Calculate Wald og Likelihood based (profile likelihood) confidence intervals
# S3 method for lvmfit confint( object, parm = seq_len(length(coef(object))), level = 0.95, profile = FALSE, curve = FALSE, n = 20, interval = NULL, lower = TRUE, upper = TRUE, ... )
object 


parm  Index of which parameters to calculate confidence limits for. 
level  Confidence level 
profile  Logical expression defining whether to calculate confidence limits via the profile log likelihood 
curve  if FALSE and profile is TRUE, confidence limits are returned. Otherwise, the profile curve is returned. 
n  Number of points to evaluate profile loglikelihood in
over the interval defined by 
interval  Interval over which the profiling is done 
lower  If FALSE the lower limit will not be estimated (profile intervals only) 
upper  If FALSE the upper limit will not be estimated (profile intervals only) 
...  Additional arguments to be passed to the low level functions 
A 2xp matrix with columns of lower and upper confidence limits
Calculates either Wald confidence limits: $$\hat{\theta} \pm z_{\alpha/2}*\hat\sigma_{\hat\theta}$$ or profile likelihood confidence limits, defined as the set of value \(\tau\): $$logLik(\hat\theta_{\tau},\tau)logLik(\hat\theta)< q_{\alpha}/2$$
where \(q_{\alpha}\) is the \(\alpha\) fractile of the \(\chi^2_1\) distribution, and \(\hat\theta_{\tau}\) are obtained by maximizing the loglikelihood with tau being fixed.
bootstrap{lvm}
Klaus K. Holst
#> 2.5 % 97.5 % #> y~~y 0.5888514 1.026329#> 2.5 % 97.5 % #> y~~y 0.5547589 0.9802276#> Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_lapply1’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallelsafe random numbers are produced via the L'EcuyerCMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".B#> Nonparametric bootstrap statistics (R=50): #> #> Estimate Bias Std.Err 2.5 % 97.5 % #> y 0.124504326 0.019136487 0.091432200 0.048940002 0.294380917 #> y~x 1.061750034 0.006653642 0.094422581 0.907870490 1.231099705 #> y~~y 0.767493210 0.003652062 0.104073510 0.572331738 0.960123567 #>