mean.t.test.twosample.independent.equal.variance.simple<-function(
#Equal variance t-test for difference in means
#Input - pre-computed sample statistics
#Parameter = Default #Description
sample.mean.g1 = 0 #Single value, a pre-computed mean
,sample.variance.g1 = 1 #Single value, a pre-computed variance
,sample.size.g1 = 10 #Single value, a sample size
,sample.mean.g2 = 2 #Single value, a pre-computed mean
,sample.variance.g2 = 1 #Single value, a pre-computed variance
,sample.size.g2 = 10 #Single value, a sample size
,null.hypothesis.difference = 0
,alternative = #Alternative hypothesis for test
c("two.sided" #Two-tail test (default)
,"less" #H1: mu < mu.0
,"greater") #H1: mu > mu.0
,conf.level = 0.95 #Confidence Level
,g1.details = T #Include point estimates and confidence intervals for mean, variance, and sd
,g2.details = T #Include point estimates and confidence intervals for mean, variance, and sd
#,details.include = c("mean", "mean.ci", "var") #,"var.ci" ,"sd", "sd.ci"
) {
validate.htest.alternative(alternative = alternative)
var.test.conf.level = conf.level #TODO
var.test.details = F # TODO
#Independent samples, equal variances
s.denom = sqrt((1/sample.size.g1 + 1/sample.size.g2 ) * ((sample.size.g1-1)* sample.variance.g1 + (sample.size.g2-1)*sample.variance.g2) / (sample.size.g1+sample.size.g2-2))
t <- ((sample.mean.g1 - sample.mean.g2)-null.hypothesis.difference)/s.denom
df <- sample.size.g1+sample.size.g2-2
cv <- qt(conf.level+(1-conf.level)/2, df= df)
diff.upper <- (sample.mean.g1 - sample.mean.g2) + cv*s.denom
diff.lower <- (sample.mean.g1 - sample.mean.g2) - cv*s.denom
p.value <- if (alternative[1] == "two.sided") {
tmp<-pt(t, df)
min(tmp,1-tmp)*2
} else if (alternative[1] == "greater") {
pt(t, df, lower.tail = FALSE)
} else if (alternative[1] == "less") {
pt(t, df, lower.tail = TRUE)
} else {
NA
}
estimate <- c(diff = sample.mean.g1-sample.mean.g2
,se.est = s.denom
,df = df
)
if (g1.details) {
g1.t.out <- t.test.onesample.simple(sample.mean = sample.mean.g1,
sample.variance = sample.variance.g1,
sample.size = sample.size.g1,
conf.level = conf.level)
g1.chi.out <- variance.test.onesample.simple(sample.variance = sample.variance.g1,
sample.size = sample.size.g1,
conf.level = var.test.conf.level)
estimate<-c(estimate
,g1.mean = sample.mean.g1
,g1.mean.lowerci = g1.t.out$conf.int[1]
,g1.mean.upperci = g1.t.out$conf.int[2]
,g1.sample.size = sample.size.g1
,g1.var = sample.variance.g1
,g1.var.lowerci = g1.chi.out$conf.int[1]
,g1.var.upperci = g1.chi.out$conf.int[2]
,g1.sd = sqrt(sample.variance.g1)
,g1.sd.lowerci = sqrt(g1.chi.out$conf.int[1])
,g1.sd.upperci = sqrt(g1.chi.out$conf.int[2])
)
}
if (g2.details) {
g2.t.out <- t.test.onesample.simple(sample.mean = sample.mean.g2,
sample.variance = sample.variance.g2,
sample.size = sample.size.g2,
conf.level = conf.level)
g2.chi.out <- variance.test.onesample.simple(sample.variance = sample.variance.g2,
sample.size = sample.size.g2,
conf.level = var.test.conf.level)
estimate<-c(estimate
,g2.mean = sample.mean.g2
,g2.mean.lowerci = g2.t.out$conf.int[1]
,g2.mean.upperci = g2.t.out$conf.int[2]
,g2.sample.size = sample.size.g2
,g2.var = sample.variance.g2
,g2.var.lowerci = g2.chi.out$conf.int[1]
,g2.var.upperci = g2.chi.out$conf.int[2]
,g2.sd = sqrt(sample.variance.g2)
,g2.sd.lowerci = sqrt(g2.chi.out$conf.int[1])
,g2.sd.upperci = sqrt(g2.chi.out$conf.int[2])
)
}
estimate <- c(
estimate
,omega.sq = (t^2-1)/(t^2+sample.size.g1+sample.size.g2-1)
,eta.sq = t^2 / (t^2 + df)
,power=power.mean.t.test.twosample.independent.equal.variance(
sample.size.g1 = sample.size.g1
,sample.size.g2 = sample.size.g2
,mean.g1 = sample.mean.g1
,mean.g2 = sample.mean.g2
,variance.est.g1 = sample.variance.g1
,variance.est.g2 = sample.variance.g2
,alpha = 1-conf.level
,null.hypothesis.difference = null.hypothesis.difference
,alternative = alternative
,details = F
)
)
retval<-list(data.name = "input sample means and variances",
statistic = t,
estimate = estimate,
parameter = null.hypothesis.difference,
p.value = p.value,
null.value = null.hypothesis.difference,
alternative = alternative[1],
method = paste("Two-Sample t Test For Difference in Means (Equal Variance)"),
conf.int = c(diff.lower, diff.upper)
)
#names(retval$estimate) <- c("sample mean", "df")
names(retval$statistic) <- "t statistic"
names(retval$null.value) <- "difference of means"
names(retval$parameter) <- "null hypothesis difference"
attr(retval$conf.int, "conf.level") <- conf.level
class(retval)<-"htest"
retval
}
#t.test.twosample.independent.simple(sample.mean.g1 = 2,sample.variance.g1 = 1,sample.size.g1 = 10,sample.mean.g2 = 3,sample.variance.g2 = 1,sample.size.g2 = 10)
#mean.t.test.twosample.independent.equal.variance.simple()
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