Package 'libdr'

Title: Support Functions used by the LIBDR LCMM Analysis
Description: This package provides support functions frequently used by Constantine et al. for their paper, "Large-scale clustering of longitudinal faecal calprotectin and C-reactive protein profiles in inflammatory bowel disease". These functions are primarily used to manipulate data and visualise findings.
Authors: Nathan Constantine-Cooke [aut, cre] , Catalina Vallejos [aut]
Maintainer: Nathan Constantine-Cooke <[email protected]>
License: GPL (>= 3)
Version: 1.0.0
Built: 2024-11-15 15:32:52 UTC
Source: https://github.com/VallejosGroup/Lothian-IBDR

Help Index

Build DT::datatable objects from matrix of fit statistics

Description

Build DT::datatable objects from matrix of fit statistics

Usage

buildDT(hlme.metrics)

Arguments

hlme.metrics

A data frame or matrix of model fit metrics for different number of assumed clusters (G)

Create co-clustering matrix for FCAL and CRP models

Description

Create co-clustering matrix for FCAL and CRP models

Usage

compareClustering(G, models.fcal, models.crp, cutoff = FALSE, threshold = 0.8)

Arguments

G

Number of clusters assumed by both models
models.fcal

List holding all hlme objects created from FCAL data
models.crp

List holding all hlme objects created from CRP data
cutoff

Logical, should only subjects assigned posterior cluster membership probabilities above a given threshold for both models be included?
threshold

Numeric. The posterior probability of cluster membership cutoff required for a subject to be included in the plot. Only used if cutoff = TRUE

Produce descriptive analysis of categorical variables

Description

Produce descriptive analysis of categorical variables

Usage

describe_cat(vars, data)

Arguments

vars

Character vector giving the names of categorical variables
data

Data frame (or tibble) holding the data of interest

Produce a heatmap of LCMM cluster assignments across multiple lcmm::hlme fit objects

Description

Produce a heatmap of LCMM cluster assignments across multiple lcmm::hlme fit objects

Usage

lcmmHeatmap(
  models,
  cores = NULL,
  png = TRUE,
  pdf = FALSE,
  file.name = "heatmap",
  return.data = FALSE
)

Arguments

models

list containing objects generated by lcmm::hlme.
cores

Numeric. Number of CPU cores to use. Defaults to maximum available.
png

Logical. If TRUE, the heatmap will be saved to file as a PNG.
pdf

Logical. If TRUE, the heatmap will be saved to file as a PDF.
file.name

Character. File name (without extension) for saved heatmap files. Defaults to "heatmap". Not used if png and pdf are both FALSE.
return.data

Logical. If TRUE, the averaged co-cluster matrix will be returned.

Produce table of model fit metrics (AIC, BIC, Log-likelihood) for LCMMs

Description

Produce table of model fit metrics (AIC, BIC, Log-likelihood) for LCMMs

Usage

makeMetrics(Gs, models)

Arguments

Gs

Vector indicating which values of G should metrics be calculated for.
models

List containing LCMM model fits

Forest plot of multinomial logistic regression model

Description

Forest plot of multinomial logistic regression model

Usage

mlrPlot(dat, var, class = "class_combined", prob = "probmax", minprob = 0.5)

Arguments

dat

Data frame holding covariate and cluster assignment data.
var

Character. The name of the covariate(s) of interest. If given as a character vector then a multivariate model is fitted using the specified covariates.
class

Character. The name of the class assignment variable. Assumed to be "class_combined" if not manually specified.
prob

Character. The name of the variable which gives posterior probabilities for cluster membership. Assumed to be "probmax" if not manually specified.
minprob

Numeric. The minimum posterior probability for cluster membership required for a subject to be included in the analysis. Assumed to be 0.5 if not manually specified

Percentage bar plots for categorical data

Description

Percentage bar plots for categorical data

Usage

plotCat(dat, var, class = "class_combined")

Arguments

dat

Data frame holding categorical and cluster assignment data.
var

Character. The name of the categorical variable of interest.
class

Character. The name of the class assignment variable. Assumed to be "class_combined" if not manually specified

Value

A patchwork object

Rank clusters by the area under their mean trajectories

Description

Rank clusters by the area under their mean trajectories

Usage

rankCumulative(model, tmax = 7, var.time = "calpro_time")

Arguments

model

An hlme object
tmax

Numeric. Maximum observation period. Defaults to 10
var.time

Character. Name of variable used for time. Either "calpro_time" or "crp_time" Also used to determine if the LCMM has been fitted for FCAL or CRP.

Value

A data frame with columns for the original cluster number (Original), the new rank (New), and the area calculated via the trapezoidal rule (Area).

relabel alluvial class labels ensuring consistency.

Description

Relabel class labels in an alluvial data frame to ensure class labels are consistent with the class labels used models assuming fewer classes

Usage

reLabel(new.G, alluvial.df)

Arguments

new.G

The number of classes assumed by the LCMM which is now subject to class relabelling.
alluvial.df

Data frame containing class assignments for subjects for differing G

Spaghetti plots of each class

Description

Spaghetti plots of each class

Usage

spaghettiPlot(
  data,
  models,
  G,
  log = TRUE,
  indi = FALSE,
  multi = TRUE,
  tmax = 10,
  ylim = NULL,
  column = FALSE,
  pprob.cutoff = NA,
  sizes = FALSE,
  mapping = NULL,
  save = FALSE,
  knots = FALSE,
  knot.type = "quantiles",
  n.knots = 3,
  grbf = FALSE,
  l = 1,
  color = NULL,
  clusters = FALSE,
  var.time = "calpro_time"
)

Arguments

data

Data frame used to fit the LCMM(s).
models

A list containing hlme{lcmm} S3 objects
G

Numeric. How many classes does the model assume?
log

Logical. Should plots be on log scale? Defaults to TRUE.
indi

Logical. Should separate plots for each class be generated? Defaults to FALSE.
multi

Logical. Should sub-plots for each class be plotted alongside each other? Defaults to TRUE.
tmax

Numeric. Maximum observation period. Defaults to 10
ylim

Character. What should the y-axis of the plots be scaled to fit? Either NULL (default), "data", "pred", or "conf". The default uses pre-determined values for FCAL and CRP
column

Logical. Should all sub-plots be in a single column? Defaults to FALSE (two columns)
pprob.cutoff

Posterior probability cut-off for subjects to be included as trajectories
sizes

Logical. Should class sizes be given as plot titles?
mapping

Numeric vector Giving the new ordering of sub-plots. One must take into account plots are generated by column - not row. If not specified then the default class numbers will be used.
save

Logical. Should sub-figure labels be generated?
knots

Logical. If TRUE, knot locations will be plotted as vertical lines. Defaults to FALSE
knot.type

Character. How are knots placed? Either "quantiles" (default) or "equal" (equally spaced). Not used if knots = FALSE.
n.knots

Numeric integer giving the number of knots used. Defaults to 3. Not used if knots = FALSE.
grbf

Logical. If TRUE, then the model is assumed to have been fitted using two Gaussian Radial basis functions. Defaults to FALSE (using natural cubic splines instead).
l

Numeric. Length scale constant for the Gaussian radial basis functions (not used if GRBF = FALSE)
color

Character. Name of variable in data to use to colour lines and points corresponding to subjects. If NULL then lines and points will be grey.
clusters

Logical. Are clusters given via a "class" column in data? Otherwise clusters are found from the hlme object in models[[G]]. Defaults to FALSE.
var.time

Character. Name of variable used for time. Either "calpro_time" or "crp_time" Also used to determine if the LCMM has been fitted for FCAL or CRP.