Package 'pastaPlot'

Spaghetti-plot fixed and random effects of linear mixed models

Description

cookPasta() creates dataframes from fixed and random effects of 'lme4' or 'glmmTMB' models (e.g., for plotting)

Usage

cookPasta(
  model = NULL,
  predictor = NULL,
  nested.in = NULL,
  group = NULL,
  ci.int = FALSE,
  ci.lvl = ci.lvl
)

Arguments

model	lme4 or glmmTMB model object
predictor	(Character) Name of predictor (e.g., "time" or "math_score"), as it is present in the model
nested.in	(Character) Name of the variable your time points or subjects are nested in (e.g.,"school" or "id")
group	(Optional, character) The name of your grouping variable (e.g., "condition" or "gender")
ci.int	(Optional, boolean) Enable confidence (prediction) intervals, disabled by default
ci.lvl	(Optional, numeric) Set level of confidence (prediction) intervals (default: 0.95). Requires ci.int to be set to TRUE

Value

Returns a list of two dataframes, in which the first element is the fixed effects dataframe and the second element the random effects dataframe

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ECOVIA data

Description

A subset of data from the ECOVIA app

Usage

ecovia_data

Format

A data frame with 1743 rows and 4 columns:

id

Subjects, in which time points are nested in

time

Day of intervention

condition

Condition (control vs. intervention)

CO2

Daily dietary carbon emissions

Source

<https://osf.io/qd7vw/?view_only=cc22e0d1de8844e1850d8ef1442fbecb>

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JSP data

Description

A subset of JSP secondary school exam data

Usage

jsp_data

Format

A data frame with 728 rows and 7 columns:

math_score_y1

Score on math test year 1

math_score_y3

Score on math test year 3

gender

Gender of subjects (pupils)

social_class

Social class of subjects (pupils)

school

School, in which subjects (pupils) are nested in

normal_score_y1

Normal test score year 1

normal_score_y3

Normal test score year 3

Source

<https://www.bristol.ac.uk/cmm/team/hg/msm-3rd-ed/datasets.html>

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Spaghetti-plot fixed and random effects of linear mixed models

Description

pastaPlot() plots slopes for both fixed and random effects of linear mixed models from 'lme4' or 'glmmTMB' packages as a single spaghetti plot, optionally between conditions including confidence bands for fixed effects.

Usage

pastaPlot(
  model = NULL,
  predictor = NULL,
  nested.in = NULL,
  group = NULL,
  legend.title = "Legend",
  group.labels = NULL,
  xlab = NULL,
  ylab = NULL,
  font.family = NULL,
  colors = NULL,
  ci.lvl = 0.95,
  ci.int = FALSE,
  ci.linetype = 0,
  lwd.fix = 1,
  lwd.ran = 0.5,
  xlab.inc = 0,
  xlab.int = NULL,
  ylim = NULL,
  opacity.ci = 0.25,
  opacity.ran = 0.3,
  colors.ci = NULL
)

Arguments

model	lme4 or glmmTMB model object
predictor	(Character) Name of predictor (e.g., "time" or "math_score"), as it is present in the model
nested.in	(Character) Name of the variable your time points or subjects are nested in (e.g.,"school" or "id")
group	(Optional, character) The name of your grouping variable (e.g., "condition" or "gender")
legend.title	(Optional, character) Name of legend in plot (e.g., "Condition", or "Gender")
group.labels	(Optional, vector of characters) Names of group labels to be displayed in the plot (e.g., c("Control", "Intervention"))
xlab	(Optional, character) Label of x-axis (predictor) (e.g., "Time (days)")
ylab	(Optional, character) Label of y-axis (dependant variable) (e.g., "GAF")
font.family	(Optional, character) Name of the font family (e.g. "serif")
colors	(Optional, vector of characters) Set color of slopes. Length of vector should correspond to number of values in group variable (e.g., c("#5e9aff", "blue")). If no group variable is specified, pass a single color.
ci.lvl	(Optional, numeric) Set confidence interval (default: 0.95)
ci.int	(Optional, logical) Enable confidence (prediction) intervals, disabled by default
ci.linetype	(Optional, numeric) Set linetype of confidence bands outline (default: 0)
lwd.fix	(Optional, numeric) Line width of fixed effects (default: 1)
lwd.ran	(Optional, numeric) Line width of random effects (default: 0.5)
xlab.inc	(Optional, numeric) Increment the displayed values of your predictor (e.g., xlab_int = 1 changes range of x from 0-29 to 1-30), set to 0 by default
xlab.int	(Optional, numeric) Interval between displayed predictor values on x-axis (e.g., "1"), disabled by default
ylim	(Optional, numeric vector) Limited range of values on y-axis (e.g. c(1,5.5))
opacity.ci	(Optional, numeric) Set opacity of confidence bands in the range of 0 to 1 (default = 0.1)
opacity.ran	(Optional, numeric) Set opacity of random slopes in the range of 0 to 1 (default = 0.4)
colors.ci	(Optional, vector of characters) Set color of confidence bands. Length of vector should correspond to number of values in group variable (e.g., c("#5e9aff", "blue")). If no group variable is specified, pass a single color.

Value

Returns a ggplot2 plot object to further be modified

Examples

lme4_model <- lme4::lmer(CO2 ~ 1 + time*condition + (1 + time | id),
data=ecovia_data, REML = FALSE, control = lme4::lmerControl(optimizer = "bobyqa"))
pastaPlot(lme4_model, "time", "id", group = "condition", legend.title = "Condition",
group.labels = c("Control", "Intervention"), ci.int = TRUE, xlab = "Time (days)",
ylab = "CO2")

glmmTMB_model <- glmmTMB::glmmTMB(math_score_y3 ~ 1 + math_score_y1*gender +
(1 + math_score_y1 | school), data=jsp_data, REML = FALSE)
pastaPlot(glmmTMB_model, "math_score_y1", "school", group = "gender",
legend.title = "Gender", group.labels = c("Male", "Female"), ci.int = FALSE,
xlab = "Math score (year 1)", ylab = "Math score (year 3)")

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