c95.Rd
Small helper function that computes the 2.5% and 97.5% quantiles and the
mean of a vector. Useful for example when using function predict.bamlss
.
c95(x)
A numeric vector.
x <- rnorm(100)
c95(x)
#> 2.5% Mean 97.5%
#> -2.03558585 -0.08520532 1.96359896
if (FALSE) ## Example computing predictions.
set.seed(123)
d <- data.frame("x" = seq(-3, 3, length = 30))
d$y <- sin(d$x) + rnorm(30, sd = 0.3)
## Estimate model and compute predictions.
## with c95().
b <- bamlss(y ~ s(x), data = d)
#> AICc 32.4494 logPost -45.4219 logLik -9.3006 edf 5.4381 eps 0.5421 iteration 1
#> AICc 20.6556 logPost -37.4143 logLik -3.1010 edf 5.6297 eps 0.1766 iteration 2
#> AICc 15.6084 logPost -34.5604 logLik 0.1807 edf 6.0962 eps 0.1383 iteration 3
#> AICc 14.6331 logPost -34.0098 logLik 1.2677 edf 6.4519 eps 0.0783 iteration 4
#> AICc 14.5393 logPost -33.9662 logLik 1.6215 edf 6.6299 eps 0.0344 iteration 5
#> AICc 14.5353 logPost -33.9652 logLik 1.7003 edf 6.6740 eps 0.0081 iteration 6
#> AICc 14.5352 logPost -33.9653 logLik 1.7114 edf 6.6803 eps 0.0011 iteration 7
#> AICc 14.5352 logPost -33.9653 logLik 1.7127 edf 6.6810 eps 0.0001 iteration 8
#> AICc 14.5352 logPost -33.9653 logLik 1.7129 edf 6.6811 eps 0.0000 iteration 9
#> AICc 14.5352 logPost -33.9653 logLik 1.7129 edf 6.6811 eps 0.0000 iteration 9
#> elapsed time: 0.12sec
#> Starting the sampler...
#>
#> | | 0% 2.02sec
#> |* | 5% 1.99sec 0.10sec
#> |** | 10% 1.84sec 0.20sec
#> |*** | 15% 1.76sec 0.31sec
#> |**** | 20% 1.66sec 0.41sec
#> |***** | 25% 1.59sec 0.53sec
#> |****** | 30% 1.48sec 0.63sec
#> |******* | 35% 1.38sec 0.74sec
#> |******** | 40% 1.28sec 0.85sec
#> |********* | 45% 1.18sec 0.96sec
#> |********** | 50% 1.08sec 1.08sec
#> |*********** | 55% 0.97sec 1.18sec
#> |************ | 60% 0.86sec 1.29sec
#> |************* | 65% 0.75sec 1.40sec
#> |************** | 70% 0.65sec 1.51sec
#> |*************** | 75% 0.54sec 1.62sec
#> |**************** | 80% 0.43sec 1.73sec
#> |***************** | 85% 0.33sec 1.84sec
#> |****************** | 90% 0.22sec 2.00sec
#> |******************* | 95% 0.11sec 2.12sec
#> |********************| 100% 0.00sec 2.28sec
p <- predict(b, model = "mu", FUN = c95)
plot(d)
matplot(d$x, p, type = "l", lty = c(2, 1, 2),
col = "black", add = TRUE)
## Example extracting coefficients.
coef(b, FUN = c95)
#> 2.5% Mean 97.5%
#> mu.s.s(x).b1 -2.7128249 -2.07000703 -1.44983517
#> mu.s.s(x).b2 -1.4800066 0.31732825 2.23923359
#> mu.s.s(x).b3 -0.6976676 -0.17336785 0.35867711
#> mu.s.s(x).b4 -1.2198943 0.15481990 1.54778071
#> mu.s.s(x).b5 -0.4754548 0.05119379 0.52651786
#> mu.s.s(x).b6 -1.5442848 -0.17577973 1.04634256
#> mu.s.s(x).b7 -0.6296562 -0.05705302 0.40215896
#> mu.s.s(x).b8 -2.9576602 -0.04595215 2.79233488
#> mu.s.s(x).b9 -2.5895736 -1.51987111 -0.34548875
#> mu.s.s(x).tau21 2.8397411 13.59739370 48.98173816
#> mu.s.s(x).alpha 1.0000000 1.00000000 1.00000000
#> mu.p.(Intercept) -0.1202318 -0.01986826 0.06927125
#> mu.p.alpha 1.0000000 1.00000000 1.00000000
#> sigma.p.(Intercept) -1.6712943 -1.39173221 -1.09268439
#> sigma.p.alpha 0.5042547 0.93969229 1.00000000