epil2.RdExtended version of the epil dataset of the MASS package.
The three transformed variables Visit, Base, and
Age used by Booth et al. (2003) have been added to epil.
epil2A data frame with 236 observations on the following 12 variables:
yan integer vector.
trta factor with levels "placebo" and
"progabide".
basean integer vector.
agean integer vector.
V4an integer vector.
subjectan integer vector.
periodan integer vector.
lbasea numeric vector.
lagea numeric vector.
(rep(1:4,59) - 2.5) / 5.
log(base/4).
log(age).
Booth, J.G., G. Casella, H. Friedl, and J.P. Hobert. (2003) Negative binomial loglinear mixed models. Statistical Modelling 3, 179--191.
# \donttest{
epil2$subject <- factor(epil2$subject)
op <- options(digits=3)
(fm <- glmmTMB(y ~ Base*trt + Age + Visit + (Visit|subject),
data=epil2, family=nbinom2))
#> Formula: y ~ Base * trt + Age + Visit + (Visit | subject)
#> Data: epil2
#> AIC BIC logLik df.resid
#> 1269 1304 -625 226
#> Random-effects (co)variances:
#>
#> Conditional model:
#> Groups Name Std.Dev. Corr
#> subject (Intercept) 0.4660
#> Visit 0.0073 -1.00
#>
#> Number of obs: 236 / Conditional model: subject, 59
#>
#> Dispersion parameter for nbinom2 family (): 7.46
#>
#> Fixed Effects:
#>
#> Conditional model:
#> (Intercept) Base trtprogabide Age
#> -1.322 0.884 -0.928 0.473
#> Visit Base:trtprogabide
#> -0.268 0.336
meths <- methods(class = class(fm))
if((Rv <- getRversion()) > "3.1.3") {
funs <- attr(meths, "info")[, "generic"]
funs <- setdiff(funs, "profile") ## too slow! pkgdown is trying to run this??
for(fun in funs[is.na(match(funs, "getME"))]) {
cat(sprintf("%s:\n-----\n", fun))
r <- tryCatch( get(fun)(fm), error=identity)
if (inherits(r, "error")) cat("** Error:", r$message,"\n")
else tryCatch( print(r) )
cat(sprintf("---end{%s}--------------\n\n", fun))
}
}
#> Anova:
#> -----
#> Analysis of Deviance Table (Type II Wald chisquare tests)
#>
#> Response: y
#> Chisq Df Pr(>Chisq)
#> Base 107.66 1 <2e-16 ***
#> trt 4.52 1 0.033 *
#> Age 1.79 1 0.180
#> Visit 2.40 1 0.121
#> Base:trt 2.71 1 0.100 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---end{Anova}--------------
#>
#> Effect:
#> -----
#> ** Error: argument "mod" is missing, with no default
#> ---end{Effect}--------------
#>
#> VarCorr:
#> -----
#>
#> Conditional model:
#> Groups Name Std.Dev. Corr
#> subject (Intercept) 0.4660
#> Visit 0.0073 -1.00
#> ---end{VarCorr}--------------
#>
#> anova:
#> -----
#> ** Error: no single-model anova() method for glmmTMB
#> ---end{anova}--------------
#>
#> coef:
#> -----
#> $subject
#> (Intercept) Base trtprogabide Age Visit Base:trtprogabide
#> 1 -1.286 0.884 -0.928 0.473 -0.269 0.336
#> 2 -1.275 0.884 -0.928 0.473 -0.269 0.336
#> 3 -1.037 0.884 -0.928 0.473 -0.273 0.336
#> 4 -1.196 0.884 -0.928 0.473 -0.270 0.336
#> 5 -1.312 0.884 -0.928 0.473 -0.269 0.336
#> 6 -1.505 0.884 -0.928 0.473 -0.266 0.336
#> 7 -1.442 0.884 -0.928 0.473 -0.267 0.336
#> 8 -0.975 0.884 -0.928 0.473 -0.274 0.336
#> 9 -1.489 0.884 -0.928 0.473 -0.266 0.336
#> 10 -0.528 0.884 -0.928 0.473 -0.281 0.336
#> 11 -1.192 0.884 -0.928 0.473 -0.270 0.336
#> 12 -1.353 0.884 -0.928 0.473 -0.268 0.336
#> 13 -1.396 0.884 -0.928 0.473 -0.267 0.336
#> 14 -1.395 0.884 -0.928 0.473 -0.267 0.336
#> 15 -1.532 0.884 -0.928 0.473 -0.265 0.336
#> 16 -2.076 0.884 -0.928 0.473 -0.257 0.336
#> 17 -1.979 0.884 -0.928 0.473 -0.258 0.336
#> 18 -1.168 0.884 -0.928 0.473 -0.271 0.336
#> 19 -1.545 0.884 -0.928 0.473 -0.265 0.336
#> 20 -1.424 0.884 -0.928 0.473 -0.267 0.336
#> 21 -1.314 0.884 -0.928 0.473 -0.269 0.336
#> 22 -1.042 0.884 -0.928 0.473 -0.273 0.336
#> 23 -1.598 0.884 -0.928 0.473 -0.264 0.336
#> 24 -1.253 0.884 -0.928 0.473 -0.270 0.336
#> 25 -0.486 0.884 -0.928 0.473 -0.282 0.336
#> 26 -1.725 0.884 -0.928 0.473 -0.262 0.336
#> 27 -1.300 0.884 -0.928 0.473 -0.269 0.336
#> 28 -1.108 0.884 -0.928 0.473 -0.272 0.336
#> 29 -1.609 0.884 -0.928 0.473 -0.264 0.336
#> 30 -1.469 0.884 -0.928 0.473 -0.266 0.336
#> 31 -1.612 0.884 -0.928 0.473 -0.264 0.336
#> 32 -0.867 0.884 -0.928 0.473 -0.276 0.336
#> 33 -0.935 0.884 -0.928 0.473 -0.274 0.336
#> 34 -1.599 0.884 -0.928 0.473 -0.264 0.336
#> 35 -0.447 0.884 -0.928 0.473 -0.282 0.336
#> 36 -0.856 0.884 -0.928 0.473 -0.276 0.336
#> 37 -1.094 0.884 -0.928 0.473 -0.272 0.336
#> 38 -1.924 0.884 -0.928 0.473 -0.259 0.336
#> 39 -1.380 0.884 -0.928 0.473 -0.268 0.336
#> 40 -1.327 0.884 -0.928 0.473 -0.268 0.336
#> 41 -1.823 0.884 -0.928 0.473 -0.261 0.336
#> 42 -1.218 0.884 -0.928 0.473 -0.270 0.336
#> 43 -0.986 0.884 -0.928 0.473 -0.274 0.336
#> 44 -1.324 0.884 -0.928 0.473 -0.268 0.336
#> 45 -1.254 0.884 -0.928 0.473 -0.269 0.336
#> 46 -1.001 0.884 -0.928 0.473 -0.273 0.336
#> 47 -1.238 0.884 -0.928 0.473 -0.270 0.336
#> 48 -1.655 0.884 -0.928 0.473 -0.263 0.336
#> 49 -0.742 0.884 -0.928 0.473 -0.278 0.336
#> 50 -1.516 0.884 -0.928 0.473 -0.265 0.336
#> 51 -1.504 0.884 -0.928 0.473 -0.266 0.336
#> 52 -1.992 0.884 -0.928 0.473 -0.258 0.336
#> 53 -0.950 0.884 -0.928 0.473 -0.274 0.336
#> 54 -1.672 0.884 -0.928 0.473 -0.263 0.336
#> 55 -1.158 0.884 -0.928 0.473 -0.271 0.336
#> 56 -0.369 0.884 -0.928 0.473 -0.283 0.336
#> 57 -1.892 0.884 -0.928 0.473 -0.260 0.336
#> 58 -2.136 0.884 -0.928 0.473 -0.256 0.336
#> 59 -1.249 0.884 -0.928 0.473 -0.270 0.336
#>
#> ---end{coef}--------------
#>
#> confint:
#> -----
#> 2.5 % 97.5 % Estimate
#> (Intercept) -3.67e+00 1.02e+00 -1.3225
#> Base 6.27e-01 1.14e+00 0.8843
#> trtprogabide -1.72e+00 -1.41e-01 -0.9284
#> Age -2.19e-01 1.16e+00 0.4727
#> Visit -6.08e-01 7.11e-02 -0.2684
#> Base:trtprogabide -6.40e-02 7.37e-01 0.3363
