Package 'PhysioReport' reference manual

Title:	Clinical Report Generation for the x-biosignal Ecosystem
Description:	Parameterized, bilingual (Japanese/English), colorblind-safe clinical reports (gait, HRV, EMG) with normative overlays and MDC/MCID-annotated change for the x-biosignal physiological signal ecosystem.
Authors:	Yusuke Matsui [aut, cre]
Maintainer:	Yusuke Matsui <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.2
Built:	2026-10-03 13:42:32 UTC
Source:	https://github.com/x-biosignal/PhysioReport

Classify pre-to-post change against MDC and MCID thresholds

Description

Annotates a set of paired (pre, post) outcome scores against their Minimal Detectable Change (MDC) and Minimal Clinically Important Difference (MCID) thresholds, classifying each change as "no change" (within measurement error), "detectable (>MDC)" (a real change that clears measurement error) or "clinically meaningful (>MCID)". Thresholds are compared to the absolute change and are boundary-inclusive (a change exactly equal to the MDC is "detectable"). The direction of benefit is tracked separately so decrease-is-good metrics (pain, timed tests) are handled correctly.

Usage

annotateChange(pre, post, mdc, mcid, direction = "increase")
annotateChange(pre, post, mdc, mcid, direction = "increase")

Arguments

pre, post

Numeric vectors of pre- and post-intervention scores (one element per metric); may be named to label the metrics.

mdc, mcid

Numeric MDC / MCID thresholds (positive), a scalar recycled across metrics or a vector matching pre. Obtain the MDC from PhysioExperiment::mdc() (or PhysioMoCap::mdc()); the MCID is the instrument's anchor-based value.

direction

Benefit direction, "increase" (higher is better, default) or "decrease" (lower is better); a scalar applied to all metrics or a vector matching pre.

Value

A data.frame of class "change_annotation" with columns metric, pre, post, change (signed post - pre), improvement (signed so positive = beneficial), mdc, mcid, exceeds_mdc, exceeds_mcid, improved, direction and classification. A metric with a missing pre or post yields NA change and classification: missingness is propagated rather than silently reported as "no change".

References

de Vet HCW et al. (2006). Minimally important change determined by a visual method. Qual Life Res; Shrout & Fleiss (1979) for the SEM underlying MDC.

Examples

annotateChange(pre = c(fma = 20, pain = 7),
               post = c(fma = 31, pain = 4),
               mdc = c(5.2, 1.0), mcid = c(9, 2),
               direction = c("increase", "decrease"))
annotateChange(pre = c(fma = 20, pain = 7),
               post = c(fma = 31, pain = 4),
               mdc = c(5.2, 1.0), mcid = c(9, 2),
               direction = c("increase", "decrease"))

Adapt a longitudinal tracker to timeline data

Description

Converts a longitudinal-tracking result into the sessions / outcomes data frames consumed by longitudinalTimeline.

Usage

as.timelineData(x, ...)

## Default S3 method:
as.timelineData(x, ...)

## S3 method for class 'MSKLongitudinalTracker'
as.timelineData(x, ...)
as.timelineData(x, ...)

## Default S3 method:
as.timelineData(x, ...)

## S3 method for class 'MSKLongitudinalTracker'
as.timelineData(x, ...)

Arguments

x

A tracker object (e.g. a MSKLongitudinalTracker from PhysioMSKNet::mskLongitudinalTracker()).

...

Unused.

Value

A list with sessions and outcomes data frames.

Examples

# A tracker is any object carrying $metrics_table and $timepoint_labels;
# build a minimal one directly so the adapter runs without PhysioMSKNet.
tracker <- structure(
  list(metrics_table = data.frame(
         timepoint = rep(c("wk0", "wk6"), each = 2),
         muscle = rep(c("biceps", "triceps"), 2),
         metric_name = "rms",
         value = c(0.10, 0.08, 0.14, 0.12)),
       timepoint_labels = c("wk0", "wk6")),
  class = "MSKLongitudinalTracker")
td <- as.timelineData(tracker)
td$outcomes
# A tracker is any object carrying $metrics_table and $timepoint_labels;
# build a minimal one directly so the adapter runs without PhysioMSKNet.
tracker <- structure(
  list(metrics_table = data.frame(
         timepoint = rep(c("wk0", "wk6"), each = 2),
         muscle = rep(c("biceps", "triceps"), 2),
         metric_name = "rms",
         value = c(0.10, 0.08, 0.14, 0.12)),
       timepoint_labels = c("wk0", "wk6")),
  class = "MSKLongitudinalTracker")
td <- as.timelineData(tracker)
td$outcomes

