Help for package DACF

Title:

Data Analysis with Ceiling and/or Floor Data

Version:

1.1.0

Description:

An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (2021) <doi:10.3758/s13428-020-01407-2>.

License:

GPL-2

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

Imports:

mvtnorm, qgraph, Matrix, psych, utils

Suggests:

knitr, rmarkdown, MASS, ggplot2

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2026-07-16 18:33:53 UTC; trichtil

Maintainer:

Qimin Liu <Qliuacademia@gmail.com>

Repository:

CRAN

Depends:

R (≥ 3.5)

Author:

Qimin Liu [aut, cre], Lijuan Wang [aut], Lauren Trichtinger [aut], Grace Murray [ctb]

Date/Publication:

2026-07-16 21:00:45 UTC

Data Analyses for data with Ceiling/Floor effects

Description

The DACF package is used to conduct data analyses for data with ceiling/floor effects. The current version contains functions to estimate mean and variances and to conduct mean comparison tests.

Usage

To access this package's tutorial, type the following line into the console:

vignette("DACF-vignette")

Author(s)

Maintainer: Qimin Liu Qliuacademia@gmail.com

Authors:

Lijuan Wang lwang4@nd.edu
Lauren Trichtinger lauren.trichtinger@gmail.com

Other contributors:

Grace Murray gemurray7@gmail.com [contributor]

Test dataset for DACF package

Description

A dataset used to demonstrate ceiling/floor effect corrections.

Usage

Qi_data_males

Format

A data frame with 1731 rows and 2 columns:

group: The values of the grouping variable. The participants were divided into three groups: moderate Problematic Internet Use (PIU) and Internet Gaming Disorder (IGD) risk, high PIU risk, and high IGD risk.
value: The observed data of the dependent variable, IGD severity from a scale from 0 to 9.

Source

Qi, H., Dong, Z., Wei, Q., Chen, X., & Luo, Y. (2025). Are Gamers Happier? Multidimensional Well-Being Differences in Risk Groups for Problematic Internet Use and Internet Gaming Disorder. Personality and Individual Differences 246, 113334. doi:10.2139/ssrn.5148047

cf_correction

Description

Extends Tukey's HSD and Bonferroni procedure to account for ceiling and floor effects

Usage

cf_correction(
  x,
  tests = "all",
  df.adjustment = "trunc",
  gh.correction = "no_gh",
  alpha = 0.05,
  flr,
  ceil
)

Arguments

x

a dataframe of data with ceiling/floor effects and corresponding group variables or aov results

tests

a character string specifying the desired multiple-comparison procedure: "all" (default, Tukey's HSD and Bonferroni), "tukey" (Tukey's HSD), or "bonf" (Bonferroni)

df.adjustment

a character string specifying the desired method for adjusting the degree of freedom: "trunc" (default, Liu and Wang's truncated-normal corrections) or "unadj" (unadjusted)

gh.correction

a character string specifying if the Welch & Games-Howell correction for heteroscedasticity should be included: "no_gh"(default) or "yes_gh"

alpha

a (non-empty) numeric value of desired of alpha level

flr

a (non-empty) numeric value of the floor threshold (e.g., the minimum score of the measurement scale)

ceil

a (non-empty) numeric value of the ceiling threshold (e.g., the maximum score of the measurement scale)

Value

a matrix containing pairwise comparison results. Columns depend on the tests and gh.correction arguments:

Comparison_i: First group in the comparison
Comparison_j: Second group in the comparison
mean_i: The adjusted mean of the first group
mean_j: The adjusted mean of the second group
diff_in_means: The difference in adjusted means
tukey.CI_lwr: Lower bound of Tukey's HSD confidence interval (tests = "tukey" or "all")
tukey.CI_upr: Upper bound of Tukey's HSD confidence interval (tests = "tukey" or "all")
hedges_g: Hedges' g effect size (tests = "tukey" or "all")
Q: Adjusted Tukey's Q statistic (tests = "tukey" or "all")
p: p-value for Tukey's Q statistic (tests = "tukey" or "all")
t: t-test statistic (tests = "bonf" or "all")
t_p: p-value for the t-test statistic (tests = "bonf" or "all")
p.bonferroni: Bonferroni-adjusted p-value (tests = "bonf" or "all")
bonf.CI_lwr: Lower bound of Bonferroni confidence interval (tests = "bonf" or "all")
bonf.CI_upr: Upper bound of Bonferroni confidence interval (tests = "bonf" or "all")
gh.CI_lwr: Lower bound of Games-Howell confidence interval (gh.correction = "yes_gh", tests = "tukey" or "all")
gh.CI_upr: Upper bound of Games-Howell confidence interval (gh.correction = "yes_gh", tests = "tukey" or "all")
games.howell.t: Games-Howell t statistic (gh.correction = "yes_gh", tests = "tukey" or "all")
games.howell.p: Games-Howell p-value (gh.correction = "yes_gh", tests = "tukey" or "all")
games.howell.g: Games-Howell Hedges' g (gh.correction = "yes_gh", tests = "tukey" or "all")
welch.satterthwaite.df: Welch-Satterthwaite degrees of freedom (gh.correction = "yes_gh")
welch.bonf.CI_lwr: Lower bound of Welch-Bonferroni confidence interval (gh.correction = "yes_gh", tests = "bonf" or "all")
welch.bonf.CI_upr: Upper bound of Welch-Bonferroni confidence interval (gh.correction = "yes_gh", tests = "bonf" or "all")
t_Welch: Welch t statistic (gh.correction = "yes_gh", tests = "bonf" or "all")
p_Welch: Welch p-value (gh.correction = "yes_gh", tests = "bonf" or "all")
p_Welch.bonferroni: Welch Bonferroni-adjusted p-value (gh.correction = "yes_gh", tests = "bonf" or "all")