#> Std.Dev.(Intercept)|subject 3.57e-01 6.08e-01 0.4660
#> Std.Dev.Visit|subject 2.93e-26 1.82e+21 0.0073
#> Cor.Visit.(Intercept)|subject -1.00e+00 1.00e+00 -0.9990
#> ---end{confint}--------------
#>
#> deviance:
#> -----
#> [1] 226
#> ---end{deviance}--------------
#>
#> df.residual:
#> -----
#> [1] 226
#> ---end{df.residual}--------------
#>
#> emm_basis:
#> -----
#> ** Error: argument "trms" is missing, with no default
#> ---end{emm_basis}--------------
#>
#> extractAIC:
#> -----
#> [1] 10 1269
#> ---end{extractAIC}--------------
#>
#> family:
#> -----
#>
#> Family: nbinom2
#> Link function: log
#>
#> ---end{family}--------------
#>
#> fitted:
#> -----
#> [1] 3.714 3.519 3.335 3.160 3.700 3.506 3.323 3.149 2.521 2.387
#> [11] 2.260 2.140 3.294 3.120 2.956 2.800 15.013 14.227 13.483 12.778
#> [21] 6.394 6.063 5.749 5.452 3.431 3.253 3.084 2.924 23.159 21.925
#> [31] 20.756 19.650 6.324 5.997 5.686 5.392 6.970 6.590 6.230 5.889
#> [41] 17.310 16.399 15.535 14.717 8.132 7.707 7.305 6.924 4.465 4.233
#> [51] 4.012 3.803 11.691 11.082 10.505 9.958 16.622 15.764 14.950 14.178
#> [61] 5.895 5.600 5.320 5.054 2.728 2.590 2.460 2.336 32.287 30.585
#> [71] 28.972 27.445 4.495 4.263 4.043 3.834 4.567 4.329 4.104 3.891
#> [81] 3.779 3.582 3.394 3.217 3.306 3.130 2.964 2.807 4.049 3.841
#> [91] 3.643 3.456 7.926 7.510 7.116 6.743 33.920 32.063 30.307 28.648
#> [101] 2.259 2.144 2.034 1.930 2.670 2.531 2.398 2.273 13.640 12.918
#> [111] 12.235 11.588 12.209 11.581 10.986 10.421 7.911 7.501 7.112 6.744
#> [121] 2.356 2.235 2.120 2.011 2.756 2.608 2.468 2.336 4.422 4.186
#> [131] 3.962 3.751 3.458 3.280 3.111 2.951 16.712 15.795 14.929 14.110
#> [141] 4.519 4.276 4.047 3.830 2.344 2.220 2.102 1.991 8.014 7.609
#> [151] 7.225 6.860 7.955 7.541 7.148 6.775 1.086 1.030 0.976 0.925
#> [161] 2.434 2.310 2.193 2.081 3.055 2.894 2.742 2.598 16.475 15.597
#> [171] 14.766 13.980 7.022 6.655 6.307 5.978 10.243 9.705 9.196 8.714
#> [181] 1.429 1.353 1.281 1.213 8.470 8.025 7.604 7.204 1.286 1.220
#> [191] 1.158 1.098 74.106 70.105 66.320 62.740 3.872 3.672 3.482 3.302
#> [201] 7.460 7.074 6.708 6.361 3.959 3.760 3.571 3.391 17.542 16.606
#> [211] 15.720 14.881 4.139 3.927 3.726 3.535 3.762 3.564 3.376 3.197
#> [221] 11.118 10.506 9.927 9.380 2.544 2.415 2.293 2.177 1.155 1.098
#> [231] 1.043 0.991 2.589 2.453 2.325 2.203
#> ---end{fitted}--------------
#>
#> fixef:
#> -----
#>
#> Conditional model:
#> (Intercept) Base trtprogabide Age
#> -1.322 0.884 -0.928 0.473
#> Visit Base:trtprogabide
#> -0.268 0.336
#> ---end{fixef}--------------
#>
#> formula:
#> -----
#> y ~ Base * trt + Age + Visit + (Visit | subject)
#> <environment: 0x7fa874e3ac80>
#> ---end{formula}--------------
#>
#> logLik:
#> -----
#> 'log Lik.' -625 (df=10)
#> ---end{logLik}--------------
#>
#> model.frame:
#> -----
#> y Base trt Age Visit subject
#> 1 5 1.012 placebo 3.43 -0.3 1
#> 2 3 1.012 placebo 3.43 -0.1 1
#> 3 3 1.012 placebo 3.43 0.1 1
#> 4 3 1.012 placebo 3.43 0.3 1
#> 5 3 1.012 placebo 3.40 -0.3 2
#> 6 5 1.012 placebo 3.40 -0.1 2
#> 7 3 1.012 placebo 3.40 0.1 2
#> 8 3 1.012 placebo 3.40 0.3 2
#> 9 2 0.405 placebo 3.22 -0.3 3
#> 10 4 0.405 placebo 3.22 -0.1 3
#> 11 0 0.405 placebo 3.22 0.1 3
#> 12 5 0.405 placebo 3.22 0.3 3
#> 13 4 0.693 placebo 3.58 -0.3 4
#> 14 4 0.693 placebo 3.58 -0.1 4
#> 15 1 0.693 placebo 3.58 0.1 4
#> 16 4 0.693 placebo 3.58 0.3 4
#> 17 7 2.803 placebo 3.09 -0.3 5
#> 18 18 2.803 placebo 3.09 -0.1 5
#> 19 9 2.803 placebo 3.09 0.1 5
#> 20 21 2.803 placebo 3.09 0.3 5
#> 21 5 1.910 placebo 3.37 -0.3 6
#> 22 2 1.910 placebo 3.37 -0.1 6
#> 23 8 1.910 placebo 3.37 0.1 6
#> 24 7 1.910 placebo 3.37 0.3 6
#> 25 6 1.099 placebo 3.43 -0.3 7
#> 26 4 1.099 placebo 3.43 -0.1 7
#> 27 0 1.099 placebo 3.43 0.1 7
#> 28 2 1.099 placebo 3.43 0.3 7
#> 29 40 2.565 placebo 3.74 -0.3 8
#> 30 20 2.565 placebo 3.74 -0.1 8
#> 31 21 2.565 placebo 3.74 0.1 8
#> 32 12 2.565 placebo 3.74 0.3 8
#> 33 5 1.749 placebo 3.61 -0.3 9
#> 34 6 1.749 placebo 3.61 -0.1 9
#> 35 6 1.749 placebo 3.61 0.1 9
#> 36 5 1.749 placebo 3.61 0.3 9
#> 37 14 0.916 placebo 3.33 -0.3 10
#> 38 13 0.916 placebo 3.33 -0.1 10
#> 39 6 0.916 placebo 3.33 0.1 10
#> 40 0 0.916 placebo 3.33 0.3 10
#> 41 26 2.565 placebo 3.58 -0.3 11
#> 42 12 2.565 placebo 3.58 -0.1 11
#> 43 6 2.565 placebo 3.58 0.1 11
#> 44 22 2.565 placebo 3.58 0.3 11
#> 45 12 2.110 placebo 3.18 -0.3 12
#> 46 6 2.110 placebo 3.18 -0.1 12
#> 47 8 2.110 placebo 3.18 0.1 12
#> 48 4 2.110 placebo 3.18 0.3 12
#> 49 4 1.504 placebo 3.14 -0.3 13
#> 50 4 1.504 placebo 3.14 -0.1 13
#> 51 6 1.504 placebo 3.14 0.1 13
#> 52 2 1.504 placebo 3.14 0.3 13
#> 53 7 2.351 placebo 3.58 -0.3 14
#> 54 9 2.351 placebo 3.58 -0.1 14
#> 55 12 2.351 placebo 3.58 0.1 14
#> 56 14 2.351 placebo 3.58 0.3 14
#> 57 16 3.080 placebo 3.26 -0.3 15
#> 58 24 3.080 placebo 3.26 -0.1 15
#> 59 10 3.080 placebo 3.26 0.1 15
#> 60 9 3.080 placebo 3.26 0.3 15
#> 61 11 2.526 placebo 3.26 -0.3 16
#> 62 0 2.526 placebo 3.26 -0.1 16
#> 63 0 2.526 placebo 3.26 0.1 16
#> 64 5 2.526 placebo 3.26 0.3 16
#> 65 0 1.504 placebo 3.33 -0.3 17
#> 66 0 1.504 placebo 3.33 -0.1 17
#> 67 3 1.504 placebo 3.33 0.1 17
#> 68 3 1.504 placebo 3.33 0.3 17
#> 69 37 3.323 placebo 3.43 -0.3 18
#> 70 29 3.323 placebo 3.43 -0.1 18
#> 71 28 3.323 placebo 3.43 0.1 18
#> 72 29 3.323 placebo 3.43 0.3 18
#> 73 3 1.504 placebo 3.47 -0.3 19
#> 74 5 1.504 placebo 3.47 -0.1 19
#> 75 2 1.504 placebo 3.47 0.1 19
#> 76 5 1.504 placebo 3.47 0.3 19
#> 77 3 1.609 placebo 3.04 -0.3 20
#> 78 0 1.609 placebo 3.04 -0.1 20
#> 79 6 1.609 placebo 3.04 0.1 20
#> 80 7 1.609 placebo 3.04 0.3 20
