interval_statistics: Interval statistics

Description Usage Arguments Details Value Note See Also Examples

Description

Calculate coverage intervals and confidence intervals for the sample mean, median, sd, proportion, ... Typically, these will be used within df_stats(). For the mean, median, and sd, the variable x must be quantitative. For proportions, the x can be anything; use the success argument to specify what value you want the proportion of. Default for success is TRUE for x logical, or the first level returned by unique for categorical or numerical variables.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
coverage(x, level = 0.95, na.rm = TRUE)

ci.mean(x, level = 0.95, na.rm = TRUE)

ci.median(x, level = 0.9, na.rm = TRUE)

ci.sd(x, level = 0.95, na.rm = TRUE)

ci.prop(
  x,
  success = NULL,
  level = 0.95,
  method = c("Clopper-Pearson", "binom.test", "Score", "Wilson", "prop.test", "Wald",
    "Agresti-Coull", "Plus4")
)

Arguments

x

a variable.

level

number in 0 to 1 specifying the confidence level for the interval. (Default: 0.95)

na.rm

if TRUE disregard missing data

success

for proportions, this specifies the categorical level for which the calculation of proportion will be done. Defaults: TRUE for logicals for which the proportion is to be calculated.

method

for ci.prop(), the method to use in calculating the confidence interval. See mosaic::binom.test() for details.

Details

Methods: ci.mean() uses the standard t confidence interval. ci.median() uses the normal approximation method. ci.sd() uses the chi-squared method. ci.prop() uses the binomial method. In the usual situation where the mosaic package is available, ci.prop() uses mosaic::binom.test() internally, which provides several methods for the calculation. See the documentation for binom.test() for details about the available methods. Clopper-Pearson is the default method. When used with df_stats(), the confidence interval is calculated for each group separately. For "pooled" confidence intervals, see methods such as lm() or glm().

Value

a named numerical vector with components lower and upper, and, in the case of ci.prop(), center. When used the df_stats(), these components are formed into a data frame.

Note

When using these functions with df_stats(), omit the x argument, which will be supplied automatically by df_stats(). See examples.

See Also

df_stats(), mosaic::binom.test(), mosaic::t.test()

Examples

1
2
3
4
5
6
7
8
# The central 95% interval
df_stats(hp ~ cyl, data = mtcars, c95 = coverage(0.95))
# The confidence interval on the mean
df_stats(hp ~ cyl, data = mtcars, mean, ci.mean)
# What fraction of cars have 6 cylinders?
df_stats(mtcars, ~ cyl, six_cyl_prop = ci.prop(success = 6, level = 0.90))
# Use without `df_stats()` (rare)
ci.mean(mtcars$hp)

mosaicCore documentation built on Jan. 16, 2021, 5:32 p.m.