Build an editable DOCX clinical report (officer)

Description

Renders a one-page clinical report to an editable Word document using officer (with flextable when available) — no LaTeX toolchain required. Called by renderClinicalReport for format = "docx"; usable directly.

Usage

buildDocxReport(
  data,
  template = c("gait", "hrv", "emg"),
  lang = c("en", "ja"),
  out
)
buildDocxReport(
  data,
  template = c("gait", "hrv", "emg"),
  lang = c("en", "ja"),
  out
)

Arguments

data

A report payload list (see renderClinicalReport): subject, figures (a named list of ggplots), change (an optional change_annotation) and provenance (an optional named list).

template

One of "gait", "hrv", "emg" (drives the localized title).

lang

"en" or "ja".

out

Output .docx path.

Value

out, invisibly.

Examples

# Writes an editable .docx via officer (a Suggests dependency) into tempdir().
if (requireNamespace("officer", quietly = TRUE)) {
  fig <- plotNormativeBand(NULL, NormativeModel(
    mean = sin(seq(0, pi, length.out = 101)), sd = rep(0.1, 101), time = 0:100))
  out <- tempfile(fileext = ".docx")
  buildDocxReport(list(subject = "P01", figures = list(gait_cycle = fig)),
                  template = "gait", out = out)
}
# Writes an editable .docx via officer (a Suggests dependency) into tempdir().
if (requireNamespace("officer", quietly = TRUE)) {
  fig <- plotNormativeBand(NULL, NormativeModel(
    mean = sin(seq(0, pi, length.out = 101)), sd = rep(0.1, 101), time = 0:100))
  out <- tempfile(fileext = ".docx")
  buildDocxReport(list(subject = "P01", figures = list(gait_cycle = fig)),
                  template = "gait", out = out)
}

Clinical gait report dashboard

Description

Composes a multi-panel clinical gait report: one waveform panel per gait variable (a colorblind-safe normative $\pm$ SD corridor from plotNormativeBand overlaid with the subject's Left and Right mean traces and optional event markers), followed by a gait-index dashboard (GDI / GPS / Movement Analysis Profile). Panels are laid out with patchwork.

Usage

clinicalGaitReport(
  pe,
  norm,
  events = NULL,
  sides = c("L", "R"),
  indices = TRUE
)
clinicalGaitReport(
  pe,
  norm,
  events = NULL,
  sides = c("L", "R"),
  indices = TRUE
)

Arguments

pe

A named list of per-variable side-split cycle waveforms: pe[[variable]] is a list with $left and $right, each a numeric cycle waveform or a points-by-cycles matrix (averaged across cycles). Variables are matched to norm$variables by name; kinetic variables (moments/power) can be included when norm carries their corridors.

norm

A gait_norm-like list (e.g. from PhysioGaitNorm::loadGaitNorm()) with $variables, row-named $mean and $sd matrices (variables x cycle points), and optionally $percent and $features (for the GDI).

events

Optional numeric vector of gait-cycle percentages at which to draw event markers (e.g. toe-off) on every waveform panel.

sides

Which sides to overlay (currently informational; both Left and Right are drawn).

indices

Logical; if TRUE (default) append the gait-index dashboard panel (requires PhysioMoCap and all norm variables present in pe); omitted with a warning if it cannot be computed.

Value

A patchwork composite (a ggplot-compatible object).

