# svyquantile

##### Summary statistics for sample surveys

Compute means, variances, quantiles, and cross-tabulations for data from complex surveys.

##### Usage

```
svyquantile(x, design, quantiles, method = "linear", f = 1)
svymean(x, design, na.rm=FALSE)
svyvar(x, design, na.rm=FALSE)
svytable(formula, design, Ntotal = NULL)
```

##### Arguments

##### Details

These functions perform weighted estimation, with each observation being weighted by the inverse of its sampling probability. The `svymean`

and `svyvar`

functions also give precision estimates that incorporate the effects of stratification and clustering. The first three functions are similar to the standard functions whose names do not begin with `svy`

.

The `svytable`

function computes a weighted crosstabulation. If the sampling probabilities supplied to `svydesign`

were actual probabilities (rather than relative probabilities) this estimates a full population crosstabulation. Otherwise it estimates only relative proportions and should be normalised to some convenient total such as 100 or 1.0 by specifying the `Ntotal`

argument.

##### Value

- The first three functions return vectors, the last returns an
`xtabs`

object.

##### References

~put references to the literature/web site here ~

##### See Also

##### Examples

```
#population
df<-data.frame(x=rnorm(1000),z=rep(0:4,200))
df$y<-with(df, 3+3*x*z)
#sampling fraction
df$p<-with(df, exp(x)/(1+exp(x)))
#sample
xi<-rbinom(1000,1,df$p)
sdf<-df[xi==1,]
#survey design object: independent sampling,
dxi<-svydesign(~0,~p,data=sdf)
dxi
mean(df$x) #right
mean(sdf$x) #wrong
svymean(sdf$x,dxi) #right
var(df$x) #right
var(sdf$x) #wrong
svyvar(sdf$x,dxi) #right
quantile(df$x,c(0.025,0.5,0.975)) #right
quantile(sdf$x,c(0.025,0.5,0.975)) #wrong
svyquantile(sdf$x,design=dxi,c(0.025,0.5,0.975)) #right
table(sdf$z) # sample table
svytable(~z, dxi) # estimated population table
```

*Documentation reproduced from package survey, version 0.9-1, License: LGPL*