#> 81 3 1.099 placebo 3.37 -0.3 21
#> 82 4 1.099 placebo 3.37 -0.1 21
#> 83 3 1.099 placebo 3.37 0.1 21
#> 84 4 1.099 placebo 3.37 0.3 21
#> 85 3 0.811 placebo 3.04 -0.3 22
#> 86 4 0.811 placebo 3.04 -0.1 22
#> 87 3 0.811 placebo 3.04 0.1 22
#> 88 4 0.811 placebo 3.04 0.3 22
#> 89 2 1.447 placebo 3.47 -0.3 23
#> 90 3 1.447 placebo 3.47 -0.1 23
#> 91 3 1.447 placebo 3.47 0.1 23
#> 92 5 1.447 placebo 3.47 0.3 23
#> 93 8 1.946 placebo 3.22 -0.3 24
#> 94 12 1.946 placebo 3.22 -0.1 24
#> 95 2 1.946 placebo 3.22 0.1 24
#> 96 8 1.946 placebo 3.22 0.3 24
#> 97 18 2.621 placebo 3.40 -0.3 25
#> 98 24 2.621 placebo 3.40 -0.1 25
#> 99 76 2.621 placebo 3.40 0.1 25
#> 100 25 2.621 placebo 3.40 0.3 25
#> 101 2 0.811 placebo 3.69 -0.3 26
#> 102 1 0.811 placebo 3.69 -0.1 26
#> 103 2 0.811 placebo 3.69 0.1 26
#> 104 1 0.811 placebo 3.69 0.3 26
#> 105 3 0.916 placebo 2.94 -0.3 27
#> 106 1 0.916 placebo 2.94 -0.1 27
#> 107 4 0.916 placebo 2.94 0.1 27
#> 108 2 0.916 placebo 2.94 0.3 27
#> 109 13 2.464 placebo 3.09 -0.3 28
#> 110 15 2.464 placebo 3.09 -0.1 28
#> 111 13 2.464 placebo 3.09 0.1 28
#> 112 12 2.464 placebo 3.09 0.3 28
#> 113 11 2.944 progabide 2.89 -0.3 29
#> 114 14 2.944 progabide 2.89 -0.1 29
#> 115 9 2.944 progabide 2.89 0.1 29
#> 116 8 2.944 progabide 2.89 0.3 29
#> 117 8 2.251 progabide 3.47 -0.3 30
#> 118 7 2.251 progabide 3.47 -0.1 30
#> 119 9 2.251 progabide 3.47 0.1 30
#> 120 4 2.251 progabide 3.47 0.3 30
#> 121 0 1.558 progabide 3.00 -0.3 31
#> 122 4 1.558 progabide 3.00 -0.1 31
#> 123 3 1.558 progabide 3.00 0.1 31
#> 124 0 1.558 progabide 3.00 0.3 31
#> 125 3 0.916 progabide 3.40 -0.3 32
#> 126 6 0.916 progabide 3.40 -0.1 32
#> 127 1 0.916 progabide 3.40 0.1 32
#> 128 3 0.916 progabide 3.40 0.3 32
#> 129 2 1.558 progabide 2.89 -0.3 33
#> 130 6 1.558 progabide 2.89 -0.1 33
#> 131 7 1.558 progabide 2.89 0.1 33
#> 132 4 1.558 progabide 2.89 0.3 33
#> 133 4 1.792 progabide 3.18 -0.3 34
#> 134 3 1.792 progabide 3.18 -0.1 34
#> 135 1 1.792 progabide 3.18 0.1 34
#> 136 3 1.792 progabide 3.18 0.3 34
#> 137 22 2.048 progabide 3.40 -0.3 35
#> 138 17 2.048 progabide 3.40 -0.1 35
#> 139 19 2.048 progabide 3.40 0.1 35
#> 140 16 2.048 progabide 3.40 0.3 35
#> 141 5 1.253 progabide 3.56 -0.3 36
#> 142 4 1.253 progabide 3.56 -0.1 36
#> 143 7 1.253 progabide 3.56 0.1 36
#> 144 4 1.253 progabide 3.56 0.3 36
#> 145 2 1.012 progabide 3.30 -0.3 37
#> 146 4 1.012 progabide 3.30 -0.1 37
#> 147 0 1.012 progabide 3.30 0.1 37
#> 148 4 1.012 progabide 3.30 0.3 37
#> 149 3 2.818 progabide 3.00 -0.3 38
#> 150 7 2.818 progabide 3.00 -0.1 38
#> 151 7 2.818 progabide 3.00 0.1 38
#> 152 7 2.818 progabide 3.00 0.3 38
#> 153 4 2.327 progabide 3.09 -0.3 39
#> 154 18 2.327 progabide 3.09 -0.1 39
#> 155 2 2.327 progabide 3.09 0.1 39
#> 156 5 2.327 progabide 3.09 0.3 39
#> 157 2 0.560 progabide 3.33 -0.3 40
#> 158 1 0.560 progabide 3.33 -0.1 40
#> 159 1 0.560 progabide 3.33 0.1 40
#> 160 0 0.560 progabide 3.33 0.3 40
#> 161 0 1.705 progabide 3.14 -0.3 41
#> 162 2 1.705 progabide 3.14 -0.1 41
#> 163 4 1.705 progabide 3.14 0.1 41
#> 164 0 1.705 progabide 3.14 0.3 41
#> 165 5 1.179 progabide 3.69 -0.3 42
#> 166 4 1.179 progabide 3.69 -0.1 42
#> 167 0 1.179 progabide 3.69 0.1 42
#> 168 3 1.179 progabide 3.69 0.3 42
#> 169 11 2.442 progabide 3.50 -0.3 43
#> 170 14 2.442 progabide 3.50 -0.1 43
#> 171 25 2.442 progabide 3.50 0.1 43
#> 172 15 2.442 progabide 3.50 0.3 43
#> 173 10 2.197 progabide 3.04 -0.3 44
#> 174 5 2.197 progabide 3.04 -0.1 44
#> 175 3 2.197 progabide 3.04 0.1 44
#> 176 8 2.197 progabide 3.04 0.3 44
#> 177 19 2.251 progabide 3.56 -0.3 45
#> 178 7 2.251 progabide 3.56 -0.1 45
#> 179 6 2.251 progabide 3.56 0.1 45
#> 180 7 2.251 progabide 3.56 0.3 45
#> 181 1 0.560 progabide 3.22 -0.3 46
#> 182 1 0.560 progabide 3.22 -0.1 46
#> 183 2 0.560 progabide 3.22 0.1 46
#> 184 3 0.560 progabide 3.22 0.3 46
#> 185 6 2.197 progabide 3.26 -0.3 47
#> 186 10 2.197 progabide 3.26 -0.1 47
#> 187 8 2.197 progabide 3.26 0.1 47
#> 188 8 2.197 progabide 3.26 0.3 47
#> 189 2 1.012 progabide 3.22 -0.3 48
#> 190 1 1.012 progabide 3.22 -0.1 48
#> 191 0 1.012 progabide 3.22 0.1 48
#> 192 0 1.012 progabide 3.22 0.3 48
#> 193 102 3.631 progabide 3.09 -0.3 49
#> 194 65 3.631 progabide 3.09 -0.1 49
#> 195 72 3.631 progabide 3.09 0.1 49
#> 196 63 3.631 progabide 3.09 0.3 49
#> 197 4 1.705 progabide 3.47 -0.3 50
#> 198 3 1.705 progabide 3.47 -0.1 50
#> 199 2 1.705 progabide 3.47 0.1 50
#> 200 4 1.705 progabide 3.47 0.3 50
#> 201 8 2.327 progabide 3.22 -0.3 51
#> 202 6 2.327 progabide 3.22 -0.1 51
#> 203 5 2.327 progabide 3.22 0.1 51
#> 204 7 2.327 progabide 3.22 0.3 51
#> 205 1 2.079 progabide 3.56 -0.3 52
#> 206 3 2.079 progabide 3.56 -0.1 52
#> 207 1 2.079 progabide 3.56 0.1 52
#> 208 5 2.079 progabide 3.56 0.3 52
#> 209 18 2.639 progabide 3.04 -0.3 53
#> 210 11 2.639 progabide 3.04 -0.1 53
#> 211 28 2.639 progabide 3.04 0.1 53
#> 212 13 2.639 progabide 3.04 0.3 53
#> 213 6 1.792 progabide 3.71 -0.3 54
#> 214 3 1.792 progabide 3.71 -0.1 54
#> 215 4 1.792 progabide 3.71 0.1 54
#> 216 0 1.792 progabide 3.71 0.3 54
#> 217 3 1.386 progabide 3.47 -0.3 55
#> 218 5 1.386 progabide 3.47 -0.1 55
#> 219 4 1.386 progabide 3.47 0.1 55
#> 220 3 1.386 progabide 3.47 0.3 55
#> 221 1 1.705 progabide 3.26 -0.3 56
#> 222 23 1.705 progabide 3.26 -0.1 56
#> 223 19 1.705 progabide 3.26 0.1 56
#> 224 8 1.705 progabide 3.26 0.3 56
#> 225 2 1.833 progabide 3.04 -0.3 57
#> 226 3 1.833 progabide 3.04 -0.1 57
#> 227 0 1.833 progabide 3.04 0.1 57
#> 228 1 1.833 progabide 3.04 0.3 57
#> 229 0 1.179 progabide 3.58 -0.3 58
#> 230 0 1.179 progabide 3.58 -0.1 58
