2", or "none".

ci

Logical. If TRUE, includes contrast confidence-interval columns in the wide and rendered outputs when a single contrast is shown. Defaults to TRUE.

labels

An optional named character vector of outcome labels. Names must match column names in data. When NULL (the default), labels are auto-detected from variable attributes; if none are found, the column name is used.

ci_level

Confidence level for coefficient and model-based mean intervals (default: 0.95). Must be between 0 and 1 exclusive.

digits

Number of decimal places for descriptive values, regression coefficients, and test statistics (default: 2).

fit_digits

Number of decimal places for model-fit columns (R2 or adjusted R2) in wide and rendered outputs (default: 2).

effect_size_digits

Number of decimal places for effect-size columns (f2) in wide and rendered outputs (default: 2).

decimal_mark

Character used as decimal separator. Either "." (default) or ",".

output

Output format. One of:

"default": a printed ASCII table, returned invisibly
"data.frame": a plain wide data.frame
"long": a raw long data.frame
"tinytable" (requires tinytable)
"gt" (requires gt)
"flextable" (requires flextable)
"excel" (requires openxlsx2)
"clipboard" (requires clipr)
"word" (requires flextable and officer)

excel_path

File path for output = "excel".

excel_sheet

Sheet name for output = "excel" (default: "Linear models").

clipboard_delim

Delimiter for output = "clipboard" (default: "\t").

word_path

File path for output = "word".

verbose

Logical. If TRUE, prints messages about ignored non-numeric selected outcomes (default: FALSE).

Value

Depends on output:

"default": prints a styled ASCII table and returns the underlying long data.frame invisibly, with class "spicy_continuous_lm_table".
"data.frame": a plain wide data.frame with one row per outcome and numeric columns for means, optional contrasts, optional test statistics, model fit, and sample size.
"long": a raw data.frame with one block per outcome and columns describing estimated means, contrasts, test statistics, model fit, and sample size.
"tinytable": a tinytable object.
"gt": a gt_tbl object.
"flextable": a flextable object.
"excel" / "word": writes to disk and returns the file path.
"clipboard": copies the wide table and returns it invisibly.

Details

table_continuous_lm() is designed for article-style bivariate reporting: a single predictor supplied with by, and one simple model per selected continuous outcome. The model fit is always lm(outcome ~ by, ...).

For categorical predictors, the reported means are model-based fitted means for each level of by, and the reported contrasts are derived from the same fitted linear model.

Compared with table_continuous(), this function is the model-based companion for users who want to report bivariate mean comparisons in a linear-model framework. In practice, it is the better choice when you want heteroskedasticity-consistent standard errors (vcov = "HC*"), model fit statistics, or case weights via lm(..., weights = ...).

Effect size is reported as Cohen's f2, computed from the model R2 as R2 / (1 - R2). When vcov != "classical", standard errors, confidence intervals, and test statistics use the requested heteroskedasticity- consistent estimator, while R2 and adjusted R2 remain the ordinary least-squares fit statistics.

When weights is supplied, table_continuous_lm() fits weighted linear models using lm(..., weights = ...) and reports weighted model-based means or slopes accordingly. This is appropriate for case-weighted analyses and weighted article tables, but it is not a substitute for a full complex-survey design workflow.

In wide and rendered outputs, n always reports the unweighted analytic sample size used for each outcome. When show_weighted_n = TRUE, an additional Weighted n column reports the sum of case weights for the same analytic sample.

For dichotomous categorical predictors, the wide outputs report means in reference-level order and labels the contrast column explicitly as Delta (level2 - level1). For categorical predictors with more than two levels, no single contrast or contrast confidence interval is shown in the wide outputs; instead, the table reports level-specific means plus the overall F test when statistic = TRUE (or F(df1, df2) when the degrees of freedom are constant across outcomes).

Optional output engines require suggested packages:

tinytable for output = "tinytable"
gt for output = "gt"
flextable for output = "flextable"
flextable + officer for output = "word"
openxlsx2 for output = "excel"
clipr for output = "clipboard"

Examples

table_continuous_lm(
  sochealth,
  select = c(wellbeing_score, bmi),
  by = sex
)
#> Continuous outcomes by Sex
#> 
#>  Variable                      │ M (Female)  M (Male)  Δ (Male - Female) 
#> ───────────────────────────────┼─────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │   67.16      71.05          3.89        
#>  Body mass index               │   25.69      26.20          0.51        
#> 
#>  Variable                      │ 95% CI LL  95% CI UL      p   R²      n 
#> ───────────────────────────────┼─────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │   2.13       5.64     <.001  0.02  1200 
#>  Body mass index               │   0.09       0.93      .018  0.00  1188 

