Compute weighted summaries for declared objects
Source:R/w_IQR.R, R/w_fivenum.R, R/w_mean.R, and 8 more
weighted.RdFunctions to compute weighted tables or summaries, based on a vector of
frequency weights. These are reimplementations of various existing functions,
adapted to objects of class "declared" (see Details below)
Usage
w_IQR(x, wt = NULL, na.rm = FALSE, ...)
w_fivenum(x, wt = NULL, na.rm = FALSE)
w_mean(x, wt = NULL, trim = 0, na.rm = TRUE)
w_median(x, wt = NULL, na.rm = TRUE, ...)
w_mode(x, wt = NULL)
w_quantile(x, wt = NULL, probs = seq(0, 1, 0.25), na.rm = TRUE, ...)
w_sd(x, wt = NULL, method = NULL, na.rm = TRUE)
w_standardize(x, wt = NULL, na.rm = TRUE)
w_summary(x, wt = NULL, ...)
w_table(
x,
y = NULL,
wt = NULL,
values = FALSE,
valid = TRUE,
observed = TRUE,
margin = NULL,
vlabel = FALSE
)
w_var(x, wt = NULL, method = NULL, na.rm = TRUE)Arguments
- x
A numeric vector for summaries, or declared / factor for frequency tables
- wt
A numeric vector of frequency weights
- na.rm
Logical, should the empty missing values be removed?
- ...
Further arguments passed to or from other methods.
- trim
A fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.
- probs
Numeric vector of probabilities with values in [0,1]
- method
Character, specifying how the result is scaled, see 'Details' below.
- y
An optional variable, to create crosstabs; must have the same length as x
- values
Logical, print the values in the table rows
- valid
Logical, print separate percent distribution for valid values, if any missing values are present; for cross tables, use valid values only
- observed
Logical, print the observed categories only
- margin
Numeric, indicating the margin to calculate crosstab proportions: 0 from the total, 1 from row totals and 2 from column totals
- vlabel
Logical, print the variable label, if existing
Details
Weighted summaries
A frequency table is usually performed for a categorical variable, displaying the frequencies of the respective categories. Note that general variables containing text are not necessarily factors, despite having a small number of characters.
A general table of frequencies, using the base function table(), ignores
the defined missing values (which are all stored as NAs). The
reimplementation of this function in w_table() takes care of this detail,
and presents frequencies for each separately defined missing values. Similar
reimplementations for the other functions have the same underlying objective.
It is also possible to perform a frequency table for numerical variables, if the number of values is limited (an arbitrary and debatable upper limit of 15 is used). An example of such variable can be the number of children, where each value can be interpreted as a class, containing a single value (for instance 0 meaning the category of people with no children).
Objects of class declared are not pure categorical variables (R factors)
but they are nevertheless interpreted as if they were factors, to allow
producing frequency tables. Given the high similarity with package
haven, objects of class haven_labelled_spss are automatically
coerced to objects of class declared and treated accordingly.
The argument values makes sense only when the input is of family class
declared, otherwise for regular (base R) factors the values are
just a sequence of numbers.
The later introduced argument observed is useful in situations when a
variable has a very large number of potential values, and a smaller subset of
actually observed ones. As an example, the variable “Occupation” has
hundreds of possible values in the ISCO08 codelist, and not all of them might
be actually observed. When activated, this argument restricts the printed
frequency table to the subset of observed values only.
The argument method can be one of "unbiased" or "ML".
When this is set to "unbiased", the result is an unbiased estimate
using Bessel's correction. When this is set to "ML", the result is the
maximum likelihood estimate for a Gaussian distribution.
The argument wt refers only to frequency weights. Users should be
aware of the differences between frequency weights, analytic weights,
probability weights, design weights, post-stratification weights etc. For
purposes of inferential testing, Thomas Lumley's package survey
should be employed.
If no frequency weights are provided, the result is identical to the corresponding base functions.
The function w_quantile() extensively borrowed ideas from packages
stats and Hmisc, to ensure a constant interpolation that would
produce the same quantiles if no weights are provided or if all
weights are equal to 1.
Other arguments can be passed to the stats function quantile() via the
three dots ... argument, and their extensive explanation is found in the
corresponding stats function's help page.
For all functions, the argument na.rm refers to the empty missing values
and its default is set to TRUE. The declared missing values are automatically
eliminated from the summary statistics, even if this argument is deactivated.
The function w_mode() returns the weighted mode of a variable. Unlike the
other functions where the prefix w_ signals a weighted version of the
base function with the same name, this has nothing to do with the base
function mode() which refers to the storage mode / type of an R object.