Examples

# clinicalGaitReport() composes panels with patchwork (a Suggests dependency);
# indices = FALSE keeps the gait-index panel (and PhysioMoCap) out of the way.
if (requireNamespace("patchwork", quietly = TRUE)) {
  np <- 101
  vars <- c("knee", "hip")
  ref <- sin(seq(0, pi, length.out = np))
  norm <- list(
    variables = vars,
    mean = matrix(rep(ref, 2), nrow = 2, byrow = TRUE,
                  dimnames = list(vars, NULL)),
    sd = matrix(0.1, nrow = 2, ncol = np, dimnames = list(vars, NULL)),
    percent = seq(0, 100, length.out = np))
  pe <- list(
    knee = list(left = ref + 0.05, right = ref - 0.05),
    hip  = list(left = ref,        right = ref))
  clinicalGaitReport(pe, norm, indices = FALSE)
}
# clinicalGaitReport() composes panels with patchwork (a Suggests dependency);
# indices = FALSE keeps the gait-index panel (and PhysioMoCap) out of the way.
if (requireNamespace("patchwork", quietly = TRUE)) {
  np <- 101
  vars <- c("knee", "hip")
  ref <- sin(seq(0, pi, length.out = np))
  norm <- list(
    variables = vars,
    mean = matrix(rep(ref, 2), nrow = 2, byrow = TRUE,
                  dimnames = list(vars, NULL)),
    sd = matrix(0.1, nrow = 2, ncol = np, dimnames = list(vars, NULL)),
    percent = seq(0, 100, length.out = np))
  pe <- list(
    knee = list(left = ref + 0.05, right = ref - 0.05),
    hip  = list(left = ref,        right = ref))
  clinicalGaitReport(pe, norm, indices = FALSE)
}

Gait index dashboard panel (GDI / GPS / MAP)

Description

A single bar panel summarising the gait indices for a clinical gait report: one bar per Gait Variable Score (the Movement Analysis Profile) plus the overall Gait Profile Score, coloured by deviation magnitude, with the Gait Deviation Index reported in the subtitle (GDI 100 = normative mean, each 10 points is one standard deviation, lower = more deviation).

Usage

gaitIndexDashboard(gdi, gps, map)
gaitIndexDashboard(gdi, gps, map)

Arguments

gdi

A gait_deviation_index (from PhysioMoCap::gaitDeviationIndex()) or a numeric GDI value. A named numeric vector (e.g. c(L = 82, R = 61)) reports a per-side GDI.

gps

A numeric Gait Profile Score, or a movement_analysis_profile (its $gps is used).

map

A movement_analysis_profile (from PhysioMoCap::movementAnalysisProfile()) or a named numeric vector of Gait Variable Scores.

Value

A ggplot object.

Examples

gaitIndexDashboard(gdi = 82,
                   gps = 8.4,
                   map = c(pelvis = 3, hip = 9, knee = 12, ankle = 6))
gaitIndexDashboard(gdi = 82,
                   gps = 8.4,
                   map = c(pelvis = 3, hip = 9, knee = 12, ankle = 6))

Longitudinal patient timeline (swimlane)

Description

Draws a longitudinal rehabilitation timeline: each outcome measure is a stacked lane showing its trajectory across sessions, intervention periods are shaded spans across all lanes, and points where the session-to-session change crosses the Minimal Detectable Change / Minimal Clinically Important Difference are marked (via annotateChange).

Usage

longitudinalTimeline(
  sessions,
  interventions = NULL,
  outcomes,
  mdc = NULL,
  mcid = NULL
)
longitudinalTimeline(
  sessions,
  interventions = NULL,
  outcomes,
  mdc = NULL,
  mcid = NULL
)

Arguments

sessions

A data.frame of session timepoints with a time column (numeric or Date) and an optional label; drawn as light session reference lines.

interventions

Optional data.frame of intervention periods with start and end columns (and an optional label); drawn as shaded spans behind every lane.

outcomes

A long data.frame of outcome measurements with columns time, metric and value (one lane per metric).

mdc, mcid

Optional MDC / MCID thresholds used to flag crossings: a scalar applied to all metrics, or a value named by metric. Both must be supplied to draw crossing markers.

Value

A ggplot object (faceted, one lane per metric).