#> 231 0 1.179 progabide 3.58 0.1 58
#> 232 0 1.179 progabide 3.58 0.3 58
#> 233 1 1.099 progabide 3.61 -0.3 59
#> 234 4 1.099 progabide 3.61 -0.1 59
#> 235 3 1.099 progabide 3.61 0.1 59
#> 236 2 1.099 progabide 3.61 0.3 59
#> ---end{model.frame}--------------
#>
#> model.matrix:
#> -----
#> (Intercept) Base trtprogabide Age Visit Base:trtprogabide
#> 1 1 1.012 0 3.43 -0.3 0.000
#> 2 1 1.012 0 3.43 -0.1 0.000
#> 3 1 1.012 0 3.43 0.1 0.000
#> 4 1 1.012 0 3.43 0.3 0.000
#> 5 1 1.012 0 3.40 -0.3 0.000
#> 6 1 1.012 0 3.40 -0.1 0.000
#> 7 1 1.012 0 3.40 0.1 0.000
#> 8 1 1.012 0 3.40 0.3 0.000
#> 9 1 0.405 0 3.22 -0.3 0.000
#> 10 1 0.405 0 3.22 -0.1 0.000
#> 11 1 0.405 0 3.22 0.1 0.000
#> 12 1 0.405 0 3.22 0.3 0.000
#> 13 1 0.693 0 3.58 -0.3 0.000
#> 14 1 0.693 0 3.58 -0.1 0.000
#> 15 1 0.693 0 3.58 0.1 0.000
#> 16 1 0.693 0 3.58 0.3 0.000
#> 17 1 2.803 0 3.09 -0.3 0.000
#> 18 1 2.803 0 3.09 -0.1 0.000
#> 19 1 2.803 0 3.09 0.1 0.000
#> 20 1 2.803 0 3.09 0.3 0.000
#> 21 1 1.910 0 3.37 -0.3 0.000
#> 22 1 1.910 0 3.37 -0.1 0.000
#> 23 1 1.910 0 3.37 0.1 0.000
#> 24 1 1.910 0 3.37 0.3 0.000
#> 25 1 1.099 0 3.43 -0.3 0.000
#> 26 1 1.099 0 3.43 -0.1 0.000
#> 27 1 1.099 0 3.43 0.1 0.000
#> 28 1 1.099 0 3.43 0.3 0.000
#> 29 1 2.565 0 3.74 -0.3 0.000
#> 30 1 2.565 0 3.74 -0.1 0.000
#> 31 1 2.565 0 3.74 0.1 0.000
#> 32 1 2.565 0 3.74 0.3 0.000
#> 33 1 1.749 0 3.61 -0.3 0.000
#> 34 1 1.749 0 3.61 -0.1 0.000
#> 35 1 1.749 0 3.61 0.1 0.000
#> 36 1 1.749 0 3.61 0.3 0.000
#> 37 1 0.916 0 3.33 -0.3 0.000
#> 38 1 0.916 0 3.33 -0.1 0.000
#> 39 1 0.916 0 3.33 0.1 0.000
#> 40 1 0.916 0 3.33 0.3 0.000
#> 41 1 2.565 0 3.58 -0.3 0.000
#> 42 1 2.565 0 3.58 -0.1 0.000
#> 43 1 2.565 0 3.58 0.1 0.000
#> 44 1 2.565 0 3.58 0.3 0.000
#> 45 1 2.110 0 3.18 -0.3 0.000
#> 46 1 2.110 0 3.18 -0.1 0.000
#> 47 1 2.110 0 3.18 0.1 0.000
#> 48 1 2.110 0 3.18 0.3 0.000
#> 49 1 1.504 0 3.14 -0.3 0.000
#> 50 1 1.504 0 3.14 -0.1 0.000
#> 51 1 1.504 0 3.14 0.1 0.000
#> 52 1 1.504 0 3.14 0.3 0.000
#> 53 1 2.351 0 3.58 -0.3 0.000
#> 54 1 2.351 0 3.58 -0.1 0.000
#> 55 1 2.351 0 3.58 0.1 0.000
#> 56 1 2.351 0 3.58 0.3 0.000
#> 57 1 3.080 0 3.26 -0.3 0.000
#> 58 1 3.080 0 3.26 -0.1 0.000
#> 59 1 3.080 0 3.26 0.1 0.000
#> 60 1 3.080 0 3.26 0.3 0.000
#> 61 1 2.526 0 3.26 -0.3 0.000
#> 62 1 2.526 0 3.26 -0.1 0.000
#> 63 1 2.526 0 3.26 0.1 0.000
#> 64 1 2.526 0 3.26 0.3 0.000
#> 65 1 1.504 0 3.33 -0.3 0.000
#> 66 1 1.504 0 3.33 -0.1 0.000
#> 67 1 1.504 0 3.33 0.1 0.000
#> 68 1 1.504 0 3.33 0.3 0.000
#> 69 1 3.323 0 3.43 -0.3 0.000
#> 70 1 3.323 0 3.43 -0.1 0.000
#> 71 1 3.323 0 3.43 0.1 0.000
#> 72 1 3.323 0 3.43 0.3 0.000
#> 73 1 1.504 0 3.47 -0.3 0.000
#> 74 1 1.504 0 3.47 -0.1 0.000
#> 75 1 1.504 0 3.47 0.1 0.000
#> 76 1 1.504 0 3.47 0.3 0.000
#> 77 1 1.609 0 3.04 -0.3 0.000
#> 78 1 1.609 0 3.04 -0.1 0.000
#> 79 1 1.609 0 3.04 0.1 0.000
#> 80 1 1.609 0 3.04 0.3 0.000
#> 81 1 1.099 0 3.37 -0.3 0.000
#> 82 1 1.099 0 3.37 -0.1 0.000
#> 83 1 1.099 0 3.37 0.1 0.000
#> 84 1 1.099 0 3.37 0.3 0.000
#> 85 1 0.811 0 3.04 -0.3 0.000
#> 86 1 0.811 0 3.04 -0.1 0.000
#> 87 1 0.811 0 3.04 0.1 0.000
#> 88 1 0.811 0 3.04 0.3 0.000
#> 89 1 1.447 0 3.47 -0.3 0.000
#> 90 1 1.447 0 3.47 -0.1 0.000
#> 91 1 1.447 0 3.47 0.1 0.000
#> 92 1 1.447 0 3.47 0.3 0.000
#> 93 1 1.946 0 3.22 -0.3 0.000
#> 94 1 1.946 0 3.22 -0.1 0.000
#> 95 1 1.946 0 3.22 0.1 0.000
#> 96 1 1.946 0 3.22 0.3 0.000
#> 97 1 2.621 0 3.40 -0.3 0.000
#> 98 1 2.621 0 3.40 -0.1 0.000
#> 99 1 2.621 0 3.40 0.1 0.000
#> 100 1 2.621 0 3.40 0.3 0.000
#> 101 1 0.811 0 3.69 -0.3 0.000
#> 102 1 0.811 0 3.69 -0.1 0.000
#> 103 1 0.811 0 3.69 0.1 0.000
#> 104 1 0.811 0 3.69 0.3 0.000
#> 105 1 0.916 0 2.94 -0.3 0.000
#> 106 1 0.916 0 2.94 -0.1 0.000
#> 107 1 0.916 0 2.94 0.1 0.000
#> 108 1 0.916 0 2.94 0.3 0.000
#> 109 1 2.464 0 3.09 -0.3 0.000
#> 110 1 2.464 0 3.09 -0.1 0.000
#> 111 1 2.464 0 3.09 0.1 0.000
#> 112 1 2.464 0 3.09 0.3 0.000
#> 113 1 2.944 1 2.89 -0.3 2.944
#> 114 1 2.944 1 2.89 -0.1 2.944
#> 115 1 2.944 1 2.89 0.1 2.944
#> 116 1 2.944 1 2.89 0.3 2.944
#> 117 1 2.251 1 3.47 -0.3 2.251
#> 118 1 2.251 1 3.47 -0.1 2.251
#> 119 1 2.251 1 3.47 0.1 2.251
#> 120 1 2.251 1 3.47 0.3 2.251
#> 121 1 1.558 1 3.00 -0.3 1.558
#> 122 1 1.558 1 3.00 -0.1 1.558
#> 123 1 1.558 1 3.00 0.1 1.558
#> 124 1 1.558 1 3.00 0.3 1.558
#> 125 1 0.916 1 3.40 -0.3 0.916
#> 126 1 0.916 1 3.40 -0.1 0.916
#> 127 1 0.916 1 3.40 0.1 0.916
#> 128 1 0.916 1 3.40 0.3 0.916
#> 129 1 1.558 1 2.89 -0.3 1.558
#> 130 1 1.558 1 2.89 -0.1 1.558
#> 131 1 1.558 1 2.89 0.1 1.558
#> 132 1 1.558 1 2.89 0.3 1.558
#> 133 1 1.792 1 3.18 -0.3 1.792
#> 134 1 1.792 1 3.18 -0.1 1.792
#> 135 1 1.792 1 3.18 0.1 1.792
#> 136 1 1.792 1 3.18 0.3 1.792
#> 137 1 2.048 1 3.40 -0.3 2.048
#> 138 1 2.048 1 3.40 -0.1 2.048
#> 139 1 2.048 1 3.40 0.1 2.048
#> 140 1 2.048 1 3.40 0.3 2.048
#> 141 1 1.253 1 3.56 -0.3 1.253
#> 142 1 1.253 1 3.56 -0.1 1.253
#> 143 1 1.253 1 3.56 0.1 1.253
#> 144 1 1.253 1 3.56 0.3 1.253
#> 145 1 1.012 1 3.30 -0.3 1.012
#> 146 1 1.012 1 3.30 -0.1 1.012
#> 147 1 1.012 1 3.30 0.1 1.012
#> 148 1 1.012 1 3.30 0.3 1.012
#> 149 1 2.818 1 3.00 -0.3 2.818
#> 150 1 2.818 1 3.00 -0.1 2.818
#> 151 1 2.818 1 3.00 0.1 2.818
#> 152 1 2.818 1 3.00 0.3 2.818
#> 153 1 2.327 1 3.09 -0.3 2.327
#> 154 1 2.327 1 3.09 -0.1 2.327
#> 155 1 2.327 1 3.09 0.1 2.327
#> 156 1 2.327 1 3.09 0.3 2.327
#> 157 1 0.560 1 3.33 -0.3 0.560
#> 158 1 0.560 1 3.33 -0.1 0.560
#> 159 1 0.560 1 3.33 0.1 0.560
#> 160 1 0.560 1 3.33 0.3 0.560
#> 161 1 1.705 1 3.14 -0.3 1.705
#> 162 1 1.705 1 3.14 -0.1 1.705
#> 163 1 1.705 1 3.14 0.1 1.705
#> 164 1 1.705 1 3.14 0.3 1.705
#> 165 1 1.179 1 3.69 -0.3 1.179
