Calculate confidence limits for parameters

Calculate Wald og Likelihood based (profile likelihood) confidence intervals

Usage

# S3 method for class 'lvmfit'
confint(
  object,
  parm = seq_len(length(coef(object))),
  level = 0.95,
  profile = FALSE,
  curve = FALSE,
  n = 20,
  interval = NULL,
  lower = TRUE,
  upper = TRUE,
  ...
)

Arguments

object: lvm-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 log-likelihood in over the interval defined by interval
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

Value

A 2xp matrix with columns of lower and upper confidence limits

Details

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 log-likelihood with tau being fixed.

Author

Klaus K. Holst

Examples


m <- lvm(y~x)
d <- sim(m,100)
e <- estimate(lvm(y~x), d)
confint(e,3,profile=TRUE)
#>          2.5 %   97.5 %
#> y~~y 0.5888514 1.026329
confint(e,3)
#>          2.5 %    97.5 %
#> y~~y 0.5547589 0.9802276
 ## Reduce Ex.timings
B <- bootstrap(e,R=50)
B
#> Non-parametric bootstrap statistics (R=50):
#> 
#>      Estimate     Bias         Std.Err      2.5 %        97.5 %      
#> y     0.124504326 -0.007753062  0.073936733  0.008375347  0.288089457
#> y~x   1.061750034  0.013940880  0.074844578  0.949601761  1.235976345
#> y~~y  0.767493210 -0.026454597  0.100031344  0.584892411  0.986727053
#>