Examples

sessions <- data.frame(time = 1:5)
outcomes <- data.frame(time = rep(1:5, 2),
  metric = rep(c("gait_speed", "fma"), each = 5),
  value = c(0.6, 0.7, 0.9, 1.0, 1.1, 20, 22, 28, 30, 33))
longitudinalTimeline(sessions, outcomes = outcomes,
  mdc = c(gait_speed = 0.1, fma = 5), mcid = c(gait_speed = 0.15, fma = 9))
sessions <- data.frame(time = 1:5)
outcomes <- data.frame(time = rep(1:5, 2),
  metric = rep(c("gait_speed", "fma"), each = 5),
  value = c(0.6, 0.7, 0.9, 1.0, 1.1, 20, 22, 28, 30, 33))
longitudinalTimeline(sessions, outcomes = outcomes,
  mdc = c(gait_speed = 0.1, fma = 5), mcid = c(gait_speed = 0.15, fma = 9))

Normative reference model (mean / SD corridor)

Description

Builds a reusable description of a normative reference — the expected mean and sd of a measurement, optionally stratified by covariates such as age, sex or task. A model can be scalar (one mean/sd), a waveform (per-timepoint mean/sd vectors, e.g. a 101-point gait-cycle corridor), a stratified table (a data.frame with the strata columns plus mean/sd and an optional time column), or a lookup function function(age, sex, task) returning a list(mean, sd, time). The table/function forms let a normative database drive the model; when no database is available an inline scalar/waveform/table spec degrades gracefully to the same interface.

Usage

NormativeModel(
  mean,
  sd = NULL,
  by = c("age", "sex", "task"),
  source = NULL,
  time = NULL
)
NormativeModel(
  mean,
  sd = NULL,
  by = c("age", "sex", "task"),
  source = NULL,
  time = NULL
)

Arguments

mean

Numeric scalar or vector (the reference mean / waveform), OR a data.frame of stratified norms (must contain mean and sd columns, plus the by strata columns and an optional time column), OR a lookup function(age, sex, task) returning list(mean=, sd=, time=).

sd

Numeric scalar or vector of the same length as mean (recycled from length 1). Required for the numeric form; ignored for the table / function forms.

by

Character vector naming the stratification columns/arguments (default c("age", "sex", "task")); for a data.frame the defaults are narrowed to the columns actually present.

source

Optional character string describing the normative source (citation), used as the default plot title.

time

Optional numeric vector (same length as mean) giving the x-axis (e.g. % gait cycle) for a waveform model.

Value

An object of class "NormativeModel".

Examples

# scalar norm (e.g. comfortable gait speed for a stratum)
m <- NormativeModel(mean = 1.30, sd = 0.15, source = "Bohannon 1997")
normativeZScore(1.00, m)

# waveform norm (101-point knee-flexion corridor)
wf <- NormativeModel(mean = sin(seq(0, pi, length.out = 101)),
                     sd = rep(0.1, 101), time = 0:100)
# scalar norm (e.g. comfortable gait speed for a stratum)
m <- NormativeModel(mean = 1.30, sd = 0.15, source = "Bohannon 1997")
normativeZScore(1.00, m)

# waveform norm (101-point knee-flexion corridor)
wf <- NormativeModel(mean = sin(seq(0, pi, length.out = 101)),
                     sd = rep(0.1, 101), time = 0:100)

Normative z-score of an observation

Description

Standardizes an observed value (or waveform) against a NormativeModel: $z = (value - mean) / sd$ , element-wise. A scalar model recycles across a vector of observations; a waveform model returns a per-timepoint z of the same length as the observation.

Usage

normativeZScore(value, model, age = NULL, sex = NULL, task = NULL)
normativeZScore(value, model, age = NULL, sex = NULL, task = NULL)

Arguments

value

Numeric observation. A scalar, a vector of scalars (scored against a scalar model), or a waveform matching a waveform model's length.

model

A NormativeModel.

age, sex, task

Optional strata forwarded to the model to select the applicable norm.

Value

Numeric z-score(s), matching the length of value (or the waveform length).

