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,
...
)
```

## 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.

## 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)
#> Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_lapply-1’) 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, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".
B
#> Non-parametric 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
#>
```