References

Aitkin MA. Correlation in a Singly Truncated Bivariate Normal Distribution. Psychometrika. 1964;29(3):263-270. doi:10.1007/BF02289723

Cohen, A. C. (1959). Simplified Estimators for the Normal Distribution When Samples Are Singly Censored or Truncated. AnnalsTechnometrics, 1(3), 217–237. doi:10.1080/00401706.1959.10489859

Greene, W. H. (2002). Econometric Analysis. In Econometric Analysis.

Liu, Q., Wang, L. (2020) t-Test and ANOVA for data with ceiling and/or floor effects. Behav Res 53, 264–277 . doi:10.3758/s13428-020-01407-2

Examples

data("Qi_data_males") # (Qi et al., 2025)
cf_correction(Qi_data_males, tests = "all", df.adjustment = "trunc",
              gh.correction = "yes_gh", alpha = .05, flr = 0, ceil = 9)
cf_correction(Qi_data_males, tests = "tukey", df.adjustment = "unadj",
              gh.correction = "no_gh", alpha = .05, flr = 0, ceil = 9)
cf_correction(Qi_data_males, tests = "all", df.adjustment = "unadj",
              gh.correction = "no_gh", alpha = .05, flr = 0, ceil = 9)

f.star.test

Description

conduct a Brown-Forsythe F star test

Usage

f.star.test(means, variances, ns)

Arguments

means

a (non-empty) numeric vector of the group means

variances

a (non-empty) numeric vector of the group variances

ns

a (non-empty) numeric vector of sample sizes per group

Value

statistic

the value of the adjusted Brown-Forsythe F star statistic

p.value

the p-value for the test

est.f.squared

effect size estimate as in Cohen's f squared

Examples

# a f star test for three-group mean comparison
f.star.test(c(-.2,0,.2),c(1,1,1),c(100,100,100))
f.star.test(c(0,0,1),c(2,1,3),c(100,100,100))

Gaussian Graphical Model with Ceiling/Floor Effect Correction

Description

Estimates a psychological network (GGM) from data with ceiling and/or floor effects using a two-step pairwise censored-normal correction. Step 1 corrects item means and variances via truncated-normal moment matching (Liu & Wang, 2021). Step 2 estimates pairwise latent correlations by solving the moment equation E[cov*(rho)] = observed censored covariance, evaluated using 50-point Gauss-Hermite quadrature. The corrected covariance matrix is supplied as input to EBICglasso or significance-based edge selection, preserving the standard qgraph/bootnet workflow.

Usage

ggm_cfe(
  data,
  floor = NULL,
  ceiling = NULL,
  method = "EBICglasso",
  gamma = 0.5,
  nlambda = 100,
  lambda.min.ratio = 0.01,
  threshold = FALSE,
  alpha = 0.05,
  verbose = TRUE
)

Arguments

data

A data frame or numeric matrix (n x p). Rows = observations, columns = variables (items/nodes).

floor

Floor threshold(s). Either a single value applied to all variables, or a named numeric vector with one value per column. Use NULL to indicate no floor effect. Default: NULL.

ceiling

Ceiling threshold(s). Same format as floor. Default: NULL.

method

Estimation method(s): "EBICglasso" (default), "FDR", "Bonferroni", or "all" to run all three.

gamma

EBIC hyperparameter (0 = BIC, 0.5 = default). Only used when method includes "EBICglasso".

nlambda

Number of tuning parameters searched by EBICglasso. Default: 100.

lambda.min.ratio

Smallest lambda searched, as a fraction of the largest lambda. Passed to qgraph::EBICglasso. Default: 0.01.

threshold

Logical. Passed to qgraph::EBICglasso; TRUE enforces higher specificity at the cost of sensitivity. Default: FALSE.

alpha

Significance level for p-value methods. Default: 0.05.

verbose

Logical. Print progress and diagnostics. Default: TRUE.

