neg.paired {negligible} | R Documentation |
Negligible Effect Test on the Difference between the Means of Dependent Populations
Description
This function allows researchers to test whether the difference between the means of two dependent populations is negligible, where negligible represents the smallest meaningful effect size (MMES)
Usage
neg.paired(
var1 = NULL,
var2 = NULL,
outcome = NULL,
group = NULL,
ID = NULL,
neiL,
neiU,
normality = TRUE,
nboot = 10000,
alpha = 0.05,
plot = TRUE,
saveplot = FALSE,
data = NULL,
seed = NA,
...
)
## S3 method for class 'neg.paired'
print(x, ...)
Arguments
var1 |
Data for Group 1 (if outcome, group and ID are omitted) |
var2 |
Data for Group 2 (if outcome, group and ID are omitted) |
outcome |
Dependent Variable (if var1 and var2 are omitted) |
group |
Dichotomous Predictor/Independent Variable (if var1 and var2 are omitted) |
ID |
participant ID (if var1 and var2 are omitted) |
neiL |
Lower Bound of the Equivalence Interval |
neiU |
Upper Bound of the Equivalence Interval |
normality |
Are the population variances (and hence the residuals) assumed to be normally distributed? |
nboot |
Number of bootstrap samples for calculating CIs |
alpha |
Nominal Type I Error rate |
plot |
Should a plot of the results be produced? |
saveplot |
Should the plot be saved? |
data |
Dataset containing var1/var2 or outcome/group/ID |
seed |
Seed number |
... |
Extra arguments |
x |
object of class |
Details
This function evaluates whether the difference in the means of 2 dependent populations can be considered negligible (i.e., the population means can be considered equivalent).
The user specifies either the data associated with the first and second groups/populations (var1, var2, both should be continuous) or specifies the Indepedent Variable/Predictor (group, should be a factor) and the Dependent Variable (outcome, should be continuous). A 'data' statement can be used if the variables are stored in an R dataset.
The user must also specify the lower and upper bounds of the negligible effect (equivalence) interval. These are specified in the original units of the outcome variable.
Value
A list
including the following:
-
meanx
Sample mean of the first population/group. -
meany
Sample mean of the second population/group. -
medx
Sample median of the first population/group. -
medy
Sample median second population/group. -
sdx
Sample standard deviation of the first population/group. -
sdy
Sample standard deviation of the second population/group. -
madx
Sample median absolute deviation of the first population/group. -
mady
Sample median absolute deviation of the second population/group. -
neiL
Lower bound of the negligible effect (equivalence) interval. -
neiU
Upper bound of the negligible effect (equivalence) interval. -
effsizeraw
Simple difference in the means (or medians if normality = FALSE) -
cilraw2
Lower bound of the 1-alpha CI for the raw mean difference. -
ciuraw2
Upper bound of the 1-alpha CI for the raw mean difference. -
cilraw
Lower bound of the 1-2*alpha CI for the raw mean difference. -
ciuraw
Upper bound of the 1-2*alpha CI for the raw mean difference. -
effsized
Standardized mean (or median if normality = FALSE) difference. -
cild
Lower bound of the 1-alpha CI for the standardized mean (or median if normality = FALSE) difference. -
ciud
Upper bound of the 1-alpha CI for the standardized mean (or median if normality = FALSE) difference. -
effsizepd
Proportional distance statistic. -
cilpd
Lower bound of the 1-alpha CI for the proportional distance statistic. -
ciupd
Upper bound of the 1-alpha CI for the proportional distance statistic. -
t1
First t-statistic from the TOST procedure. -
t1
Second t-statistic from the TOST procedure. -
df1
Degrees of freedom for the first t-statistic from the TOST procedure. -
df2
Degrees of freedom for the second t-statistic from the TOST procedure. -
pval1
p value associated with the first t-statistic from the TOST procedure. -
pval2
p value associated with the second t-statistic from the TOST procedure. -
alpha
Nominal Type I error rate -
seed
Seed number
Author(s)
Rob Cribbie cribbie@yorku.ca Naomi Martinez Gutierrez naomimg@yorku.ca
Examples
#wide format
ID<-rep(1:20)
control<-rnorm(20)
intervention<-rnorm(20)
d<-data.frame(ID, control, intervention)
head(d)
neg.paired(var1=control,var2=intervention,neiL=-1,neiU=1,plot=TRUE,
data=d)
neg.paired(var1=d$control,var2=d$intervention,neiL=-1,neiU=1,plot=TRUE)
neg.paired(var1=d$control,var2=d$intervention,neiL=-1,neiU=1,normality=FALSE,
nboot=10,plot=TRUE)
## Not run:
#long format
sample1<-sample(1:20, 20, replace=FALSE)
sample2<-sample(1:20, 20, replace=FALSE)
ID<-c(sample1, sample2)
group<-rep(c("control","intervention"),c(20,20))
outcome<-c(control,intervention)
d<-data.frame(ID,group,outcome)
neg.paired(outcome=outcome,group=group,ID=ID,neiL=-1,neiU=1,plot=TRUE,data=d)
neg.paired(outcome=d$outcome,group=d$group,ID=d$ID,neiL=-1,neiU=1,plot=TRUE)
neg.paired(outcome=d$outcome,group=d$group,ID=d$ID,neiL=-1,neiU=1,plot=TRUE, normality=FALSE)
#long format with multiple variables
sample1<-sample(1:20, 20, replace=FALSE)
sample2<-sample(1:20, 20, replace=FALSE)
ID<-c(sample1, sample2)
attendance<-sample(1:3, 20, replace=TRUE)
group<-rep(c("control","intervention"),c(20,20))
outcome<-c(control,intervention)
d<-data.frame(ID,group,outcome,attendance)
neg.paired(outcome=outcome,group=group,ID=ID,neiL=-1,neiU=1,plot=TRUE,data=d)
neg.paired(outcome=d$outcome,group=d$group,ID=d$ID,neiL=-1,neiU=1,plot=TRUE)
#open a dataset
library(negligible)
d<-perfectionism
names(d)
head(d)
neg.paired(var1=atqpre.total,var2=atqpost.total,
neiL=-10,neiU=10,data=d)
#Dataset with missing data
x<-rnorm(10)
x[c(3,6)]<-NA
y<-rnorm(10)
y[c(7)]<-NA
neg.paired(x,y,neiL=-1,neiU=1, normality=FALSE)
## End(Not run)