Skip to contents

Regressions

Source: R/jmvtabreg.h.R
jmvtabreg.Rd

Regressions

Usage

jmvtabreg(
  data,
  outcome = NULL,
  predictors = NULL,
  tab_vars = NULL,
  wt = NULL,
  family = NULL,
  link = NULL,
  outcome_level = NULL,
  trials = NULL,
  effect = "auto",
  measure = "auto",
  empirical = TRUE,
  models = NULL,
  na = "drop_by_outcome",
  run_compare = FALSE,
  levels_order = NULL,
  levels_collapse = NULL,
  crosses = NULL,
  ref_levels = NULL,
  shape = NULL,
  multiplier = NULL,
  conf_level = 0.95,
  ci_method = "wald",
  stars = TRUE,
  color = "measure",
  color_signif = "grey_non_signif",
  display = "auto",
  n = "range",
  digits = "0",
  cleannames = TRUE,
  subtext = "",
  tab_theme = "light",
  wrap_rows = 35,
  wrap_cols = 15,
  export_format = "excel",
  exportExcel = FALSE,
  export_dir = "~/Documents",
  export_filename = "Reg_model",
  resetPath = FALSE,
  xl_check = FALSE,
  xl_replace = FALSE
)

Arguments

data

A data.frame.

outcome

The outcome variable(s). One model is built per outcome. Set each outcome's family (and, for a binomial outcome, its modelled level or number of trials) in the Model table.

predictors

The explanatory variables of the model. Factors are shown one line per level (the reference level as the neutral value); numeric predictors as a single line.

tab_vars

A grouping variable. The same model is fitted within each of its levels and the tables are stacked (like tab_vars for crosstables).

wt

A survey weight variable. Switches to design-based estimation (scale-invariant sandwich standard errors). Leave empty for unweighted results.

family

.

WHICH MEASURE THE MODEL ESTIMATES – the only argument that changes the model. A link IS a measure, so it takes the same words as measure. Chosen per outcome, in the Model table beside family: "auto" is the family's own (a logistic regression for a binary outcome, a linear one for a quantity, a Poisson one for a count), and "odds_ratio" / "ratio" / "difference" name the model whose coefficient IS that measure – on a binary outcome, the logistic fit, the modified Poisson (Zou 2004) and the identity-link additive-risk one. The picker only ever offers the links the chosen family can be fitted on.

outcome_level

.

trials

.

effect

WHERE THE NUMBER COMES FROM, once the model and the reported measure are fixed.

  • "auto": the coefficients when the reported measure IS the model's own, the model's predictions otherwise. Nobody needs to change this.

  • "conditional": read off the model's own coefficients.

  • "marginal": worked out from the model's predictions for every observed person, then averaged.

  • "at_reference": the same, at one profile (every other predictor at its reference level / mean).

measure

WHICH MEASURE IS REPORTED. It never changes the model: where it is not the measure the model estimates (see link), it is worked out from the model's predictions.

  • "auto": the model's own measure – follow from the left. On a prediction route it steps back to the outcome's own (a percentage reads as "x times as likely"), because a marginal odds ratio is a specialist quantity, asked for by name.

  • "odds_ratio" / "ratio" / "difference": the named measure, when the outcome's level can carry it. One it cannot says so, and lists what it does offer.

  • "raw_coefficient": the model's own coefficient, un-transformed — the log of the reported measure wherever that measure is multiplicative, and the additive estimate itself on a model that is already additive.

empirical

Show the crude, unadjusted, single-predictor effect beside each model effect — the bivariate association that IS the modelised quantity when there is a single predictor, so the gap between the two is what adjustment changed.

models

.

na

"drop_by_outcome" (default) fits every model OF ONE OUTCOME on the same complete cases, which is what makes the observed columns comparable to the model beside them and lets the likelihood-ratio comparison run; "drop_by_model" gives each model its own complete cases (a model on a different population then gets no observed effect); "drop_all" shares one population across every outcome as well.

run_compare

.

levels_order

.

levels_collapse

.

crosses

.

ref_levels

.

shape

.

multiplier

.

conf_level

The confidence level for intervals and the significance stars.

ci_method

Wald intervals (also the only option for weighted models) or profile-likelihood intervals (unweighted binomial / poisson only; needs MASS). A profile interval is an output of the likelihood at one confidence level, so it cannot be cached: every change refits the models.

stars

Show per-cell significance stars (the colours read the confidence interval either way).

color

WHAT the effect cells are coloured by. The colour LADDER always comes from what the column estimates (an odds ratio is read on the odds-ratio scale, a beta on the standardized-difference one), so what is left to choose is what the estimate is compared TO.

  • "measure": the effect's own size (compared to no effect).

  • "no": no colours.

  • "adjustment": how far the ADJUSTED effect moved from the crude one – needs empirical.

  • "between_groups": how far each group's effect is from the first group's – needs tab_vars.

color_signif

How significance interacts with the colours: observed size + grey out non-significant cells, colour only the guaranteed (error-adjusted) effect, ignore significance.

display

The estimate-cell LAYOUT (never the estimand: a display may fold in another quantity of the SAME fit, it can never change the fit). The same named layouts tab() offers, written in the same {} grammar: est is whatever the column estimates and base the level it sits on (an adjusted probability, an adjusted mean). "auto" keeps the built-in layout.

n

The column giving the number of observations behind each predictor level: "range" prints min-max when several models were fitted on different people, "min" the smallest count only, "no" no column at all.

digits

The minimum number of digits to print, as a single integer (0-6): each measure keeps its own precision where that is finer (an odds ratio reads at two decimals, a mean score at one). In R, tab_reg() also names one display field at a time, digits = c(ratio = 3).

cleannames

Strip numeric prefixes from factor level labels.

subtext

A free note printed below the table.

tab_theme

How the table is painted, in the results panel and in every export. "light" is the colour palette; "print_ready" says the same thing typographically — bold, italics, underlines and marks instead of blue and red — for a page that has no colour. See tab_css.

wrap_rows

.

wrap_cols

.

export_format

.

exportExcel

Press to export the table to the chosen format (the button label follows the format).

export_dir

The folder to save the exported file in. Blank or ~/Documents auto-detects your real Documents folder (a redirected D:/Documents or network Documents included). Type any other folder to override; a leading ~ there expands to your home folder.

export_filename

The bare file name, with NO extension (the chosen format adds it).

resetPath

Reset the folder and file name to their defaults (your Documents folder and "Regression").

xl_check

Excel export only: draw the model-check plots (reg_check_plots()) under each table in the workbook — the panels that apply to the fitted family (tab_xl(check = "auto")). Needs ggplot2 and gridExtra; without them the export says so and writes the table alone.

xl_replace

"Set to TRUE to overwrite an existing file."

Value

A results object containing:

results$html_tablea html
results$cache_statean image
results$compare_statean image