Value

An object of class "ggm_cfe" containing:

network

Partial correlation matrix from the corrected method (primary estimator: EBICglasso or first specified method).

network_corrected

Partial correlation matrix from naive EBICglasso (ignoring ceiling/floor effects).

network_naive

Partial correlation matrix estimated from the raw (uncorrected) sample covariance using the primary method.

networks

Named list of partial correlation matrices for all requested methods.

networks_corrected

Partial correlation matrix from naive EBICglasso (ignoring ceiling/floor effects).

networks_naive

Named list of naive partial correlation matrices (one per requested method), estimated without ceiling/floor correction.

Sigma_corrected

The corrected covariance matrix (p x p).

Sigma_naive

The raw sample covariance matrix (p x p).

diagnostics

List with per-variable censoring statistics, nearPD correction magnitude, and floor/ceiling thresholds.

censoring: Data frame (p rows) with per-variable censoring statistics: variable, n_floor, n_ceil, pct_floor, pct_ceil, naive and corrected means (mu_naive, mu_corrected), and standard deviations (sd_naive, sd_corrected).
npd_correction: Relative Frobenius-norm magnitude of the nearest positive-definite projection applied to the corrected covariance matrix. Values above 0.05 suggest instability and should be interpreted cautiously.
a_vec: Length-p numeric vector of effective floor thresholds used internally (with NULL inputs replaced by data-driven lower bounds).
b_vec: Length-p numeric vector of effective ceiling thresholds (with NULL inputs replaced by data-driven upper bounds).

method

Character vector of estimation method(s) run.

gamma

EBIC hyperparameter value used.

nlambda

Number of regularization parameters searched.

lambda.min.ratio

Smallest lambda as a fraction of the largest.

threshold

Logical; whether EBICglasso hard-thresholding was applied.

alpha

Significance level for p-value methods.

n

The number of observations.

p

The number of variables.

varnames

The character vector of variable (column) names.

data

Original data matrix.

call

Matched call.

Examples

# Simulate data with ceiling effects
set.seed(42)
Y <- MASS::mvrnorm(200, rep(0,5), diag(5) + 0.3)
Y_cens <- pmin(Y, 1.0)   # ceiling at 1.0 SD
fit <- ggm_cfe(Y_cens, floor=NULL, ceiling=1.0)
print(fit)
plot(fit)

# With psych::bfi personality data
# library(psych)
# fit <- ggm_cfe(bfi[,1:25], floor=1, ceiling=6)

induce.cfe

Description

inducing ceiling/floor effects in data

Usage

induce.cfe(floor.perc, ceiling.perc, y)

Arguments

floor.perc

a (non-empty) numeric value from 0 to 1 denoting the desired percentage of floor effects

ceiling.perc

a (non-empty) numeric value from 0 to 1 denoting the desired percentage of ceiling effects

y

a (non-empty) numeric vector of data

Value

y scores with induced ceiling/floor effects

Examples

x=rnorm(1000,0,1) #simulate "healthy data"
x.c20=induce.cfe(0,.2,x) #induce 20% ceiling effects into the data
sum(x.c20==max(x.c20))/length(x.c20) #check ceiling percentage
x.f20=induce.cfe(.2,0,x) #induce 20% floor effects into the data
sum(x.f20==min(x.f20))/length(x.f20) #check ceiling percentage

lw.f.star

Description

conduct an F star test for data with ceiling/floor effects

Usage

lw.f.star(data, formula, flr, ceil, method_type)

Arguments

data

a dataframe of data with ceiling/floor effects and corresponding group variables in wide format

formula

a formula denoting the dependent and independent variable, e.g., y~group

flr

a (non-empty) numeric value of the floor threshold (e.g., the minimum score of the measurement scale)

ceil

a (non-empty) numeric value of the ceiling threshold (e.g., the maximum score of the measurement scale)

method_type

a character string specifying the preferred method type. "a" uses the original sample size and "b" uses after-truncation sample size.

Value

statistic

the value of the Brown-Forsythe F star statistics

p.value

the p-value for the test

est.f.squared

effect size estimate in Cohen's f squared

Examples

dat=threeganova.sim(1000,.16,1)
dat[dat$group==1,3]=induce.cfe(0,.15,dat[dat$group==1,3])
lw.f.star(dat, y~group, flr=min(dat$y), ceil=max(dat$y), "a") #using truncated n
lw.f.star(dat, y~group, flr=min(dat$y), ceil=max(dat$y), "b") #using original n

lw.t.test

Description

conduct a t test adjusting for ceiling and/or floor effects

Usage

lw.t.test(x1, x2, flr1, ceil1, flr2, ceil2, method_type)

Arguments

x1

a (non-empty) numeric vector of data values for group 1 with floor/ceiling effects

x2

a (non-empty) numeric vector of data values for group 2 with floor/ceiling effects

flr1

a (non-empty) numeric value of the floor threshold for group 1

ceil1

a (non-empty) numeric value of the ceiling threshold for group 1

flr2

a (non-empty) numeric value of the floor threshold for group 2

ceil2

a (non-empty) numeric value of the ceiling threshold for group 2

method_type

a character string specifying the preferred method type. "a" uses the original sample size and "b" uses after-truncation sample size.

