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The goal of DACF is to implement methods to deal with challenges associated with ceiling/floor effects in the data using paramtric methods that assume normality for the true scores.
You can install DACF from github with:
# install.packages("devtools")
devtools::install_github("QMmmmLiu/DACFD")This is a basic example which shows you how to solve a common problem:
library(DACF)
# Simulate healthy data for two groups
x.1=rnorm(300,2,4)
x.2=rnorm(300,3,5)
# check mean and variance for simulated healthy data
mean(x.1);var(x.1)
#> [1] 2.046914
#> [1] 12.34239
mean(x.2);var(x.2)
#> [1] 3.054255
#> [1] 19.49582
# induce ceiling effects of 20% in group 1
x.1.cf=induce.cfe(.2,0,x.1)
# induce floor effects of 10% in group 2
x.2.cf=induce.cfe(0,.1,x.2)
# recover the mean and variance for ceiling/floor data
rec.mean.var(x.1.cf)
#> $ceiling.percentage
#> [1] 0.003333333
#>
#> $floor.percentage
#> [1] 0.24
#>
#> $est.mean
#> [1] 2.141253
#>
#> $est.var
#> [1] 11.79842
rec.mean.var(x.2.cf)
#> $ceiling.percentage
#> [1] 0.1
#>
#> $floor.percentage
#> [1] 0.003333333
#>
#> $est.mean
#> [1] 2.959067
#>
#> $est.var
#> [1] 18.01588
# conduct a t test on healthy data
t.test(x.1,x.2)
#>
#> Welch Two Sample t-test
#>
#> data: x.1 and x.2
#> t = -3.0922, df = 569.26, p-value = 0.002084
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -1.6472027 -0.3674792
#> sample estimates:
#> mean of x mean of y
#> 2.046914 3.054255
t.test(x.1.cf,x.2.cf)
#>
#> Welch Two Sample t-test
#>
#> data: x.1.cf and x.2.cf
#> t = -1.1283, df = 550.93, p-value = 0.2597
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -0.8717900 0.2356737
#> sample estimates:
#> mean of x mean of y
#> 2.473312 2.791370
# conduct an adjusted t test on ceiling/floor data
lw.t.test(x.1.cf,x.2.cf,"a")
#> $statistic
#> [1] -2.37371
#>
#> $p.value
#> [1] 0.0183786
#>
#> $est.d
#> [1] -0.2099299
#>
#> $conf.int
#> [1] -1.4964171 -0.1392114
lw.t.test(x.1.cf,x.2.cf,"b")
#> $statistic
#> [1] -2.594197
#>
#> $p.value
#> [1] 0.009970491
#>
#> $est.d
#> [1] -0.2118153
#>
#> $conf.int
#> [1] -1.4383181 -0.1973104
# generate a dataframe for ANOVA demo
testdat=threeganova.sim(10000,.0625,1)
# induce ceiling/floor effects in the data
testdat.cf=testdat
testdat.cf[testdat.cf$group==2,]$y=induce.cfe(.2,0,testdat.cf[testdat.cf$group==2,]$y)
# conduct an adjusted F star test on ceiling/floor data
lw.f.star(testdat.cf,y~group,"a")
#> $statistic
#> [1] 868.0733
#>
#> $p.value
#> [1] 0
#>
#> $est.f.squared
#> [1] 0.05787155
lw.f.star(testdat.cf,y~group,"b")
#> $statistic
#> [1] 781.9596
#>
#> $p.value
#> [1] 0
#>
#> $est.f.squared
#> [1] 0.05591017These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.