coverage | R Documentation |

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.

```
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")
)
```

`x` |
a variable. |

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

`na.rm` |
if |

`success` |
for proportions, this specifies the categorical level for which the calculation of proportion will
be done. Defaults: |

`method` |
for |

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()`

.

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.

When using these functions with `df_stats()`

, omit the `x`

argument, which
will be supplied automatically by `df_stats()`

. See examples.

`df_stats()`

, `mosaic::binom.test()`

, `mosaic::t.test()`

```
# 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)
```

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