Examples

m <- NormativeModel(mean = 1.30, sd = 0.15)
normativeZScore(c(1.00, 1.30, 1.60), m)
m <- NormativeModel(mean = 1.30, sd = 0.15)
normativeZScore(c(1.00, 1.30, 1.60), m)

Bilingual (Japanese / English) label lookup for reports

Description

Resolves a report label key to its localized string. Unknown keys fall back to the key itself, so partial dictionaries never break a report. Labels live in inst/extdata/labels.csv (UTF-8), keeping the R source ASCII.

Usage

physioLabel(key, lang = c("en", "ja"))
physioLabel(key, lang = c("en", "ja"))

Arguments

key

Character label key (e.g. "bias").

lang

Language: "en" (default) or "ja".

Value

The localized label string.

Examples

physioLabel("bias")
physioLabel("bias", "ja")
physioLabel("bias")
physioLabel("bias", "ja")

Bland-Altman agreement plot

Description

Draws a Bland-Altman plot (mean vs. difference of paired measurements) with bias and limit-of-agreement reference lines. The statistics come from PhysioExperiment::blandAltman(), so the plotted lines exactly match the computed bias and limits of agreement.

Usage

plotBlandAltman(x, y, confidence = 0.95)
plotBlandAltman(x, y, confidence = 0.95)

Arguments

x, y

Numeric vectors of paired measurements (e.g. two methods, or baseline vs follow-up).

confidence

Confidence level for the limits of agreement (default 0.95).

Value

A ggplot object.

Examples

plotBlandAltman(c(1, 2, 3, 4, 5), c(1.1, 1.9, 3.2, 3.8, 5.1))
plotBlandAltman(c(1, 2, 3, 4, 5), c(1.1, 1.9, 3.2, 3.8, 5.1))

Forest-style plot of annotated pre-to-post changes

Description

Draws one row per metric showing the signed change as a point, with the $\pm$ MDC and $\pm$ MCID thresholds as symmetric reference bands around the no-change line, coloured by the change classification.

Usage

plotChangeAnnotated(changes)
plotChangeAnnotated(changes)

Arguments

changes

A "change_annotation" object from annotateChange.

Value

A ggplot object.

Examples

ch <- annotateChange(c(fma = 20, pain = 7), c(fma = 31, pain = 4),
                     mdc = c(5.2, 1.0), mcid = c(9, 2),
                     direction = c("increase", "decrease"))
plotChangeAnnotated(ch)
ch <- annotateChange(c(fma = 20, pain = 7), c(fma = 31, pain = 4),
                     mdc = c(5.2, 1.0), mcid = c(9, 2),
                     direction = c("increase", "decrease"))
plotChangeAnnotated(ch)

Plot an observation against a normative band

Description

Draws a normative corridor as graded $\pm1$ SD and $\pm2$ SD ribbons around the reference median, overlays the observed trajectory, and annotates the z-score. Uses the ecosystem colorblind-safe palette and report theme.

Usage

plotNormativeBand(
  observed = NULL,
  model,
  bands = c(1, 2),
  time_axis = NULL,
  annotate_z = TRUE,
  age = NULL,
  sex = NULL,
  task = NULL,
  ...
)
plotNormativeBand(
  observed = NULL,
  model,
  bands = c(1, 2),
  time_axis = NULL,
  annotate_z = TRUE,
  age = NULL,
  sex = NULL,
  task = NULL,
  ...
)

Arguments

observed

Numeric observation (a waveform, or a scalar broadcast across the corridor). NULL draws just the corridor (no observed line and no z annotation), so callers can overlay their own traces (e.g. left/right); for a scalar model this requires time_axis to set the x extent.

model

A NormativeModel.

bands

Numeric vector of SD multiples to shade (default c(1, 2)); one geom_ribbon layer per band, widest drawn first.

time_axis

Optional numeric x-axis; defaults to the model's time or the sample index.

annotate_z

Logical; if TRUE (default) annotate the (max absolute) z-score.

age, sex, task

Optional strata forwarded to the model.

...

Currently unused (reserved for future styling arguments).

Value

A ggplot object.

