misty (version 0.5.0)

ci.mean: Confidence Interval for the Arithmetic Mean

Description

This function computes a confidence interval for the arithmetic mean with known or unknown population standard deviation or population variance for one or more variables, optionally by a grouping and/or split variable.

Usage

ci.mean(x, sigma = NULL, sigma2 = NULL, adjust = FALSE,
        alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
        group = NULL, split = NULL, sort.var = FALSE, na.omit = FALSE,
        digits = 2, as.na = NULL, check = TRUE, output = TRUE)

Value

Returns an object of class misty.object, which is a list with following entries:

call

function call

type

type of analysis

data

list with the input specified in x, group, and split

args

specification of function arguments

result

result table

Arguments

x

a numeric vector, matrix or data frame with numeric variables, i.e., factors and character variables are excluded from x before conducting the analysis.

sigma

a numeric vector indicating the population standard deviation when computing confidence intervals for the arithmetic mean with known standard deviation Note that either argument sigma or argument sigma2 is specified and it is only possible to specify one value for the argument sigma even though multiple variables are specified in x.

sigma2

a numeric vector indicating the population variance when computing confidence intervals for the arithmetic mean with known variance. Note that either argument sigma or argument sigma2 is specified and it is only possible to specify one value for the argument sigma2 even though multiple variables are specified in x.

adjust

logical: if TRUE (default), difference-adjustment for the confidence intervals is applied.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

conf.level

a numeric value between 0 and 1 indicating the confidence level of the interval.

group

a numeric vector, character vector or factor as grouping variable. Note that a grouping variable can only be used when computing confidence intervals with unknown population standard deviation and population variance.

split

a numeric vector, character vector or factor as split variable. Note that a split variable can only be used when computing confidence intervals with unknown population standard deviation and population variance.

sort.var

logical: if TRUE, output table is sorted by variables when specifying group.

na.omit

logical: if TRUE, incomplete cases are removed before conducting the analysis (i.e., listwise deletion) when specifying more than one outcome variable.

digits

an integer value indicating the number of decimal places to be used.

as.na

a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis. Note that as.na() function is only applied to x, but not to group or split.

check

logical: if TRUE, argument specification is checked.

output

logical: if TRUE, output is shown on the console.

Author

Takuya Yanagida

Details

A difference-adjusted confidence interval (Baguley, 2012) can be computed by specifying adjust = TRUE.

References

Baguley, T. S. (2012). Serious stats: A guide to advanced statistics for the behavioral sciences. Palgrave Macmillan.

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. John Wiley & Sons.

See Also

test.z, test.t, ci.mean.diff, ci.median, ci.prop, ci.var, ci.sd, descript

Examples

dat <- data.frame(group1 = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2),
                  group2 = c(1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2),
                  x1 = c(3, 1, 4, 2, 5, 3, 2, 4, NA, 4, 5, 3),
                  x2 = c(4, NA, 3, 6, 3, 7, 2, 7, 5, 1, 3, 6),
                  x3 = c(7, 8, 5, 6, 4, NA, 8, NA, 6, 5, 8, 6))

# Two-Sided 95% Confidence Interval for x1
ci.mean(dat$x1)

# Two-Sided 95% Difference-Adjusted Confidence Interval for x1
ci.mean(dat$x1, adjust = TRUE)

# Two-Sided 95% Confidence Interval with known standard deviation for x1
ci.mean(dat$x1, sigma = 1.2)

# Two-Sided 95% Confidence Interval with known variance for x1
ci.mean(dat$x1, sigma2 = 2.5)

# One-Sided 95% Confidence Interval for x1
ci.mean(dat$x1, alternative = "less")

# Two-Sided 99% Confidence Interval
ci.mean(dat$x1, conf.level = 0.99)

# Two-Sided 95% Confidence Interval, print results with 3 digits
ci.mean(dat$x1, digits = 3)

# Two-Sided 95% Confidence Interval for x1, convert value 4 to NA
ci.mean(dat$x1, as.na = 4)

# Two-Sided 95% Confidence Interval for x1, x2, and x3,
# listwise deletion for missing data
ci.mean(dat[, c("x1", "x2", "x3")], na.omit = TRUE)

# Two-Sided 95% Confidence Interval for x1, x2, and x3,
# analysis by group1 separately
ci.mean(dat[, c("x1", "x2", "x3")], group = dat$group1)

# Two-Sided 95% Confidence Interval for x1, x2, and x3,
# analysis by group1 separately, sort by variables
ci.mean(dat[, c("x1", "x2", "x3")], group = dat$group1, sort.var = TRUE)

# Two-Sided 95% Confidence Interval for x1, x2, and x3,
# split analysis by group1
ci.mean(dat[, c("x1", "x2", "x3")], split = dat$group1)

# Two-Sided 95% Confidence Interval for x1, x2, and x3,
# analysis by group1 separately, split analysis by group2
ci.mean(dat[, c("x1", "x2", "x3")], group = dat$group1, split = dat$group2)