Compute various weighted statistics
w_mean weighted mean of a numeric vector
w_sd weighted sample standard deviation of a numeric vector
w_var weighted sample variance of a numeric vector
w_se weighted standard error of a numeric vector
w_median weighted median of a numeric vector
w_mad weighted mean absolute deviation from median of a numeric vector
w_sum weighted sum of a numeric vector
w_n weighted number of values of a numeric vector
w_cov weighted covariance matrix of a numeric matrix/data.frame
w_cor weighted Pearson correlation matrix of a numeric matrix/data.frame
w_pearson shortcut for w_cor. Weighted Pearson
correlation matrix of a numeric matrix/data.frame
w_spearman weighted Spearman correlation matrix of a numeric matrix/data.frame
w_mean(x, weight = NULL, na.rm = TRUE)
w_median(x, weight = NULL, na.rm = TRUE)
w_var(x, weight = NULL, na.rm = TRUE)
w_sd(x, weight = NULL, na.rm = TRUE)
w_se(x, weight = NULL, na.rm = TRUE)
w_mad(x, weight = NULL, na.rm = TRUE)
w_sum(x, weight = NULL, na.rm = TRUE)
w_n(x, weight = NULL, na.rm = TRUE)
unweighted_valid_n(x, weight = NULL)
valid_n(x, weight = NULL)
w_max(x, weight = NULL, na.rm = TRUE)
w_min(x, weight = NULL, na.rm = TRUE)
w_cov(x, weight = NULL, use = c("pairwise.complete.obs", "complete.obs"))
w_cor(x, weight = NULL, use = c("pairwise.complete.obs", "complete.obs"))
w_pearson(x, weight = NULL, use = c("pairwise.complete.obs", "complete.obs"))
w_spearman(x, weight = NULL, use = c("pairwise.complete.obs", "complete.obs"))x |
a numeric vector (matrix/data.frame for correlations) containing the values whose weighted statistics is to be computed. |
weight |
a vector of weights to use for each element of x. Cases with
missing, zero or negative weights will be removed before calculations. If
|
na.rm |
a logical value indicating whether NA values should be stripped before the computation proceeds. Note that contrary to base R statistic functions the default value is TRUE (remove missing values). |
use |
|
If argument of correlation functions is data.frame with variable labels then
variables names will be replaced with labels. If this is undesirable behavior
use unvr function: w_cor(unvr(x)). Weighted spearman
correlation coefficients are calculated with rounded to nearest integer
weights. It gives the same result as in SPSS Statistics software. By now this
algorithm is not memory efficient.
a numeric value of length one/correlation matrix
data(mtcars)
dfs = mtcars %>% keep(mpg, disp, hp, wt)
with(dfs, w_mean(hp, weight = 1/wt))
# apply labels
dfs = modify(dfs, {
var_lab(mpg) = "Miles/(US) gallon"
var_lab(disp) = "Displacement (cu.in.)"
var_lab(hp) = "Gross horsepower"
var_lab(wt) = "Weight (1000 lbs)"
})
# weighted correlations with labels
w_cor(dfs, weight = 1/dfs$wt)
# without labels
w_cor(unvr(dfs), weight = 1/dfs$wt)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.