Calculate observed statistics

Source: R/observe.R

This function is a wrapper that calls specify(), hypothesize(), and calculate() consecutively that can be used to calculate observed statistics from data. hypothesize() will only be called if a point null hypothesis parameter is supplied.

Learn more in vignette("infer").

observe(
  x,
  formula,
  response = NULL,
  explanatory = NULL,
  success = NULL,
  null = NULL,
  p = NULL,
  mu = NULL,
  med = NULL,
  sigma = NULL,
  stat = c("mean", "median", "sum", "sd", "prop", "count", "diff in means",
    "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation", "t", "z",
    "ratio of props", "odds ratio"),
  order = NULL,
  ...
)

Arguments

x

A data frame that can be coerced into a tibble.

formula

A formula with the response variable on the left and the explanatory on the right. Alternatively, a response and explanatory argument can be supplied.

response

The variable name in x that will serve as the response. This is an alternative to using the formula argument.

explanatory

The variable name in x that will serve as the explanatory variable. This is an alternative to using the formula argument.

success

The level of response that will be considered a success, as a string. Needed for inference on one proportion, a difference in proportions, and corresponding z stats.

null

The null hypothesis. Options include "independence", "point", and "paired independence".

  • independence: Should be used with both a response and explanatory variable. Indicates that the values of the specified response variable are independent of the associated values in explanatory.

  • point: Should be used with only a response variable. Indicates that a point estimate based on the values in response is associated with a parameter. Sometimes requires supplying one of p, mu, med, or sigma.

  • paired independence: Should be used with only a response variable giving the pre-computed difference between paired observations. Indicates that the order of subtraction between paired values does not affect the resulting distribution.

p

The true proportion of successes (a number between 0 and 1). To be used with point null hypotheses when the specified response variable is categorical.

mu

The true mean (any numerical value). To be used with point null hypotheses when the specified response variable is continuous.

med

The true median (any numerical value). To be used with point null hypotheses when the specified response variable is continuous.

sigma

The true standard deviation (any numerical value). To be used with point null hypotheses.

stat

A string giving the type of the statistic to calculate. Current options include "mean", "median", "sum", "sd", "prop", "count", "diff in means", "diff in medians", "diff in props", "Chisq" (or "chisq"), "F" (or "f"), "t", "z", "ratio of props", "slope", "odds ratio", "ratio of means", and "correlation". infer only supports theoretical tests on one or two means via the "t" distribution and one or two proportions via the "z".

order

A string vector of specifying the order in which the levels of the explanatory variable should be ordered for subtraction (or division for ratio-based statistics), where order = c("first", "second") means ("first" - "second"), or the analogue for ratios. Needed for inference on difference in means, medians, proportions, ratios, t, and z statistics.

...

To pass options like na.rm = TRUE into functions like mean(), sd(), etc. Can also be used to supply hypothesized null values for the "t" statistic or additional arguments to stats::chisq.test().

Value

A 1-column tibble containing the calculated statistic stat.

See also

Other wrapper functions: chisq_stat(), chisq_test(), prop_test(), t_stat(), t_test()

Other functions for calculating observed statistics: chisq_stat(), t_stat()

Examples

# calculating the observed mean number of hours worked per week
gss %>%
  observe(hours ~ NULL, stat = "mean")
#> Response: hours (numeric)
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1  41.4

# equivalently, calculating the same statistic with the core verbs
gss %>%
  specify(response = hours) %>%
  calculate(stat = "mean")
#> Response: hours (numeric)
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1  41.4

# calculating a t statistic for hypothesized mu = 40 hours worked/week
gss %>%
  observe(hours ~ NULL, stat = "t", null = "point", mu = 40)
#> Response: hours (numeric)
#> Null Hypothesis: point
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1  2.09

# equivalently, calculating the same statistic with the core verbs
gss %>%
  specify(response = hours) %>%
  hypothesize(null = "point", mu = 40) %>%
  calculate(stat = "t")
#> Response: hours (numeric)
#> Null Hypothesis: point
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1  2.09

# similarly for a difference in means in age based on whether
# the respondent has a college degree
observe(
  gss,
  age ~ college,
  stat = "diff in means",
  order = c("degree", "no degree")
)
#> Response: age (numeric)
#> Explanatory: college (factor)
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1 0.941

# equivalently, calculating the same statistic with the core verbs
gss %>%
  specify(age ~ college) %>%
  calculate("diff in means", order = c("degree", "no degree"))
#> Response: age (numeric)
#> Explanatory: college (factor)
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1 0.941

# for a more in-depth explanation of how to use the infer package
if (FALSE) { # \dontrun{
vignette("infer")
} # }