schouten: Schouten estimators for multiobserver agreement.

In magree: Implements the O'Connell-Dobson-Schouten Estimators of Agreement for Multiple Observers

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")

magree documentation built on Sept. 3, 2020, 9:07 a.m.