mean.z.test.twosample.independent.simple<-function(
sample.mean.g1
,variance.g1
,sample.size.g1
,sample.mean.g2 = sample.mean.g1
,variance.g2 = variance.g1
,sample.size.g2 = sample.size.g1
,null.hypothesis.difference = 0
,alternative = c("two.sided","less","greater") #difference in means only
,conf.level = 0.95
,g1.details = T #Include point estimates and confidence intervals for mean
,g2.details = T #Include point estimates and confidence intervals for mean
#,details.include = c("mean", "mean.ci", "var") #,"var.ci" ,"sd", "sd.ci"
) {
validate.htest.alternative(alternative = alternative)
#Independent samples, equal variances
s.denom <- sqrt(variance.g1/sample.size.g1+variance.g2/sample.size.g2)
z <- ((sample.mean.g1 - sample.mean.g2)-null.hypothesis.difference)/s.denom
cv <- qnorm(conf.level+(1-conf.level)/2)
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<-pnorm(z)
min(tmp,1-tmp)*2
} else if (alternative[1] == "greater") {
pnorm(z, lower.tail = FALSE)
} else if (alternative[1] == "less") {
pnorm(z, lower.tail = TRUE)
} else {
NA
}
estimate <- c(diff = sample.mean.g1-sample.mean.g2
,se.est = s.denom
)
if (g1.details) {
g1.z.out <- mean.z.test.onesample.simple(sample.mean = sample.mean.g1,
known.population.variance = 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.z.out$conf.int[1]
,g1.mean.upperci = g1.z.out$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.z.out <- mean.z.test.onesample.simple(sample.mean = sample.mean.g2,
known.population.variance = 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.z.out$conf.int[1]
,g2.mean.upperci = g2.z.out$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])
)
}
# if (var.test.details) {
# estimate<-c(estimate
# ,var.test.conf.level = var.test.conf.level
# ,var.test.F = rmnames(var.equality.test.out$statistic)
# ,var.test.df.g1 = sample.size.g1 - 1
# ,var.test.df.g2 = sample.size.g2 - 1
# ,var.test.p = var.equality.test.out$p.value
# )
# }
retval<-list(data.name = "input sample means and known variances",
statistic = z,
estimate = estimate,
parameter = null.hypothesis.difference,
p.value = p.value,
null.value = null.hypothesis.difference,
alternative = alternative[1],
method = paste("Two-Sample z Test For Difference in Means (Known Population Variances)"),
conf.int = c(diff.lower, diff.upper)
)
#names(retval$estimate) <- c("sample mean", "df")
names(retval$statistic) <- "z 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)
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