Examples

set.seed(1)
wf <- NormativeModel(mean = sin(seq(0, pi, length.out = 101)),
                     sd = rep(0.1, 101), time = 0:100)
obs <- sin(seq(0, pi, length.out = 101)) + rnorm(101, 0, 0.05)
plotNormativeBand(obs, wf)
set.seed(1)
wf <- NormativeModel(mean = sin(seq(0, pi, length.out = 101)),
                     sd = rep(0.1, 101), time = 0:100)
obs <- sin(seq(0, pi, length.out = 101)) + rnorm(101, 0, 0.05)
plotNormativeBand(obs, wf)

Multi-domain outcome radar (spider) plot

Description

Plots a rehabilitation outcome profile across domains as a radar chart, with an optional normative reference polygon and multi-timepoint overlay. Uses the ecosystem colorblind-safe palette and report theme.

Usage

plotOutcomeRadar(
  profile,
  reference = NULL,
  normalize = c("percent", "z"),
  domains = NULL
)
plotOutcomeRadar(
  profile,
  reference = NULL,
  normalize = c("percent", "z"),
  domains = NULL
)

Arguments

profile

Domain scores: a named numeric vector (one timepoint), a matrix or data.frame with domains as columns and timepoints as rows, or a MSKFunctionalOutcome (from PhysioMSKNet::mskPredictFunctionalOutcome()), whose aggregate ROM / strength / function become the domains.

reference

Optional normative reference: a named numeric of per-domain means (for "percent"), or a data.frame with domain, mean and sd columns (required for "z"). Drawn as a reference polygon.

normalize

"percent" (each domain as a percentage of its reference mean; reference polygon at 100) or "z" (z-score versus the reference; reference polygon centered at 0).

domains

Optional character vector selecting and ordering the domains.

Value

A ggplot object (a coord_polar radar).

Examples

plotOutcomeRadar(c(pain = 3, balance = 7, gait = 6, strength = 5))
plotOutcomeRadar(c(pain = 3, balance = 7, gait = 6, strength = 5))

Single-case (SCED) plot with phase overlays

Description

Plots a single-subject time series with SCED-standard overlays: shaded phase panels, phase-change reference lines, per-phase mean lines, the baseline $\pm$ 2SD band, and per-phase split-middle celeration (trend) lines.

Usage

plotSCED(data, phase, overlays = c("mean", "2SD", "celeration"))
plotSCED(data, phase, overlays = c("mean", "2SD", "celeration"))

Arguments

data

Numeric vector of observations, in session order.

phase

Vector of phase labels (e.g. "A"/"B") the same length as data; phases are drawn in order of first appearance.

overlays

Which overlays to draw: any of "mean" (per-phase mean lines), "2SD" (baseline mean $\pm$ 2SD band) and "celeration" (split-middle trend lines). Default: all three.

Value

A ggplot object.

References

Kratochwill TR et al. (2010). Single-case designs technical documentation (What Works Clearinghouse).

Examples

plotSCED(c(2, 3, 2, 3, 6, 7, 8, 7), rep(c("A", "B"), each = 4))
plotSCED(c(2, 3, 2, 3, 6, 7, 8, 7), rep(c("A", "B"), each = 4))

Render a parameterized clinical report (PDF / DOCX / HTML)

Description

Renders a one-page, bilingual, colorblind-safe clinical report for a gait, HRV or EMG assessment. PDF and HTML are produced from a parameterized Quarto template (quarto + the Quarto CLI); DOCX is produced by an editable officer path that needs no LaTeX toolchain (buildDocxReport).

Usage

renderClinicalReport(
  data,
  template = c("gait", "hrv", "emg"),
  format = c("pdf", "docx", "html"),
  lang = c("en", "ja"),
  out = NULL
)
renderClinicalReport(
  data,
  template = c("gait", "hrv", "emg"),
  format = c("pdf", "docx", "html"),
  lang = c("en", "ja"),
  out = NULL
)

Arguments

data

A report payload list. Recognized elements: subject (a label), figures (a named list of ggplot panels — build them with the colorblind-safe primitives, e.g. plotNormativeBand / clinicalGaitReport), change (an optional annotateChange result rendered as an MDC/MCID table) and provenance (an optional named list for the footer, e.g. from metadata(pe)).

template

Report template: "gait" (default), "hrv" or "emg".

format

Output format: "pdf" (default), "docx" or "html".

lang

"en" (default) or "ja"; drives the localized headings via physioLabel.

out

Output file path; defaults to a temp file named for the template and format.

