R/svy_median.R, R/wtd_chisqtest.R, R/wtd_cor.R, and 6 more
weighted_sd.RdWeighted statistics for variables
weighted_sd(), weighted_se(), weighted_mean() and weighted_median()
compute weighted standard deviation, standard error, mean or median for a
variable or for all variables of a data frame. survey_median() computes the
median for a variable in a survey-design (see svydesign).
weighted_correlation() computes a weighted correlation for a two-sided alternative
hypothesis.
Weighted tests
weighted_ttest() computes a weighted t-test, while weighted_mannwhitney()
computes a weighted Mann-Whitney-U test or a Kruskal-Wallis test
(for more than two groups). weighted_chisqtest() computes a weighted
Chi-squared test for contigency tables.
survey_median(x, design)
weighted_chisqtest(data, ...)
# S3 method for default
weighted_chisqtest(data, x, y, weights, ...)
# S3 method for formula
weighted_chisqtest(formula, data, ...)
weighted_correlation(data, ...)
# S3 method for default
weighted_correlation(data, x, y, weights, ci.lvl = 0.95, ...)
# S3 method for formula
weighted_correlation(formula, data, ci.lvl = 0.95, ...)
weighted_mean(x, weights = NULL)
weighted_median(x, weights = NULL)
weighted_mannwhitney(data, ...)
# S3 method for default
weighted_mannwhitney(data, x, grp, weights, ...)
# S3 method for formula
weighted_mannwhitney(formula, data, ...)
weighted_sd(x, weights = NULL)
wtd_sd(x, weights = NULL)
weighted_se(x, weights = NULL)
weighted_ttest(data, ...)
# S3 method for default
weighted_ttest(
data,
x,
y = NULL,
weights,
mu = 0,
paired = FALSE,
ci.lvl = 0.95,
alternative = c("two.sided", "less", "greater"),
...
)
# S3 method for formula
weighted_ttest(
formula,
data,
mu = 0,
paired = FALSE,
ci.lvl = 0.95,
alternative = c("two.sided", "less", "greater"),
...
)(Numeric) vector or a data frame. For survey_median(), weighted_ttest(),
weighted_mannwhitney() and weighted_chisqtest() the bare (unquoted) variable
name, or a character vector with the variable name.
An object of class svydesign, providing
a specification of the survey design.
A data frame.
For weighted_ttest() and weighted_mannwhitney(), currently not used.
For weighted_chisqtest(), further arguments passed down to
chisq.test.
Optional, bare (unquoted) variable name, or a character vector with the variable name.
Bare (unquoted) variable name, or a character vector with
the variable name of the numeric vector of weights. If weights = NULL,
unweighted statistic is reported.
A formula of the form lhs ~ rhs1 + rhs2 where lhs is a
numeric variable giving the data values and rhs1 a factor with two
levels giving the corresponding groups and rhs2 a variable with weights.
Confidence level of the interval.
Bare (unquoted) name of the cross-classifying variable, where
x is grouped into the categories represented by grp,
or a character vector with the variable name.
A number indicating the true value of the mean (or difference in means if you are performing a two sample test).
Logical, whether to compute a paired t-test.
A character string specifying the alternative hypothesis,
must be one of "two.sided" (default), "greater" or
"less". You can specify just the initial letter.
The weighted (test) statistic.
weighted_chisq() is a convenient wrapper for crosstable_statistics.
For a weighted one-way Anova, use means_by_group() with
weights-argument.
weighted_ttest() assumes unequal variance between the two groups.
# weighted sd and se ----
weighted_sd(rnorm(n = 100, mean = 3), runif(n = 100))
#> [1] 0.8498705
data(efc)
weighted_sd(efc[, 1:3], runif(n = nrow(efc)))
#> c12hour e15relat e16sex
#> 51.7876181 2.0540843 0.4699551
weighted_se(efc[, 1:3], runif(n = nrow(efc)))
#> c12hour e15relat e16sex
#> 1.66065784 0.06942749 0.01562877
# survey_median ----
# median for variables from weighted survey designs
if (require("survey")) {
data(nhanes_sample)
des <- svydesign(
id = ~SDMVPSU,
strat = ~SDMVSTRA,
weights = ~WTINT2YR,
nest = TRUE,
data = nhanes_sample
)
survey_median(total, des)
survey_median("total", des)
}
#> $total
#> 0.5
#> [1,] 6
#>
#> attr(,"hasci")
#> [1] FALSE
#> attr(,"class")
#> [1] "newsvyquantile"
# weighted t-test ----
efc$weight <- abs(rnorm(nrow(efc), 1, .3))
weighted_ttest(efc, e17age, weights = weight)
#>
#> One Sample t-test (two.sided)
#> # t=292.68 df=890 p-value=0.000
#>
#> mean of e17age: 79.189 [78.658, 79.720]
#>
weighted_ttest(efc, e17age, c160age, weights = weight)
#>
#> Two-Sample t-test (two.sided)
#>
#> # comparison between e17age and c160age
#> # t=49.92 df=1469 p-value=0.000
#>
#> mean of e17age : 79.187
#> mean of c160age : 53.208
#> difference of mean: 25.980 [24.959 27.001]
#>
weighted_ttest(e17age ~ e16sex + weight, efc)
#>
#> Two-Sample t-test (two.sided)
#>
#> # comparison of e17age by e16sex
#> # t=-7.46 df=604 p-value=0.000
#>
#> mean in group [1] male : 76.401
#> mean in group [2] female: 80.518
#> difference of mean : -4.117 [-5.201 -3.034]
#>
# weighted Mann-Whitney-U-test ----
weighted_mannwhitney(c12hour ~ c161sex + weight, efc)
#>
#> # Weighted Mann-Whitney-U test
#>
#> comparison of c12hour by c161sex
#> Chisq=3.26 df=899 p-value=0.001
#>
# weighted Chi-squared-test ----
weighted_chisqtest(efc, c161sex, e16sex, weights = weight, correct = FALSE)
#>
#> # Measure of Association for Contingency Tables
#>
#> Chi-squared: 2.0566
#> Phi: 0.0479
#> df: 1
#> p-value: 0.152
#> Observations: 895
weighted_chisqtest(c172code ~ c161sex + weight, efc)
#>
#> # Measure of Association for Contingency Tables
#>
#> Chi-squared: 4.8005
#> Cramer's V: 0.0758
#> df: 2
#> p-value: 0.091
#> Observations: 835
# weighted Chi-squared-test for given probabilities ----
weighted_chisqtest(c172code ~ weight, efc, p = c(.33, .33, .34))
#>
#> # Weighted chi-squared test for given probabilities
#>
#> Chi-squared: 280.2808
#> df: 2
#> p-value: < .001***
#> Observations: 908