| Title: | Data Analysis with Ceiling and/or Floor Data |
| Version: | 1.0.0 |
| 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 (in prep). |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2018-02-05 15:14:01 UTC; qliu6 |
| Author: | Qimin Liu [aut, cre], Lijuan Wang [aut] |
| Maintainer: | Qimin Liu <qliu6@nd.edu> |
| Repository: | CRAN |
| Date/Publication: | 2018-02-06 09:37:26 UTC |
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))
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 with for data with ceiling/floor effects
Usage
lw.f.star(data, formula, 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 |
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,"a") #using truncated n
lw.f.star(dat,y~group,"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, 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 |
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,"a") #using truncated n
lw.t.test(x1.c,x2.c,"b") #using original n
rec.mean.var
Description
recover mean and variance of the data with ceiling/floor effects
Usage
rec.mean.var(y)
Arguments
y |
a (non-empty) numeric vector of data with ceiling/floor effects |
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)
# 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)
# 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)
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