#> 166 1 1.179 1 3.69 -0.1 1.179
#> 167 1 1.179 1 3.69 0.1 1.179
#> 168 1 1.179 1 3.69 0.3 1.179
#> 169 1 2.442 1 3.50 -0.3 2.442
#> 170 1 2.442 1 3.50 -0.1 2.442
#> 171 1 2.442 1 3.50 0.1 2.442
#> 172 1 2.442 1 3.50 0.3 2.442
#> 173 1 2.197 1 3.04 -0.3 2.197
#> 174 1 2.197 1 3.04 -0.1 2.197
#> 175 1 2.197 1 3.04 0.1 2.197
#> 176 1 2.197 1 3.04 0.3 2.197
#> 177 1 2.251 1 3.56 -0.3 2.251
#> 178 1 2.251 1 3.56 -0.1 2.251
#> 179 1 2.251 1 3.56 0.1 2.251
#> 180 1 2.251 1 3.56 0.3 2.251
#> 181 1 0.560 1 3.22 -0.3 0.560
#> 182 1 0.560 1 3.22 -0.1 0.560
#> 183 1 0.560 1 3.22 0.1 0.560
#> 184 1 0.560 1 3.22 0.3 0.560
#> 185 1 2.197 1 3.26 -0.3 2.197
#> 186 1 2.197 1 3.26 -0.1 2.197
#> 187 1 2.197 1 3.26 0.1 2.197
#> 188 1 2.197 1 3.26 0.3 2.197
#> 189 1 1.012 1 3.22 -0.3 1.012
#> 190 1 1.012 1 3.22 -0.1 1.012
#> 191 1 1.012 1 3.22 0.1 1.012
#> 192 1 1.012 1 3.22 0.3 1.012
#> 193 1 3.631 1 3.09 -0.3 3.631
#> 194 1 3.631 1 3.09 -0.1 3.631
#> 195 1 3.631 1 3.09 0.1 3.631
#> 196 1 3.631 1 3.09 0.3 3.631
#> 197 1 1.705 1 3.47 -0.3 1.705
#> 198 1 1.705 1 3.47 -0.1 1.705
#> 199 1 1.705 1 3.47 0.1 1.705
#> 200 1 1.705 1 3.47 0.3 1.705
#> 201 1 2.327 1 3.22 -0.3 2.327
#> 202 1 2.327 1 3.22 -0.1 2.327
#> 203 1 2.327 1 3.22 0.1 2.327
#> 204 1 2.327 1 3.22 0.3 2.327
#> 205 1 2.079 1 3.56 -0.3 2.079
#> 206 1 2.079 1 3.56 -0.1 2.079
#> 207 1 2.079 1 3.56 0.1 2.079
#> 208 1 2.079 1 3.56 0.3 2.079
#> 209 1 2.639 1 3.04 -0.3 2.639
#> 210 1 2.639 1 3.04 -0.1 2.639
#> 211 1 2.639 1 3.04 0.1 2.639
#> 212 1 2.639 1 3.04 0.3 2.639
#> 213 1 1.792 1 3.71 -0.3 1.792
#> 214 1 1.792 1 3.71 -0.1 1.792
#> 215 1 1.792 1 3.71 0.1 1.792
#> 216 1 1.792 1 3.71 0.3 1.792
#> 217 1 1.386 1 3.47 -0.3 1.386
#> 218 1 1.386 1 3.47 -0.1 1.386
#> 219 1 1.386 1 3.47 0.1 1.386
#> 220 1 1.386 1 3.47 0.3 1.386
#> 221 1 1.705 1 3.26 -0.3 1.705
#> 222 1 1.705 1 3.26 -0.1 1.705
#> 223 1 1.705 1 3.26 0.1 1.705
#> 224 1 1.705 1 3.26 0.3 1.705
#> 225 1 1.833 1 3.04 -0.3 1.833
#> 226 1 1.833 1 3.04 -0.1 1.833
#> 227 1 1.833 1 3.04 0.1 1.833
#> 228 1 1.833 1 3.04 0.3 1.833
#> 229 1 1.179 1 3.58 -0.3 1.179
#> 230 1 1.179 1 3.58 -0.1 1.179
#> 231 1 1.179 1 3.58 0.1 1.179
#> 232 1 1.179 1 3.58 0.3 1.179
#> 233 1 1.099 1 3.61 -0.3 1.099
#> 234 1 1.099 1 3.61 -0.1 1.099
#> 235 1 1.099 1 3.61 0.1 1.099
#> 236 1 1.099 1 3.61 0.3 1.099
#> attr(,"assign")
#> [1] 0 1 2 3 4 5
#> attr(,"contrasts")
#> attr(,"contrasts")$trt
#> [1] "contr.treatment"
#>
#> ---end{model.matrix}--------------
#>
#> nobs:
#> -----
#> [1] 236
#> ---end{nobs}--------------
#>
#> predict:
#> -----
#> [1] 1.31207 1.25827 1.20447 1.15067 1.30843 1.25460 1.20076 1.14693
#> [9] 0.92459 0.87001 0.81543 0.76086 1.19203 1.13795 1.08387 1.02979
#> [17] 2.70889 2.65518 2.60146 2.54774 1.85528 1.80217 1.74905 1.69594
#> [25] 1.23282 1.17951 1.12620 1.07289 3.14240 3.08762 3.03285 2.97808
#> [33] 1.84439 1.79123 1.73807 1.68490 1.94168 1.88551 1.82934 1.77317
#> [41] 2.85130 2.79721 2.74312 2.68902 2.09575 2.04216 1.98857 1.93498
#> [49] 1.49626 1.44280 1.38935 1.33589 2.45879 2.40533 2.35187 2.29841
#> [57] 2.81074 2.75772 2.70469 2.65166 1.77418 1.72286 1.67153 1.62021
#> [65] 1.00346 0.95183 0.90020 0.84858 3.47468 3.42051 3.36635 3.31218
#> [73] 1.50295 1.44996 1.39697 1.34399 1.51880 1.46543 1.41206 1.35870
#> [81] 1.32950 1.27579 1.22208 1.16837 1.19565 1.14109 1.08653 1.03197
#> [89] 1.39848 1.34566 1.29284 1.24002 2.07015 2.01625 1.96235 1.90845
#> [97] 3.52400 3.46770 3.41139 3.35509 0.81490 0.76248 0.71005 0.65763
#> [105] 0.98224 0.92848 0.87473 0.82098 2.61300 2.55865 2.50429 2.44994
#> [113] 2.50217 2.44939 2.39660 2.34381 2.06826 2.01504 1.96181 1.90859
#> [121] 0.85706 0.80429 0.75151 0.69873 1.01366 0.95855 0.90344 0.84833
#> [129] 1.48659 1.43169 1.37680 1.32190 1.24066 1.18784 1.13502 1.08221
#> [137] 2.81614 2.75972 2.70330 2.64687 1.50820 1.45305 1.39791 1.34276
#> [145] 0.85183 0.79743 0.74303 0.68863 2.08113 2.02933 1.97753 1.92573
#> [153] 2.07380 2.02030 1.96679 1.91329 0.08291 0.02924 -0.02443 -0.07810
#> [161] 0.88944 0.83732 0.78520 0.73309 1.11668 1.06267 1.00866 0.95465
#> [169] 2.80182 2.74708 2.69234 2.63760 1.94909 1.89541 1.84174 1.78806
#> [177] 2.32659 2.27269 2.21879 2.16490 0.35727 0.30258 0.24789 0.19320
#> [185] 2.13655 2.08260 2.02865 1.97470 0.25178 0.19914 0.14650 0.09385
#> [193] 4.30550 4.25000 4.19450 4.13899 1.35376 1.30068 1.24761 1.19453
#> [201] 2.00955 1.95643 1.90332 1.85020 1.37589 1.32430 1.27271 1.22113
#> [209] 2.86462 2.80977 2.75492 2.70006 1.42055 1.36796 1.31537 1.26278
#> [217] 1.32496 1.27077 1.21657 1.16237 2.40861 2.35194 2.29527 2.23860
#> [225] 0.93354 0.88164 0.82974 0.77784 0.14448 0.09334 0.04220 -0.00893
#> [233] 0.95138 0.89747 0.84355 0.78964
#> ---end{predict}--------------
#>
#> print:
#> -----
#> Formula: y ~ Base * trt + Age + Visit + (Visit | subject)
#> Data: epil2
#> AIC BIC logLik df.resid
#> 1269 1304 -625 226
#> Random-effects (co)variances:
#>
#> Conditional model:
#> Groups Name Std.Dev. Corr
#> subject (Intercept) 0.4660
#> Visit 0.0073 -1.00
#>
#> Number of obs: 236 / Conditional model: subject, 59
#>
#> Dispersion parameter for nbinom2 family (): 7.46
#>
#> Fixed Effects:
#>
#> Conditional model:
#> (Intercept) Base trtprogabide Age
#> -1.322 0.884 -0.928 0.473
#> Visit Base:trtprogabide
#> -0.268 0.336
#> Formula: y ~ Base * trt + Age + Visit + (Visit | subject)
#> Data: epil2
#> AIC BIC logLik df.resid
#> 1269 1304 -625 226
#> Random-effects (co)variances:
#>
#> Conditional model:
#> Groups Name Std.Dev. Corr
#> subject (Intercept) 0.4660
#> Visit 0.0073 -1.00
#>
#> Number of obs: 236 / Conditional model: subject, 59
