`R/survey_statistics.r`

`survey_old_quantile.Rd`

Calculate quantiles from complex survey data. A wrapper
around `oldsvyquantile`

, which is a version of the function
from before version 4.1 of the survey package, available for backwards compatibility.
`survey_old_quantile`

and `survey_old_median`

should always be
called from `summarise`

. See Thomas Lumley's blogpost
<https://notstatschat.rbind.io/2021/07/20/what-s-new-in-the-survey-package/>
for more details.

survey_old_quantile( x, quantiles, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), level = 0.95, q_method = "linear", f = 1, interval_type = c("Wald", "score", "betaWald", "probability", "quantile"), ties = c("discrete", "rounded"), df = NULL, ... ) survey_old_median( x, na.rm = FALSE, vartype = c("se", "ci"), level = 0.95, q_method = "linear", f = 1, interval_type = c("Wald", "score", "betaWald", "probability", "quantile"), ties = c("discrete", "rounded"), df = NULL, ... )

x | A variable or expression |
---|---|

quantiles | A vector of quantiles to calculate |

na.rm | A logical value to indicate whether missing values should be dropped |

vartype | NULL to report no variability (default), otherwise one or more of: standard error ("se") confidence interval ("ci") (variance and coefficient of variation not available). |

level | A single number indicating the confidence level (only one level allowed) |

q_method | See "method" in |

f | See |

interval_type | See |

ties | See |

df | A number indicating the degrees of freedom for t-distribution. The
default, Inf uses the normal distribution (matches the survey package).
Also, has no effect for |

... | Ignored |

library(survey) data(api) dstrata <- apistrat %>% as_survey_design(strata = stype, weights = pw) dstrata %>% summarise(api99 = survey_old_quantile(api99, c(0.25, 0.5, 0.75)), api00 = survey_old_median(api00, vartype = c("ci")))#> # A tibble: 1 × 9 #> api99_q25 api99_q50 api99_q75 api99_q25_se api99_q50_se api99_q75_se api00 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 525. 631 727. 12.5 18.4 17.9 667. #> # … with 2 more variables: api00_low <dbl>, api00_upp <dbl>#> # A tibble: 2 × 3 #> awards api00 api00_se #> <fct> <dbl> <dbl> #> 1 No 641. 23.4 #> 2 Yes 671. 18.8