如何绘制bootstraping置信区间

Question

I am running some bootstrap confidence intervals and I would like to plot the confidence intervals with the mean. Something like this:

This is my model. As you can see DISTANCE and POS are factors.

 lmm1 <- lmer((total) ~ DISTANCE+POS + (1|NO_UNIT),data=TURN)
TURN$POS<-as.factor(TURN$POS)#Change position and distance to factors
TURN$DISTANCE<-as.factor(TURN$DISTANCE)
TURN$NO_UNIT <- as.factor(TURN$NO_UNIT)

This is the function I am using:

 mySumm <- function(.) { s <- sigma(.)
    c(beta =getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) }

# run bootstrap analysis for calculation of confidence intervals of parameter estimates
mod_lmm1_boot <- bootMer(lmm1,mySumm, nsim=300)

# confidence interval forcoefficient DISTANCE1
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=2)
# confidence interval forcoefficient DISTANCE2
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=3)
# confidence interval forcoefficient DISTANCE3
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=4)
# confidence interval forcoefficient DISTANCE4
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=5)
# confidence interval forcoefficient DISTANCECONTROL
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=6)
# confidence interval forcoefficient POS2
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=7)
# confidence interval forcoefficient POS3
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=8)
# confidence interval forcoefficient POS4
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=9)
# confidence interval forcoefficient POS5
boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=10)#I want to plot the confidence intervals and means corresponding to these indexes!

This is the TURN data frame

TREAT   NO_UNIT POS DISTANCE    total
NBNA    1   1   Control 2525837593
NBNA    1   2   Control 2127040915
NBNA    1   3   Control 1387070744
NBNA    1   4   Control 1458541776
NBNA    1   5   Control 1858881414
NBNA    2   1   Control NA
NBNA    2   2   Control 1481932857
NBNA    2   3   Control 2037767853
NBNA    2   4   Control 2056201434
NBNA    2   5   Control 2265049369
NBNA    3   1   Control 1474510932
NBNA    3   2   Control 1801028575
NBNA    3   3   Control 1542852992
NBNA    3   4   Control 2210304532
NBNA    3   5   Control 2557068715
NBA1    4   1   0   640224197.7
NBA1    4   2   1   704589654.3
NBA1    4   3   2   558153711.5
NBA1    4   4   3   2263088969
NBA1    4   5   4   1779212490
NBA1    5   1   0   1483773056
NBA1    5   2   1   1539240152
NBA1    5   3   2   1987871365
NBA1    5   4   3   2555767964
NBA1    5   5   4   2040386118
NBA1    6   1   0   1855829526
NBA1    6   2   1   1868973099
NBA1    6   3   2   1345878086
NBA1    6   4   3   1651096675
NBA1    6   5   4   1513168820
NBA5    7   1   4   1832017482
NBA5    7   2   3   1858590718
NBA5    7   3   2   1445652450
NBA5    7   4   1   1544741442
NBA5    7   5   0   1330161829
NBA5    8   1   4   1896550338
NBA5    8   2   3   2016638692
NBA5    8   3   2   1333723238
NBA5    8   4   1   1570307025
NBA5    8   5   0   1666068148
NBA5    9   1   4   NA
NBA5    9   2   3   1990898325
NBA5    9   3   2   1675553680
NBA5    9   4   1   1556562879
NBA5    9   5   0   1910142673
BNA 10  1   Control 370990618.1
BNA 10  2   Control 484424075.2
BNA 10  3   Control 659926517.8
BNA 10  4   Control NA
BNA 10  5   Control 1532572993
BNA 11  1   Control 590917346
BNA 11  2   Control 1826109624
BNA 11  3   Control 318371884.5
BNA 11  4   Control 3046708581
BNA 11  5   Control NA
BNA 12  1   Control 755992256.9
BNA 12  2   Control 457416788.1
BNA 12  3   Control 874742750.4
BNA 12  4   Control 67841374.52
BNA 12  5   Control 2933480357
BA1 13  1   0   12067695.33
BA1 13  2   1   10083668.91
BA1 13  3   2   6416950.819
BA1 13  4   3   7398691.286
BA1 13  5   4   10860980.63
BA1 14  1   0   11892423.38
BA1 14  2   1   27004799.05
BA1 14  3   2   597357273.2
BA1 14  4   3   1572190656
BA1 14  5   4   1249666803
BA1 15  1   0   38998930.08
BA1 15  2   1   279361097.3
BA1 15  3   2   350236872
BA1 15  4   3   931806452.5
BA1 15  5   4   NA
BA5 16  1   4   623714889
BA5 16  2   3   481547462
BA5 16  3   2   992879231.3
BA5 16  4   1   2287090423
BA5 16  5   0   1742484997
BA5 17  1   4   786425214.1
BA5 17  2   3   899998604.5
BA5 17  3   2   1244515257
BA5 17  4   1   2432196221
BA5 17  5   0   386404680.3
BA5 18  1   4   597970711
BA5 18  2   3   781618489.7
BA5 18  3   2   2024931390
BA5 18  4   1   1663706249
BA5 18  5   0   1231669010

Answer 1

First, to turn the mod_lmm1_boot object into a data frame you can use the tidy method for "boot" objects (from my broom package):

library(broom)
tidy(mod_lmm1_boot)

Which gives the output:

   term statistic         bias std.error
1 beta1 511476574  -2340765.59 250202968
2 beta2 264511629   8063563.41 239403518
3 beta3 104454362   5408454.64 237085206
4 beta4 391743711  12945670.41 231843390
5 beta5 188803173  -6839936.62 243065434
6 beta6 479700475   6934270.75 308630904
7 beta7 171444397    -87209.96  44159725
8 sigma 566047522 -10557609.04  50260359
9 sig01 476939097  10975306.03 121196856

You can also compute the confidence intervals you list with something like:

ci <- do.call(rbind, lapply(1:9, function(i) {
  boot.ci(mod_lmm1_boot,type="perc",conf=.95,index=i)$percent
}))

Which gives a matrix with 5 columns, where the 4th and 5th are your percentile confidence intervals.

You tagged your question with ggplot2, so here's the code to plot the resulting intervals in ggplot2:

library(broom)
library(ggplot2)

td <- tidy(mod_lmm1_boot) 
td$conf.low <- ci[, 4]
td$conf.high <- ci[, 5]

ggplot(td, aes(term, statistic)) +
  geom_point() +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high))