#>
#> Dispersion parameter for nbinom2 family (): 7.46
#>
#> Fixed Effects:
#>
#> Conditional model:
#> (Intercept) Base trtprogabide Age
#> -1.322 0.884 -0.928 0.473
#> Visit Base:trtprogabide
#> -0.268 0.336
#> ---end{print}--------------
#>
#> ranef:
#> -----
#> $subject
#> (Intercept) Visit
#> 1 0.03606 -5.64e-04
#> 2 0.04787 -7.49e-04
#> 3 0.28508 -4.46e-03
#> 4 0.12652 -1.98e-03
#> 5 0.01070 -1.67e-04
#> 6 -0.18220 2.85e-03
#> 7 -0.11940 1.87e-03
#> 8 0.34778 -5.44e-03
#> 9 -0.16654 2.61e-03
#> 10 0.79451 -1.24e-02
#> 11 0.13058 -2.04e-03
#> 12 -0.03044 4.76e-04
#> 13 -0.07363 1.15e-03
#> 14 -0.07212 1.13e-03
#> 15 -0.20966 3.28e-03
#> 16 -0.75388 1.18e-02
#> 17 -0.65667 1.03e-02
#> 18 0.15399 -2.41e-03
#> 19 -0.22234 3.48e-03
#> 20 -0.10112 1.58e-03
#> 21 0.00820 -1.28e-04
#> 22 0.28005 -4.38e-03
#> 23 -0.27601 4.32e-03
#> 24 0.06948 -1.09e-03
#> 25 0.83654 -1.31e-02
#> 26 -0.40209 6.29e-03
#> 27 0.02199 -3.44e-04
#> 28 0.21407 -3.35e-03
#> 29 -0.28630 4.48e-03
#> 30 -0.14681 2.30e-03
#> 31 -0.28914 4.53e-03
#> 32 0.45572 -7.13e-03
#> 33 0.38701 -6.06e-03
#> 34 -0.27693 4.33e-03
#> 35 0.87528 -1.37e-02
#> 36 0.46665 -7.30e-03
#> 37 0.22843 -3.58e-03
#> 38 -0.60183 9.42e-03
#> 39 -0.05732 8.97e-04
#> 40 -0.00491 7.69e-05
#> 41 -0.50078 7.84e-03
#> 42 0.10417 -1.63e-03
#> 43 0.33673 -5.27e-03
#> 44 -0.00157 2.45e-05
#> 45 0.06814 -1.07e-03
#> 46 0.32149 -5.03e-03
#> 47 0.08453 -1.32e-03
#> 48 -0.33261 5.21e-03
#> 49 0.58012 -9.08e-03
#> 50 -0.19400 3.04e-03
#> 51 -0.18142 2.84e-03
#> 52 -0.66934 1.05e-02
#> 53 0.37292 -5.84e-03
#> 54 -0.34984 5.48e-03
#> 55 0.16421 -2.57e-03
#> 56 0.95361 -1.49e-02
#> 57 -0.56938 8.91e-03
#> 58 -0.81392 1.27e-02
#> 59 0.07356 -1.15e-03
#>
#> ---end{ranef}--------------
#>
#> recover_data:
#> -----
#> Base trt Age Visit
#> 1 1.012 placebo 3.43 -0.3
#> 2 1.012 placebo 3.43 -0.1
#> 3 1.012 placebo 3.43 0.1
#> 4 1.012 placebo 3.43 0.3
#> 5 1.012 placebo 3.40 -0.3
#> 6 1.012 placebo 3.40 -0.1
#> 7 1.012 placebo 3.40 0.1
#> 8 1.012 placebo 3.40 0.3
#> 9 0.405 placebo 3.22 -0.3
#> 10 0.405 placebo 3.22 -0.1
#> 11 0.405 placebo 3.22 0.1
#> 12 0.405 placebo 3.22 0.3
#> 13 0.693 placebo 3.58 -0.3
#> 14 0.693 placebo 3.58 -0.1
#> 15 0.693 placebo 3.58 0.1
#> 16 0.693 placebo 3.58 0.3
#> 17 2.803 placebo 3.09 -0.3
#> 18 2.803 placebo 3.09 -0.1
#> 19 2.803 placebo 3.09 0.1
#> 20 2.803 placebo 3.09 0.3
#> 21 1.910 placebo 3.37 -0.3
#> 22 1.910 placebo 3.37 -0.1
#> 23 1.910 placebo 3.37 0.1
#> 24 1.910 placebo 3.37 0.3
#> 25 1.099 placebo 3.43 -0.3
#> 26 1.099 placebo 3.43 -0.1
#> 27 1.099 placebo 3.43 0.1
#> 28 1.099 placebo 3.43 0.3
#> 29 2.565 placebo 3.74 -0.3
#> 30 2.565 placebo 3.74 -0.1
#> 31 2.565 placebo 3.74 0.1
#> 32 2.565 placebo 3.74 0.3
#> 33 1.749 placebo 3.61 -0.3
#> 34 1.749 placebo 3.61 -0.1
#> 35 1.749 placebo 3.61 0.1
#> 36 1.749 placebo 3.61 0.3
#> 37 0.916 placebo 3.33 -0.3
#> 38 0.916 placebo 3.33 -0.1
#> 39 0.916 placebo 3.33 0.1
#> 40 0.916 placebo 3.33 0.3
#> 41 2.565 placebo 3.58 -0.3
#> 42 2.565 placebo 3.58 -0.1
#> 43 2.565 placebo 3.58 0.1
#> 44 2.565 placebo 3.58 0.3
#> 45 2.110 placebo 3.18 -0.3
#> 46 2.110 placebo 3.18 -0.1
#> 47 2.110 placebo 3.18 0.1
#> 48 2.110 placebo 3.18 0.3
#> 49 1.504 placebo 3.14 -0.3
#> 50 1.504 placebo 3.14 -0.1
#> 51 1.504 placebo 3.14 0.1
#> 52 1.504 placebo 3.14 0.3
#> 53 2.351 placebo 3.58 -0.3
#> 54 2.351 placebo 3.58 -0.1
#> 55 2.351 placebo 3.58 0.1
#> 56 2.351 placebo 3.58 0.3
#> 57 3.080 placebo 3.26 -0.3
#> 58 3.080 placebo 3.26 -0.1
#> 59 3.080 placebo 3.26 0.1
#> 60 3.080 placebo 3.26 0.3
#> 61 2.526 placebo 3.26 -0.3
#> 62 2.526 placebo 3.26 -0.1
#> 63 2.526 placebo 3.26 0.1
#> 64 2.526 placebo 3.26 0.3
#> 65 1.504 placebo 3.33 -0.3
#> 66 1.504 placebo 3.33 -0.1
#> 67 1.504 placebo 3.33 0.1
#> 68 1.504 placebo 3.33 0.3
#> 69 3.323 placebo 3.43 -0.3
#> 70 3.323 placebo 3.43 -0.1
#> 71 3.323 placebo 3.43 0.1
#> 72 3.323 placebo 3.43 0.3
#> 73 1.504 placebo 3.47 -0.3
#> 74 1.504 placebo 3.47 -0.1
#> 75 1.504 placebo 3.47 0.1
#> 76 1.504 placebo 3.47 0.3
#> 77 1.609 placebo 3.04 -0.3
#> 78 1.609 placebo 3.04 -0.1
#> 79 1.609 placebo 3.04 0.1
#> 80 1.609 placebo 3.04 0.3
#> 81 1.099 placebo 3.37 -0.3
#> 82 1.099 placebo 3.37 -0.1
#> 83 1.099 placebo 3.37 0.1
#> 84 1.099 placebo 3.37 0.3
#> 85 0.811 placebo 3.04 -0.3
#> 86 0.811 placebo 3.04 -0.1
#> 87 0.811 placebo 3.04 0.1
#> 88 0.811 placebo 3.04 0.3
#> 89 1.447 placebo 3.47 -0.3
#> 90 1.447 placebo 3.47 -0.1
#> 91 1.447 placebo 3.47 0.1
#> 92 1.447 placebo 3.47 0.3
#> 93 1.946 placebo 3.22 -0.3
#> 94 1.946 placebo 3.22 -0.1
#> 95 1.946 placebo 3.22 0.1
#> 96 1.946 placebo 3.22 0.3
#> 97 2.621 placebo 3.40 -0.3
#> 98 2.621 placebo 3.40 -0.1
#> 99 2.621 placebo 3.40 0.1
#> 100 2.621 placebo 3.40 0.3
#> 101 0.811 placebo 3.69 -0.3
#> 102 0.811 placebo 3.69 -0.1
#> 103 0.811 placebo 3.69 0.1
#> 104 0.811 placebo 3.69 0.3
#> 105 0.916 placebo 2.94 -0.3
#> 106 0.916 placebo 2.94 -0.1
#> 107 0.916 placebo 2.94 0.1
#> 108 0.916 placebo 2.94 0.3
#> 109 2.464 placebo 3.09 -0.3
#> 110 2.464 placebo 3.09 -0.1
#> 111 2.464 placebo 3.09 0.1
#> 112 2.464 placebo 3.09 0.3
#> 113 2.944 progabide 2.89 -0.3
#> 114 2.944 progabide 2.89 -0.1
#> 115 2.944 progabide 2.89 0.1
#> 116 2.944 progabide 2.89 0.3
#> 117 2.251 progabide 3.47 -0.3
#> 118 2.251 progabide 3.47 -0.1
#> 119 2.251 progabide 3.47 0.1
#> 120 2.251 progabide 3.47 0.3
#> 121 1.558 progabide 3.00 -0.3
#> 122 1.558 progabide 3.00 -0.1
#> 123 1.558 progabide 3.00 0.1
#> 124 1.558 progabide 3.00 0.3
#> 125 0.916 progabide 3.40 -0.3
#> 126 0.916 progabide 3.40 -0.1