Value

statistic

the value of the adjusted t test statistics

p.value

the p-value for the test

est.d

effect size estimate as in Cohen's d

conf.int

95% confidence interval

Examples

x1.c=induce.cfe(0,.3,rnorm(1000,20,5)) #group 1 scores with 30% ceiling data
x2.c=induce.cfe(.15,0,rnorm(1000,30,5)) #group 2 scores with 15% floor data
lw.t.test(x1.c, x2.c, flr1=min(x1.c), ceil1=max(x1.c), flr2=min(x2.c), ceil2=max(x2.c), "a")
lw.t.test(x1.c, x2.c, flr1=min(x1.c), ceil1=max(x1.c), flr2=min(x2.c), ceil2=max(x2.c), "b")

Censoring Profile Plot for ggm_cfe Objects

Description

Plots the floor and ceiling proportions for each variable in a ggm_cfe object. Uses ggplot2 if available, otherwise falls back to base graphics.

Usage

plot_censoring(x)

Arguments

x

An object of class "ggm_cfe" as returned by ggm_cfe.

Value

Invisibly returns x.

Examples

set.seed(42)
Y <- MASS::mvrnorm(200, rep(0, 5), diag(5) + 0.3)
Y_cens <- pmin(Y, 1.0)
fit <- ggm_cfe(Y_cens, floor = NULL, ceiling = 1.0)
plot_censoring(fit)

rec.mean.var

Description

recover mean and variance of the data with ceiling/floor effects

Usage

rec.mean.var(y, flr, ceil)

Arguments

y

a (non-empty) numeric vector of data with ceiling/floor effects

flr

a (non-empty) numeric value of the floor threshold (e.g., the minimum score of the measurement scale)

ceil

a (non-empty) numeric value of the ceiling threshold (e.g., the maximum score of the measurement scale)

Value

ceiling.percentage

the percentage of ceiling values in the data

floor.percentage

the percentage of floor values in the data

est.mean

estimated mean of the true scores

est.var

estimated variance of the true scores

Examples

# simulate normally distributed true scores
x=rnorm(1000,2,4)
mean(x); var(x)
# induce 20% floor effects
# and estimate the true mean variance from the floor data
x.f=induce.cfe(.2,0,x)
rec.mean.var(x.f, flr=min(x.f), ceil=max(x.f))
# induce 20% ceiling effects
# and estimate the true mean and variance from the ceiling data
x.c=induce.cfe(0,.2,x)
rec.mean.var(x.c, flr=min(x.c), ceil=max(x.c))
# induce 20% and 10% of floor and ceiling effects, respectively
# and estimate the true mean and variance from the data with floor and ceiling effects
x.cf=induce.cfe(.2,.1,x)
rec.mean.var(x.cf, flr=min(x.cf), ceil=max(x.cf))

threeganova.sim

Description

simulate three-group anova data

Usage

threeganova.sim(group_n, f_sqr, sd.1)

Arguments

group_n

a (non-empty) numeric value of desired sample size per group

f_sqr

a (non-empty) numeric value of desired Cohen's f squared value

sd.1

a (non-empty) numeric value of desired standard deviation ratio

Value

a dataframe containing scores "y", grouping factor "group", and residual errors.

Examples

sample.3g=threeganova.sim(1000,.16,5) #data of n=1000, sd1=sd3=1 and sd2=5, and f^2=.16
colnames(sample.3g) #examine the column names
dim(sample.3g) #examine the data structure
aggregate(sample.3g$y,sd,by=list(sample.3g$group)) #check group standard deviations

Package {DACF}

Data Analyses for data with Ceiling/Floor effects

Description

Usage

Author(s)

Test dataset for DACF package

Description

Usage

Format

Source

cf_correction

Description

Usage

Arguments

Value

References

Examples

f.star.test

Description

Usage

Arguments

Value

Examples

Gaussian Graphical Model with Ceiling/Floor Effect Correction

Description

Usage

Arguments

Value

Examples

induce.cfe

Description

Usage

Arguments

Value

Examples

lw.f.star

Description

Usage

Arguments

Value

Examples

lw.t.test

Description

Usage

Arguments

Value

Examples

Censoring Profile Plot for ggm_cfe Objects

Description

Usage

Arguments

Value

Examples

rec.mean.var

Description

Usage

Arguments

Value

Examples

threeganova.sim

Description

Usage

Arguments

Value

Examples