iccmulti {iccmult} | R Documentation |
Estimate ICC for nominal or ordinal categorical response data
Description
Estimate ICC for nominal or ordinal categorical response data
Usage
iccmulti(
cid,
y,
data,
alpha = 0.05,
method = c("rm", "mom"),
binmethod = c("aov", "aovs", "keq", "kpr", "keqs", "kprs", "stab", "ub", "fc", "mak",
"peq", "pgp", "ppr", "rm", "lin", "sim"),
ci.type = c("aov", "wal", "fc", "peq", "rm"),
kappa = 0.45,
nAGQ = 1,
M = 1000,
nowarnings = FALSE
)
Arguments
cid |
Cluster id variable. |
y |
Categorical response variable. |
data |
Dataframe containing 'cid' and 'y'. |
alpha |
Significance level for confidence interval computation. Default is 0.05. |
method |
Method used to estimate categorical ICC. A single method or multiple methods can be specified. Default is both resampling and moments estimators. See iccmult::iccmulti for more details. |
binmethod |
Method used to estimate binary ICC. A single or multiple methods can be specified. By default all 16 methods are returned. See full details in ICCbin::iccbin(). |
ci.type |
Type of confidence interval to be computed for binary ICC. By default, all 5 types will be returned See full details in ICCbin::iccbin() for more. |
kappa |
Value of Kappa to be used in computing Stabilized ICC when the binary response method 'stab' is chosen. Default value is 0.45. |
nAGQ |
An integer scaler, as in lme4::glmer(), denoting the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Used when the binary response method 'lin' is chosen. Default value is 1. |
M |
Number of Monte Carlo replicates used in binary ICC computation method 'sim'. Default is 1000. |
nowarnings |
Flag to turn off estimation warnings. Default is False. |
Value
Data frame or list of data frames with single column estimate of ICC, se(ICC), and lower and upper CI bounds.
Examples
iccdat4 <- rccat(rho=0.15, prop=c(0.15,0.25,0.20,0.40), noc=10, csize=25)
iccmulti(cid=cid, y=y, data=iccdat4)
iccdat3 <- rccat(rho=0.10, prop=c(0.30,0.25,0.45), noc=15, csize=50)
iccmulti(cid=cid, y=y, data=iccdat3)