TAM (version 2.4-9)

weighted_Stats: Descriptive Statistics for Weighted Data

Description

Some descriptive statistics for weighted data: variance, standard deviation, means, skewness, excess curtosis, quantiles and frequency tables. Missing values are automatically removed from the data.

Usage

weighted_mean(x, w = rep(1, length(x)) )
weighted_var(x, w = rep(1, length(x)), method = "unbiased")
weighted_sd(x, w = rep(1, length(x)), method = "unbiased")
weighted_skewness( x , w = rep(1,length(x)) )
weighted_curtosis( x , w = rep(1,length(x)) )
weighted_quantile( x , w=rep(1,length(x)), probs=seq(0,1,.25), type=NULL)
weighted_table( x , w = NULL, props=FALSE )

Arguments

x

A numeric vector. For weighted_table, a matrix with two columns can be used as input for cross-tabulation.

w

Optional vector of sample weights

method

Computation method (can be "unbiased" or "ML")), see stats::cov.wt

probs

Vector with probabilities

type

Quantile type. For unweighted data, quantile types 6 and 7 can be used (see stats::quantile). For weighted data, the quantile type "i/n" is used (see Hmisc::wtd.quantile)).

props

Logical indicating whether relative or abosolute frequencies should be calculated.

Value

Numeric value

See Also

See stats::weighted.mean for computing a weighted mean.

See stats::var for computing unweighted variances.

See stats::quantile and Hmisc::wtd.quantile) for quantiles.

Examples

# NOT RUN {
#############################################################################
# EXAMPLE 1: Toy example for weighted_var function
#############################################################################	

set.seed(9897)
# simulate data
N <- 10
x <- stats::rnorm(N)
w <- stats::runif(N)

#---- variance

# use weighted_var
weighted_var( x=x, w=w )
# use cov.wt
stats::cov.wt( data.frame(x=x) , w=w )$cov[1,1]
# }
# NOT RUN {
# use wtd.var from Hmisc package
Hmisc::wtd.var(x=x , weights = w , normwt=TRUE , method="unbiased")

#---- standard deviation
weighted_sd( x = x , w=w )

#---- mean
weighted_mean( x = x , w=w )
stats::weighted.mean( x=x , w=w )

#---- weighted quantiles for unweighted data
pvec <- c(.23 , .53 , .78 , .99 )  # choose probabilities
type <- 7

# quantiles for unweighted data
weighted_quantile( x , probs = pvec , type=type)
quantile( x , probs = pvec , type=type)
Hmisc::wtd.quantile(x,probs=pvec, type=type)

# quantiles for weighted data
pvec <- c(.23 , .53 , .78 , .99 )  # probabilities
weighted_quantile( x , w=w , probs = pvec)
Hmisc::wtd.quantile(x, weights=w , probs=pvec)

#--- weighted skewness and curtosis
weighted_skewness(x=x, w=w)
weighted_curtosis(x=x, w=w)

#############################################################################
# EXAMPLE 2: Descriptive statistics normally distributed data
#############################################################################

# simulate some normally distributed data
set.seed(7768)
x <- stats::rnorm( 10000 , mean=1.7, sd=1.2)
# some statistics
weighted_mean(x=x)
weighted_sd(x=x)
weighted_skewness(x=x)
weighted_curtosis(x=x)

#############################################################################
# EXAMPLE 3: Frequency tables
#############################################################################

#*****
# simulate data for weighted frequency tables
y <- scan()
   1 0  1 1   1 2   1 3    1 4
   2 0  2 1   2 2   2 3    2 4
   
y <- matrix( y , ncol=2 , byrow=TRUE)    
# define probabilities
set.seed(976)
pr <- stats::runif(10)
pr <- pr / sum(pr)
# sample data
N <- 300
x <- y[ sample( 1:10 , size=300, prob = pr , replace = TRUE ) , ]
w <- stats::runif( N , 0.5 , 1.5 )

# frequency table unweighted data
weighted_table(x[,2] )
table( x[,2] )

# weighted data and proportions
weighted_table(x[,2] , w=w, props=TRUE)

#*** contingency table
table( x[,1] , x[,2] )
weighted_table( x )
# table using weights
weighted_table( x , w=w )
# }