table_continuous_lm(
  sochealth,
  select = c(wellbeing_score, bmi),
  by = sex,
  output = "data.frame"
)
#>                                      Variable M (Female) M (Male)
#> wellbeing_score WHO-5 wellbeing index (0-100)   67.16194 71.04879
#> bmi                           Body mass index   25.68506 26.19685
#>                 Δ (Male - Female)  95% CI LL 95% CI UL            p          R²
#> wellbeing_score         3.8868576 2.12952733 5.6441879 1.548898e-05 0.015475137
#> bmi                     0.5117882 0.08879857 0.9347778 1.776254e-02 0.004728908
#>                    n
#> wellbeing_score 1200
#> bmi             1188

table_continuous_lm(
  sochealth,
  select = c(wellbeing_score, bmi),
  by = age,
  vcov = "HC3",
  ci = FALSE
)
#> Continuous outcomes by Age (years)
#> 
#>  Variable                      │  B        p   R²      n 
#> ───────────────────────────────┼─────────────────────────
#>  WHO-5 wellbeing index (0-100) │ 0.04   .176  0.00  1200 
#>  Body mass index               │ 0.04  <.001  0.02  1188 

table_continuous_lm(
  sochealth,
  select = c(wellbeing_score, bmi),
  by = sex,
  weights = weight,
  statistic = TRUE,
  effect_size = "f2",
  show_weighted_n = TRUE
)
#> Continuous outcomes by Sex
#> 
#>  Variable                      │ M (Female)  M (Male)  Δ (Male - Female) 
#> ───────────────────────────────┼─────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │   67.01      70.88          3.87        
#>  Body mass index               │   25.51      25.98          0.47        
#> 
#>  Variable                      │ 95% CI LL  95% CI UL   t        p   R²    f²  
#> ───────────────────────────────┼───────────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │   2.11       5.62     4.33  <.001  0.02  0.02 
#>  Body mass index               │   0.05       0.89     2.18   .030  0.00  0.00 
#> 
#>  Variable                      │    n  Weighted n 
#> ───────────────────────────────┼──────────────────
#>  WHO-5 wellbeing index (0-100) │ 1200     1196.47 
#>  Body mass index               │ 1188     1183.32 

# \donttest{
if (requireNamespace("tinytable", quietly = TRUE)) {
  table_continuous_lm(
    sochealth,
    select = c(wellbeing_score, bmi),
    by = sex,
    output = "tinytable"
  )
}
#> +-------------------------------+------------+----------+-------------------+------+------+-------+------+------+
#> | Variable                      | M (Female) | M (Male) | Δ (Male - Female) | 95% CI      | p     | R²   | n    |
#> +-------------------------------+------------+----------+-------------------+------+------+-------+------+------+
#> |                               |            |          |                   | LL   | UL   |       |      |      |
#> +===============================+============+==========+===================+======+======+=======+======+======+
#> | WHO-5 wellbeing index (0-100) | 67.16      | 71.05    | 3.89              | 2.13 | 5.64 | <.001 | 0.02 | 1200 |
#> +-------------------------------+------------+----------+-------------------+------+------+-------+------+------+
#> | Body mass index               | 25.69      | 26.20    | 0.51              | 0.09 | 0.93 | .018  | 0.00 | 1188 |
#> +-------------------------------+------------+----------+-------------------+------+------+-------+------+------+ 

if (requireNamespace("gt", quietly = TRUE)) {
  table_continuous_lm(
    sochealth,
    select = c(wellbeing_score, bmi),
    by = sex,
    output = "gt"
  )
}


  
        Variable
      
      
        M (Female)
      
      
        M (Male)
      
      
        Δ (Male - Female)
      
      
        95% CI
      
      
        p
      
      
        R²
      
      
        n
      
    

      
      
      
      LL
      UL
      
      
      
    
WHO-5 wellbeing index (0-100)
67.16
71.05
3.89
2.13
5.64
<.001
0.02
1200
Body mass index
25.69
26.20
0.51
0.09
0.93
.018
0.00
1188

if (requireNamespace("flextable", quietly = TRUE)) {
  table_continuous_lm(
    sochealth,
    select = c(wellbeing_score, bmi),
    by = sex,
    output = "flextable"
  )
}
Variable M (Female) M (Male) Δ (Male - Female) 95% CI p R² n
LL UL 
WHO-5 wellbeing index (0-100) 67.16 71.05 3.89 2.13 5.64 <.001 0.02 1200
Body mass index 25.69 26.20 0.51 0.09 0.93 .018 0.00 1188
# }

Variable	M (Female)	M (Male)	Δ (Male - Female)	95% CI		p	R²	n
				LL	UL
WHO-5 wellbeing index (0-100)	67.16	71.05	3.89	2.13	5.64	<.001	0.02	1200
Body mass index	25.69	26.20	0.51	0.09	0.93	.018	0.00	1188

Usage

Arguments

Value

Details

See also

Examples