#> 127 0.916 progabide 3.40 0.1
#> 128 0.916 progabide 3.40 0.3
#> 129 1.558 progabide 2.89 -0.3
#> 130 1.558 progabide 2.89 -0.1
#> 131 1.558 progabide 2.89 0.1
#> 132 1.558 progabide 2.89 0.3
#> 133 1.792 progabide 3.18 -0.3
#> 134 1.792 progabide 3.18 -0.1
#> 135 1.792 progabide 3.18 0.1
#> 136 1.792 progabide 3.18 0.3
#> 137 2.048 progabide 3.40 -0.3
#> 138 2.048 progabide 3.40 -0.1
#> 139 2.048 progabide 3.40 0.1
#> 140 2.048 progabide 3.40 0.3
#> 141 1.253 progabide 3.56 -0.3
#> 142 1.253 progabide 3.56 -0.1
#> 143 1.253 progabide 3.56 0.1
#> 144 1.253 progabide 3.56 0.3
#> 145 1.012 progabide 3.30 -0.3
#> 146 1.012 progabide 3.30 -0.1
#> 147 1.012 progabide 3.30 0.1
#> 148 1.012 progabide 3.30 0.3
#> 149 2.818 progabide 3.00 -0.3
#> 150 2.818 progabide 3.00 -0.1
#> 151 2.818 progabide 3.00 0.1
#> 152 2.818 progabide 3.00 0.3
#> 153 2.327 progabide 3.09 -0.3
#> 154 2.327 progabide 3.09 -0.1
#> 155 2.327 progabide 3.09 0.1
#> 156 2.327 progabide 3.09 0.3
#> 157 0.560 progabide 3.33 -0.3
#> 158 0.560 progabide 3.33 -0.1
#> 159 0.560 progabide 3.33 0.1
#> 160 0.560 progabide 3.33 0.3
#> 161 1.705 progabide 3.14 -0.3
#> 162 1.705 progabide 3.14 -0.1
#> 163 1.705 progabide 3.14 0.1
#> 164 1.705 progabide 3.14 0.3
#> 165 1.179 progabide 3.69 -0.3
#> 166 1.179 progabide 3.69 -0.1
#> 167 1.179 progabide 3.69 0.1
#> 168 1.179 progabide 3.69 0.3
#> 169 2.442 progabide 3.50 -0.3
#> 170 2.442 progabide 3.50 -0.1
#> 171 2.442 progabide 3.50 0.1
#> 172 2.442 progabide 3.50 0.3
#> 173 2.197 progabide 3.04 -0.3
#> 174 2.197 progabide 3.04 -0.1
#> 175 2.197 progabide 3.04 0.1
#> 176 2.197 progabide 3.04 0.3
#> 177 2.251 progabide 3.56 -0.3
#> 178 2.251 progabide 3.56 -0.1
#> 179 2.251 progabide 3.56 0.1
#> 180 2.251 progabide 3.56 0.3
#> 181 0.560 progabide 3.22 -0.3
#> 182 0.560 progabide 3.22 -0.1
#> 183 0.560 progabide 3.22 0.1
#> 184 0.560 progabide 3.22 0.3
#> 185 2.197 progabide 3.26 -0.3
#> 186 2.197 progabide 3.26 -0.1
#> 187 2.197 progabide 3.26 0.1
#> 188 2.197 progabide 3.26 0.3
#> 189 1.012 progabide 3.22 -0.3
#> 190 1.012 progabide 3.22 -0.1
#> 191 1.012 progabide 3.22 0.1
#> 192 1.012 progabide 3.22 0.3
#> 193 3.631 progabide 3.09 -0.3
#> 194 3.631 progabide 3.09 -0.1
#> 195 3.631 progabide 3.09 0.1
#> 196 3.631 progabide 3.09 0.3
#> 197 1.705 progabide 3.47 -0.3
#> 198 1.705 progabide 3.47 -0.1
#> 199 1.705 progabide 3.47 0.1
#> 200 1.705 progabide 3.47 0.3
#> 201 2.327 progabide 3.22 -0.3
#> 202 2.327 progabide 3.22 -0.1
#> 203 2.327 progabide 3.22 0.1
#> 204 2.327 progabide 3.22 0.3
#> 205 2.079 progabide 3.56 -0.3
#> 206 2.079 progabide 3.56 -0.1
#> 207 2.079 progabide 3.56 0.1
#> 208 2.079 progabide 3.56 0.3
#> 209 2.639 progabide 3.04 -0.3
#> 210 2.639 progabide 3.04 -0.1
#> 211 2.639 progabide 3.04 0.1
#> 212 2.639 progabide 3.04 0.3
#> 213 1.792 progabide 3.71 -0.3
#> 214 1.792 progabide 3.71 -0.1
#> 215 1.792 progabide 3.71 0.1
#> 216 1.792 progabide 3.71 0.3
#> 217 1.386 progabide 3.47 -0.3
#> 218 1.386 progabide 3.47 -0.1
#> 219 1.386 progabide 3.47 0.1
#> 220 1.386 progabide 3.47 0.3
#> 221 1.705 progabide 3.26 -0.3
#> 222 1.705 progabide 3.26 -0.1
#> 223 1.705 progabide 3.26 0.1
#> 224 1.705 progabide 3.26 0.3
#> 225 1.833 progabide 3.04 -0.3
#> 226 1.833 progabide 3.04 -0.1
#> 227 1.833 progabide 3.04 0.1
#> 228 1.833 progabide 3.04 0.3
#> 229 1.179 progabide 3.58 -0.3
#> 230 1.179 progabide 3.58 -0.1
#> 231 1.179 progabide 3.58 0.1
#> 232 1.179 progabide 3.58 0.3
#> 233 1.099 progabide 3.61 -0.3
#> 234 1.099 progabide 3.61 -0.1
#> 235 1.099 progabide 3.61 0.1
#> 236 1.099 progabide 3.61 0.3
#> ---end{recover_data}--------------
#>
#> refit:
#> -----
#> ** Error: argument "newresp" is missing, with no default
#> ---end{refit}--------------
#>
#> residuals:
#> -----
#> 1 2 3 4 5 6 7 8
#> 1.28616 -0.51932 -0.33500 -0.16032 -0.70036 1.49358 -0.32265 -0.14850
#> 9 10 11 12 13 14 15 16
#> -0.52082 1.61307 -2.26015 2.85989 0.70623 0.87963 -1.95610 1.19952
#> 17 18 19 20 21 22 23 24
#> -8.01266 3.77251 -4.48338 8.22181 -1.39350 -4.06278 2.25084 1.54823
#> 25 26 27 28 29 30 31 32
#> 2.56912 0.74723 -3.08390 -0.92381 16.84072 -1.92488 0.24372 -7.64997
#> 33 34 35 36 37 38 39 40
#> -1.32427 0.00317 0.31365 -0.39194 7.02952 6.41027 -0.22978 -5.88949
#> 41 42 43 44 45 46 47 48
#> 8.68965 -4.39885 -9.53535 7.28268 3.86846 -1.70725 0.69490 -2.92393
#> 49 50 51 52 53 54 55 56
#> -0.46494 -0.23254 1.98777 -1.80339 -4.69066 -2.08210 1.49477 4.04162
#> 57 58 59 60 61 62 63 64
#> -0.62229 8.23619 -4.94966 -5.17756 5.10456 -5.60050 -5.32031 -0.05413
#> 65 66 67 68 69 70 71 72
#> -2.72771 -2.59045 0.53989 0.66368 4.71255 -1.58508 -0.97246 1.55513
#> 73 74 75 76 77 78 79 80
#> -1.49494 0.73704 -2.04295 1.16570 -1.56674 -4.32941 1.89558 3.10889
#> 81 82 83 84 85 86 87 88
#> -0.77914 0.41848 -0.39423 0.78326 -0.30571 0.86982 0.03603 1.19342
#> 89 90 91 92 93 94 95 96
#> -2.04903 -0.84071 -0.64310 1.54433 0.07396 4.48988 -5.11602 1.25739
#> 97 98 99 100 101 102 103 104
#> -15.91980 -8.06278 45.69258 -3.64817 -0.25896 -1.14358 -0.03410 -0.93020
#> 105 106 107 108 109 110 111 112
#> 0.32957 -1.53067 1.60177 -0.27272 -0.63994 2.08167 0.76510 0.41237
#> 113 114 115 116 117 118 119 120
#> -1.20902 2.41876 -1.98574 -2.42087 0.08892 -0.50103 1.88778 -2.74357
#> 121 122 123 124 125 126 127 128
#> -2.35623 1.76490 0.87981 -2.01119 0.24433 3.39209 -1.46808 0.66426
#> 129 130 131 132 133 134 135 136
#> -2.42199 1.81422 3.03781 0.24945 0.54211 -0.27999 -2.11125 0.04882
#> 137 138 139 140 141 142 143 144
#> 5.28770 1.20458 4.07115 1.89018 0.48142 -0.27615 2.95328 0.17039
#> 145 146 147 148 149 150 151 152
