Description Usage Arguments Value Author(s) See Also Examples
Computes the log likelihood ratio and p-value for data that is imputed with mice
1 2 3 4 |
step2step3 |
The likelihood ratio values for simulated data as obtained with the ppc.step2step3 function. |
imp |
A mids object created with the R-package mice |
model |
The lavaan model that is to be applied to the data |
effectsize |
Logic; if TRUE, the constraints concern effectsizes. |
s.i |
A vector of length p holding with indices for the (pooled) standard deviation parameters with which the effect sizes should be computed |
sample.cov |
Numeric matrix. A sample variance-covariance matrix. The rownames and/or colnames must contain the observed variable names. For a multiple group analysis, a list with a variance-covariance matrix for each group. Note that if maximum likelihood estimation is used and likelihood="normal", the user provided covariance matrix is internally rescaled by multiplying it with a factor (N-1)/N, to ensure that the covariance matrix has been divided by N. This can be turned off by setting the sample.cov.rescale argument to FALSE. |
sample.mean |
A sample mean vector. For a multiple group analysis, a list with a mean vector for each group. |
sample.nobs |
Number of observations if the full data frame is missing and only sample moments are given. For a multiple group analysis, a list or a vector with the number of observations for each group. |
group |
A variable name in the data frame defining the groups in a multiple group analysis. |
cluster |
The cluster variable for multilevel data (beta!). |
constraints |
Additional (in)equality constraints not yet included in the model syntax. See model.syntax for more information. Note that the replication hypothesis should not be specified here! |
WLS.V |
A user provided weight matrix to be used by estimator "WLS"; if the estimator is "DWLS", only the diagonal of this matrix will be used. For a multiple group analysis, a list with a weight matrix for each group. The elements of the weight matrix should be in the following order (if all data is continuous): first the means (if a meanstructure is involved), then the lower triangular elements of the covariance matrix including the diagonal, ordered column by column. In the categorical case: first the thresholds (including the means for continuous variables), then the slopes (if any), the variances of continuous variables (if any), and finally the lower triangular elements of the correlation/covariance matrix excluding the diagonal, ordered column by column. |
NACOV |
A user provided matrix containing the elements of (N times) the asymptotic variance-covariance matrix of the sample statistics. For a multiple group analysis, a list with an asymptotic variance-covariance matrix for each group. See the WLS.V argument for information about the order of the elements. |
bayes |
Logic; if TRUE, a Bayesian estimator is used. |
dp |
blavaan default prior distributions on different types of parameters, typically the result of a call to dpriors(). See the dpriors() help file for more information. |
nchains |
A scalar indicating the number of chains to be used in the Bayesian analysis. Default value = 2. |
pT |
The average parameter table |
llratio.i |
The likelihood ratio values for each of the imputed datasets |
pvals |
The prior-predictive p-values for each of the imputed datasets |
M. A. J. Zondervan-Zwijnenburg
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | #the following example can be used, but takes >10 seconds
#create data
rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) }
#step 1 input
#create/load data
n.o=30 #sample size original data
y.o <- data.frame(y=rnorm2(n.o,0,1),x=rnorm2(n.o,3,1))
n.r=80 #sample size new data
y.r <- data.frame(y=rnorm2(n.r,0.5,1),x=rnorm2(n.r,3,1))
y.r$y[runif(5,1,n.r)] <- NA #random missing data
#blavaan model
model <- '
y ~ x #regression
y ~1 #intercept not default in lavaan (but is in blavaan)
'
step1.reg <- ppc.step1(y.o=y.o,model=model,n.r=n.r)
#H0: #reg > est, int = est = B1>0.302 & B0= -0.878
pT <- step1.reg$pT #parameter table
int.id <- which(pT$lhs=="y"&pT$op=="~1"&pT$rhs=="") #identify B0
reg.id <- which(pT$lhs=="y"&pT$op=="~"&pT$rhs=="x") #identify B1
hyp <- cbind(pT[c(int.id,reg.id),"plabel"],c("=",">"),c(pT[c(int.id,reg.id),"est"]))
print(hyp)
H0 <- paste(hyp[,1],hyp[,2],hyp[,3],collapse="&")
step23.reg <- ppc.step2step3(step1=step1.reg,y.r=NULL,model=model,H0)
y.r$y[runif(5,1,n.r)] <- NA #random missing data
imp <- mice(y.r,maxit=10,m=10)
llratio.imp(step2step3=step23.reg,imp=imp,model=model)
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