Examples
set.seed(215)
# a pure categorical variable
x <- factor(sample(letters[1:5], 215, replace = TRUE))
w_table(x)
#>
#> fre rel per cpd
#> ----------------------
#> a 37 0.172 17.2 17.2
#> b 45 0.209 20.9 38.1
#> c 43 0.200 20.0 58.1
#> d 44 0.205 20.5 78.6
#> e 46 0.214 21.4 100.0
#> ----------------------
#> 215 1.000 100.0
#>
# simulate number of children
x <- sample(0:4, 215, replace = TRUE)
w_table(x)
#>
#> fre rel per cpd
#> ----------------------
#> 0 36 0.167 16.7 16.7
#> 1 41 0.191 19.1 35.8
#> 2 49 0.228 22.8 58.6
#> 3 45 0.209 20.9 79.5
#> 4 44 0.205 20.5 100.0
#> ----------------------
#> 215 1.000 100.0
#>
# simulate a Likert type response scale from 1 to 7
values <- sample(c(1:7, -91), 215, replace = TRUE)
x <- declared(values, labels = c("Good" = 1, "Bad" = 7))
w_table(x)
#>
#> fre rel per cpd
#> ----------------------
#> -91 16 0.074 7.4 7.4
#> Good 25 0.116 11.6 19.1
#> 2 25 0.116 11.6 30.7
#> 3 32 0.149 14.9 45.6
#> 4 37 0.172 17.2 62.8
#> 5 24 0.112 11.2 74.0
#> 6 21 0.098 9.8 83.7
#> Bad 35 0.163 16.3 100.0
#> ----------------------
#> 215 1.000 100.0
#>
# Defining missing values
missing_values(x) <- -91
w_table(x)
#>
#> fre rel per vld cpd
#> ----------------------------
#> Good 25 0.116 11.6 12.6 12.6
#> 2 25 0.116 11.6 12.6 25.1
#> 3 32 0.149 14.9 16.1 41.2
#> 4 37 0.172 17.2 18.6 59.8
#> 5 24 0.112 11.2 12.1 71.9
#> 6 21 0.098 9.8 10.6 82.4
#> Bad 35 0.163 16.3 17.6 100.0
#> ----
#> -91 16 0.074 7.4
#> ----------------------------
#> 215 1.000 100.0
#>
# Defined missing values with labels
values <- sample(c(1:7, -91, NA), 215, replace = TRUE)
x <- declared(
values,
labels = c("Good" = 1, "Bad" = 7, "Don't know" = -91),
na_values = -91
)
w_table(x)
#>
#> fre rel per vld cpd
#> ----------------------------
#> Good 20 0.093 9.3 12.0 12.0
#> 2 33 0.153 15.3 19.9 31.9
#> 3 27 0.126 12.6 16.3 48.2
#> 4 27 0.126 12.6 16.3 64.5
#> 5 22 0.102 10.2 13.3 77.7
#> 6 22 0.102 10.2 13.3 91.0
#> Bad 15 0.070 7.0 9.0 100.0
#> -----
#> Don't know 24 0.112 11.2
#> NA 25 0.116 11.6
#> ----------------------------
#> 215 1.000 100.0
#>
# Including the values in the table of frequencies
w_table(x, values = TRUE)
#>
#> fre rel per vld cpd
#> ----------------------------
#> Good 1 20 0.093 9.3 12.0 12.0
#> 2 2 33 0.153 15.3 19.9 31.9
#> 3 3 27 0.126 12.6 16.3 48.2
#> 4 4 27 0.126 12.6 16.3 64.5
#> 5 5 22 0.102 10.2 13.3 77.7
#> 6 6 22 0.102 10.2 13.3 91.0
#> Bad 7 15 0.070 7.0 9.0 100.0
#> -----
#> Don't know -91 24 0.112 11.2
#> NA 25 0.116 11.6
#> ----------------------------
#> 215 1.000 100.0
#>
# An example involving multiple variables
DF <- data.frame(
Area = declared(
sample(1:2, 215, replace = TRUE, prob = c(0.45, 0.55)),
labels = c(Rural = 1, Urban = 2)
),
Gender = declared(
sample(1:2, 215, replace = TRUE, prob = c(0.55, 0.45)),
labels = c(Males = 1, Females = 2)
),
Age = sample(18:90, 215, replace = TRUE),
Children = sample(0:5, 215, replace = TRUE)
)
w_table(DF$Gender)
#>
#> fre rel per cpd
#> ----------------------
#> Males 119 0.553 55.3 55.3
#> Females 96 0.447 44.7 100.0
#> ----------------------
#> 215 1.000 100.0
#>
w_sd(DF$Age)
#> [1] 20.19372
# Weighting: observed proportions
op <- proportions(with(DF, table(Gender, Area)))
# Theoretical proportions: 53% Rural, and 50.2% Females
tp <- rep(c(0.53, 0.47), each = 2) * rep(c(0.498, 0.502), 2)
# Corrections by strata
fweights <- tp / op
DF$fweight <- fweights[match(10 * DF$Area + DF$Gender, c(11, 12, 21, 22))]
with(DF, w_table(Gender, wt = fweight))
#>
#> fre rel per cpd
#> ----------------------
#> Males 107 0.498 49.8 49.8
#> Females 108 0.502 50.2 100.0
#> ----------------------
#> 215 1.000 100.0
#>
with(DF, w_mean(Age, wt = fweight))
#> [1] 55.12584
with(DF, w_quantile(Age, wt = fweight))
#>
#> 0% 25% 50% 75% 100%
#> 18 38 55 71 90
#>