Value

The output path, invisibly.

Examples

# The LaTeX-free docx path needs only officer (a Suggests dependency); the
# pdf / html paths additionally need the quarto package and the Quarto CLI.
if (requireNamespace("officer", quietly = TRUE)) {
  fig <- plotNormativeBand(NULL,
    NormativeModel(sin(seq(0, pi, length.out = 101)), rep(0.1, 101), time = 0:100))
  renderClinicalReport(list(subject = "P01", figures = list(gait_cycle = fig)),
                       template = "gait", format = "docx", lang = "ja")
}
# The LaTeX-free docx path needs only officer (a Suggests dependency); the
# pdf / html paths additionally need the quarto package and the Quarto CLI.
if (requireNamespace("officer", quietly = TRUE)) {
  fig <- plotNormativeBand(NULL,
    NormativeModel(sin(seq(0, pi, length.out = 101)), rep(0.1, 101), time = 0:100))
  renderClinicalReport(list(subject = "P01", figures = list(gait_cycle = fig)),
                       template = "gait", format = "docx", lang = "ja")
}

The x-biosignal report theme

Description

Thin wrapper returning the shared colorblind-safe ggplot2 theme from PhysioExperiment, so every report uses consistent, accessible styling.

Usage

reportTheme()
reportTheme()

Value

A ggplot2 theme (see PhysioExperiment::theme_physio).

Examples

reportTheme()
reportTheme()

Single-case (SCED) effect-size summary

Description

Summarises the nonoverlap / trend effect sizes for a two-phase (A/B) single- case series, delegating to the peer-reviewed estimators in PhysioClinStats: the Percentage of Non-overlapping Data (as a percentage), the Nonoverlap of All Pairs, and the baseline-trend-corrected Tau-U in its bounded tau-b ("scan") form.

Usage

scedStats(data, phase, improvement = c("increase", "decrease"))
scedStats(data, phase, improvement = c("increase", "decrease"))

Arguments

data

Numeric vector of observations, in session order.

phase

Vector of phase labels the same length as data; the first two phases (in order of appearance) are taken as A (baseline) and B (intervention).

improvement

"increase" (default) if higher scores are better, or "decrease" if lower scores are the goal.

Value

A data.frame of class "sced_stats" with columns metric and estimate (PND in [0, 100], NAP in [0, 1], Tau-U in [-1, 1]).

References

Parker RI, Vannest KJ (2011). Tau-U. Behavior Therapy; Scruggs et al. (1987) PND; Parker & Vannest (2009) NAP.

Examples

# scedStats() delegates to PhysioClinStats (a Suggests dependency)
if (requireNamespace("PhysioClinStats", quietly = TRUE)) {
  scedStats(c(2, 3, 2, 3, 6, 7, 8, 7), rep(c("A", "B"), each = 4))
}
# scedStats() delegates to PhysioClinStats (a Suggests dependency)
if (requireNamespace("PhysioClinStats", quietly = TRUE)) {
  scedStats(c(2, 3, 2, 3, 6, 7, 8, 7), rep(c("A", "B"), each = 4))
}

Package 'PhysioReport'

Help Index

Classify pre-to-post change against MDC and MCID thresholds

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Adapt a longitudinal tracker to timeline data

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Build an editable DOCX clinical report (officer)

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Clinical gait report dashboard

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Gait index dashboard panel (GDI / GPS / MAP)

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Longitudinal patient timeline (swimlane)

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Normative reference model (mean / SD corridor)

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Normative z-score of an observation

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Bilingual (Japanese / English) label lookup for reports

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Bland-Altman agreement plot

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Forest-style plot of annotated pre-to-post changes

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Plot an observation against a normative band

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