schouten {magree}

Schouten estimators for multiobserver agreement.

Description

Use the Schouten estimator of agreement for nominal or ordinal data. This includes a range of statistics on agreement.

Usage

schouten(X, weights=c("unweighted","linear","quadratic","user"), w=NULL,
score=NULL)

Arguments

X

A matrix or data-frame with subjects as rows and observers as columns.

weights

"unweighted"

For nominal categories - only perfect agreement is counted.

"linear"

For ordinal categories where disagreement is proportional to the distance between the categories. This is analogous to the agreement weights w_{i,j}=1-|i-j|/(c-1).

"quadratic"

For ordinal categories where disagreement is proportional to the square of the distance between the categories. This is analogous to the agreement weights w_{i,j}=1-(i-j)^2/(c-1)^2.

"user"

An indicator for a user-defined weight matrix. The weights argument will be defined as "user" if the w argument is specified.

w

A user-defined weights matrix. This argument takes precedence over weights and score if it is specified and the weight argument will be defined as "user".

score

A user-defined set of scores for each category. If this is not specified, it is assumed that score=1:L, where L is the number of categories. This is used with the weights argument to define the w matrix.

Details

Fortran code was written by Mark Clements based on the algorithms in Schouten (1982).

The output object is closely related to the Fortan code. Not all of the variance terms are currently used in the print, summary and plot methods.

Value

N

Number of subjects

M

Number of observers

L

Number of categories

data

Re-formatted X

w

Weight matrix

kab

Kappas between each pair of observers

ka

Average kappas for each observer

kappa

Average kappa

pab, pa, p, ma, qab, qa, q, oab, eab, oa, ea, o, e, wa, wab

Working fields

varkab

Variances for kab

varka

Variances for ka

vark

Variance for the kappa

covkka

Covariance term between the overall average kappa and the average kappas for each observer

chi

Chi-squared statistics comparing the overall average kappa and the average kappa for each observer (df=1 under the null hypothesis)

pchi

P-values that the overall average kappa equals the average kappa for each observer

var0kab

Variance for kab under the null hypothesis

var0ka

Variance for ka under the null hypothesis

var0k

Variance for the overall average kappa under the null hypothesis

p0

P-value for kappa=0

p0a

P-values that the average kappa for a observer equals zero (i.e. ka=0)

weights

As input

X

As input

call

As per sys.call(), to allow for using update

See Also

magree, oconnell.

Examples

## Weights matrix used by Schouten (1982)
w <- outer(1:5,1:5,function(x,y) ((x<=2 & y<=2) | (x>=3 & y>=3))+0)
fit <- schouten(landis,w=w) # user-defined weights

summary(fit) # Schouten (1982), Tables 2 and 5

## we can fit the same model with oconnell() or magree() using the score argument
magree(landis,score=c(1,1,2,2,2))

## plot of the average kappas by observer
plot(fit, type="kappa by observer")