#> -0.34393 1.78017 -2.10230 2.00901 -5.01350 -0.60897 -0.22485 0.13987
#> 153 154 155 156 157 158 159 160
#> -3.95503 10.45942 -5.14772 -1.77533 0.91356 -0.02967 0.02413 -0.92487
#> 161 162 163 164 165 166 167 168
#> -2.43376 -0.31016 1.80715 -2.08149 1.94529 1.10590 -2.74193 0.40223
#> 169 170 171 172 173 174 175 176
#> -5.47459 -1.59704 10.23378 1.02034 2.97767 -1.65531 -3.30747 2.02218
#> 177 178 179 180 181 182 183 184
#> 8.75705 -2.70549 -3.19624 -1.71370 -0.42943 -0.35335 0.71868 1.78687
#> 185 186 187 188 189 190 191 192
#> -2.47015 1.97470 0.39619 0.79554 0.71368 -0.22035 -1.15777 -1.09840
#> 193 194 195 196 197 198 199 200
#> 27.89401 -5.10513 5.67974 0.26027 0.12804 -0.67180 -1.48200 0.69800
#> 201 202 203 204 205 206 207 208
#> 0.54005 -1.07404 -1.70810 0.63892 -2.95861 -0.75957 -2.57053 1.60900
#> 209 210 211 212 213 214 215 216
#> 0.45763 -5.60605 12.28029 -1.88068 1.86062 -0.92732 0.27388 -3.53523
#> 217 218 219 220 221 222 223 224
#> -0.76205 1.43642 0.62442 -0.19750 -10.11845 12.49410 9.07291 -1.38016
#> 225 226 227 228 229 230 231 232
#> -0.54350 0.58514 -2.29272 -1.17676 -1.15543 -1.09783 -1.04311 -0.99111
#> 233 234 235 236
#> -1.58929 1.54661 0.67539 -0.20260
#> ---end{residuals}--------------
#>
#> sigma:
#> -----
#> [1] 7.46
#> ---end{sigma}--------------
#>
#> simulate:
#> -----
#> sim_1
#> 1 2
#> 2 2
#> 3 7
#> 4 3
#> 5 7
#> 6 9
#> 7 6
#> 8 11
#> 9 4
#> 10 0
#> 11 0
#> 12 5
#> 13 4
#> 14 2
#> 15 1
#> 16 4
#> 17 7
#> 18 9
#> 19 6
#> 20 10
#> 21 6
#> 22 10
#> 23 5
#> 24 3
#> 25 2
#> 26 4
#> 27 3
#> 28 5
#> 29 9
#> 30 29
#> 31 11
#> 32 24
#> 33 15
#> 34 4
#> 35 15
#> 36 8
#> 37 4
#> 38 1
#> 39 3
#> 40 5
#> 41 8
#> 42 27
#> 43 21
#> 44 12
#> 45 36
#> 46 24
#> 47 25
#> 48 3
#> 49 6
#> 50 11
#> 51 7
#> 52 2
#> 53 7
#> 54 2
#> 55 5
#> 56 3
#> 57 39
#> 58 20
#> 59 19
#> 60 34
#> 61 3
#> 62 9
#> 63 4
#> 64 7
#> 65 27
#> 66 9
#> 67 11
#> 68 9
#> 69 31
#> 70 26
#> 71 17
#> 72 17
#> 73 4
#> 74 2
#> 75 2
#> 76 1
#> 77 3
#> 78 2
#> 79 1
#> 80 4
#> 81 4
#> 82 8
#> 83 16
#> 84 2
#> 85 8
#> 86 8
#> 87 4
#> 88 9
#> 89 6
#> 90 11
#> 91 6
#> 92 5
#> 93 6
#> 94 3
#> 95 3
#> 96 7
#> 97 11
#> 98 4
#> 99 11
#> 100 7
#> 101 8
#> 102 6
#> 103 5
#> 104 11
#> 105 2
#> 106 8
#> 107 3
#> 108 2
#> 109 1
#> 110 3
#> 111 2
#> 112 5
#> 113 7
#> 114 3
#> 115 10
#> 116 4
#> 117 11
#> 118 2
#> 119 5
#> 120 1
#> 121 7
#> 122 3
#> 123 1
#> 124 4
#> 125 2
#> 126 3
#> 127 3
#> 128 6
#> 129 2
#> 130 4
#> 131 2
#> 132 2
#> 133 1
#> 134 1
#> 135 0
#> 136 3
#> 137 6
#> 138 14
#> 139 8
#> 140 3
#> 141 2
#> 142 4
#> 143 4
#> 144 1
#> 145 1
#> 146 3
#> 147 0
#> 148 3
#> 149 7
#> 150 3
#> 151 1
#> 152 7
#> 153 18
#> 154 6
#> 155 10
#> 156 8
#> 157 0
#> 158 0
#> 159 1
#> 160 0
#> 161 3
#> 162 2
#> 163 3
#> 164 3
#> 165 4
#> 166 7
#> 167 1
#> 168 8
#> 169 18
#> 170 11
#> 171 20
#> 172 4
#> 173 7
#> 174 5
#> 175 9
#> 176 6
#> 177 12
#> 178 2
#> 179 8
#> 180 15
#> 181 0
#> 182 0
#> 183 0
#> 184 3
#> 185 1
#> 186 3
#> 187 4
#> 188 2
#> 189 1
#> 190 2
#> 191 3
#> 192 1
#> 193 38
#> 194 28
#> 195 23
#> 196 19
#> 197 16
#> 198 3
#> 199 11
#> 200 1
#> 201 9
#> 202 7
#> 203 3
#> 204 7
#> 205 9
#> 206 2
#> 207 10
#> 208 9
#> 209 10
#> 210 17
#> 211 1
#> 212 9
#> 213 10
#> 214 13
#> 215 3
#> 216 5
#> 217 0
#> 218 3
#> 219 4
#> 220 5
#> 221 3
#> 222 1
#> 223 5
#> 224 2
#> 225 4
#> 226 4
#> 227 5
#> 228 5
#> 229 5
#> 230 8
#> 231 5
#> 232 4
#> 233 0
#> 234 2
#> 235 2
#> 236 2
#> ---end{simulate}--------------
#>
#> summary:
#> -----
#> Family: nbinom2 ( log )
#> Formula: y ~ Base * trt + Age + Visit + (Visit | subject)
#> Data: epil2
#>
#> AIC BIC logLik deviance df.resid
#> 1269 1304 -625 1249 226
#>
#> Random effects:
#>
#> Conditional model:
#> Groups Name Variance Std.Dev. Corr
#> subject (Intercept) 2.17e-01 0.4660
#> Visit 5.33e-05 0.0073 -1.00
#> Number of obs: 236, groups: subject, 59
#>
#> Dispersion parameter for nbinom2 family (): 7.46
#>
#> Conditional model:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.322 1.197 -1.10 0.269
#> Base 0.884 0.131 6.74 1.6e-11 ***
#> trtprogabide -0.928 0.402 -2.31 0.021 *
#> Age 0.473 0.353 1.34 0.180
#> Visit -0.268 0.173 -1.55 0.121
#> Base:trtprogabide 0.336 0.204 1.65 0.100 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---end{summary}--------------
#>
#> terms:
#> -----
#> y ~ Base * trt + Age + Visit
#> attr(,"variables")
#> list(y, Base, trt, Age, Visit)
#> attr(,"factors")
#> Base trt Age Visit Base:trt
#> y 0 0 0 0 0
#> Base 1 0 0 0 1
#> trt 0 1 0 0 1
#> Age 0 0 1 0 0
#> Visit 0 0 0 1 0
#> attr(,"term.labels")
#> [1] "Base" "trt" "Age" "Visit" "Base:trt"
#> attr(,"order")
#> [1] 1 1 1 1 2
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,".Environment")
#> <environment: 0x7fa874e3ac80>
#> attr(,"predvars")
#> list(y, Base, trt, Age, Visit)
#> attr(,"dataClasses")
#> y Base trt Age Visit
#> "numeric" "numeric" "factor" "numeric" "numeric"
#> ---end{terms}--------------
#>
#> vcov:
#> -----
#> Conditional model:
#> (Intercept) Base trtprogabide Age Visit
#> (Intercept) 1.43367 -0.022869 0.03038 -0.41260 0.008344
#> Base -0.02287 0.017201 0.03157 -0.00242 0.000321
#> trtprogabide 0.03038 0.031572 0.16143 -0.02925 0.001870
#> Age -0.41260 -0.002417 -0.02925 0.12451 -0.002613
#> Visit 0.00834 0.000321 0.00187 -0.00261 0.030015
#> Base:trtprogabide -0.03366 -0.017596 -0.07637 0.01951 -0.001092
#> Base:trtprogabide
#> (Intercept) -0.03366
#> Base -0.01760
#> trtprogabide -0.07637
#> Age 0.01951
#> Visit -0.00109
#> Base:trtprogabide 0.04172
#>
#> ---end{vcov}--------------
#>
#> weights:
#> -----
#> NULL
#> ---end{weights}--------------
#>
options(op)
# }