`svyscoretest.Rd`

Performs two versions of the efficient score test. These are the same
for a single parameter. In the `working`

score test, different
parameters are weighted according to the inverse of the estimated population Fisher
information. In the `pseudoscore`

test, parameters are weighted according to the
inverse of their estimated covariance matrix.

```
svyscoretest(model, drop.terms=NULL, add.terms=NULL,
method=c("working","pseudoscore","individual"),ddf=NULL,
lrt.approximation = "satterthwaite", ...)
# S3 method for svyglm
svyscoretest(model, drop.terms=NULL, add.terms=NULL,
method=c("working","pseudoscore","individual"), ddf=NULL,
lrt.approximation = "satterthwaite",fullrank=TRUE, ...)
```

- model
A model of a class having a

`svyscoretest`

method (currently just`svyglm`

)- drop.terms
Model formula giving terms to remove from

`model`

- add.terms
Model formula giving terms to add to

`model`

- method
The type of score test to use. For a single parameter they are equivalent. To report tests for each column separately use

`individual`

- ddf
denominator degrees of freedom for an F or linear combination of F distributions. Use

`Inf`

to get chi-squared distributions.`NULL`

asks for the model residual degrees of freedom, which is conservative.- lrt.approximation
For the working score, the method for computing/approximating the null distribution: see

`pchisqsum`

- fullrank
If

`FALSE`

and`method="individual"`

, keep even linearly dependent columns of the efficient score- ...
for future expansion

The `working`

score test will be asymptotically equivalent to the
Rao-Scott likelihood ratio test computed by `regTermTest`

and `anova.svyglm`

. The paper by Rao, Scott and Skinner calls this
a "naive" score test. The null distribution is a linear combination
of chi-squared (or F) variables.

The `pseudoscore`

test will be
asymptotically equivalent to the Wald test computed by
`regTermTest`

; it has a chi-squared (or F) null
distribution.

If `ddf`

is negative or zero, which can happen with large numbers
of predictors and small numbers of PSUs, it will be changed to 1 with a warning.

For "pseudoscore" and "working" score methods, a named vector with the test statistic, degrees of freedom, and p-value. For "individual" an object of class "svystat"

JNK Rao, AJ Scott, and C Rao, J., Scott, A., & Skinner, C. (1998). QUASI-SCORE TESTS WITH SURVEY DATA. Statistica Sinica, 8(4), 1059-1070.

```
data(myco)
dmyco<-svydesign(id=~1, strata=~interaction(Age,leprosy),weights=~wt,data=myco)
m_full<-svyglm(leprosy~I((Age+7.5)^-2)+Scar, family=quasibinomial, design=dmyco)
svyscoretest(m_full, ~Scar)
#> Rao-Scott X^2 ddf p
#> 1.076178e+01 5.020000e+02 1.108194e-03
svyscoretest(m_full,add.terms= ~I((Age+7.5)^-2):Scar)
#> Rao-Scott X^2 ddf p
#> 6.461623e-03 5.020000e+02 9.359637e-01
svyscoretest(m_full,add.terms= ~factor(Age), method="pseudo")
#> Warning: 1 linear dependency removed
#> X2 df ddf p
#> 3.221403e+01 4.000000e+00 5.020000e+02 6.491734e-24
svyscoretest(m_full,add.terms= ~factor(Age),method="individual",fullrank=FALSE)
#> total SE
#> factor(Age)12.5 6.2221 1.5180
#> factor(Age)17.5 2.7633 3.0650
#> factor(Age)22.5 -4.3934 2.3640
#> factor(Age)27.5 2.1967 1.6124
#> factor(Age)32.5 -3.4778 2.7989
svyscoretest(m_full,add.terms= ~factor(Age),method="individual")
#> Warning: 1 linear dependency removed
#> total SE
#> factor(Age)12.5 6.2221 1.5180
#> factor(Age)17.5 2.7633 3.0650
#> factor(Age)22.5 -4.3934 2.3640
#> factor(Age)27.5 2.1967 1.6124
```