HTKS and relevant outcome
## Warning: package 'dplyr' was built under R version 3.6.2## Warning: package 'nlme' was built under R version 3.6.2## Warning: package 'lme4' was built under R version 3.6.2## Warning: package 'ggplot2' was built under R version 3.6.2## Warning: package 'tibble' was built under R version 3.6.2## Warning: package 'tidyr' was built under R version 3.6.2## Warning: package 'purrr' was built under R version 3.6.2Summary table
## table of Age:##
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 68
## 2 4 2 4 18 24 26 22 16 18 20 24 30 14 4 2 2## table of categorical Age:##
## 7 8 9 10 11
## 8 110 110 2 2## ratio of data with missingness:## [1] 0.09745763## table of HT:##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 95 6 20 6 10 2 7 1 8 2 4 3 5 2 5 4 5 4 7 5 5## table of HTKS:##
## 0 1 2 3 4 5 6 7 8 10 11 12 13 14 16 17 18 19 20 21 22 23 26 27 29 30
## 95 6 20 5 6 1 3 1 8 4 1 4 2 7 4 2 2 2 3 2 4 1 1 1 2 2
## 31 32 34 37 38 40 43 46 47 48 52
## 1 1 2 3 1 2 2 1 2 1 1## table of HT_P:##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 27
## 34 10 22 8 30 7 7 3 4 4 6 2 5 5 3 1 3 5 3 5 2 3 2 6 5 4
## 28 29 30 31 32
## 2 4 4 4 3## table of HTKS_P:##
## 0 1 2 3 4 5 6 7 8 9 10 11 14 15 16 17 19 20 22 24 25 26 28 29 30 33
## 34 10 22 8 29 7 5 3 5 3 6 1 1 2 1 2 1 3 6 1 2 2 5 1 1 3
## 34 36 37 38 39 40 41 42 44 47 50 52 54 56 57 59 60 61 63 65 67 68 71 75 76 77
## 3 3 1 1 1 1 2 3 1 3 1 2 3 1 1 1 1 1 2 1 1 2 1 1 2 1
## 84
## 1## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
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Outcome1 : Random effect model: Does treatment have effect on HT?
We consider negetaive binomial, poisson, quasi-poisson models, if not inluded, then it doesn’t converge.
Zero Inflated effect model: Full Model
## full model## Family: nbinom2 ( log )
## Formula:
## HT ~ age + I(age^2) + as.factor(Qual_Visit) + Treatment + Receive_Treatment *
## as.factor(Sex) + as.factor(Race_Ethn) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Treatment + Receive_Treatment * as.factor(Sex) +
## as.factor(Race_Ethn) + as.factor(Time) + (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 983.2 1069.8 -465.6 931.2 180
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.2299 0.4794
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.2059 0.4538
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 5.02
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.510663 0.248376 6.082 1.19e-09 ***
## age 0.043942 0.036620 1.200 0.23015
## I(age^2) 0.003866 0.006532 0.592 0.55388
## as.factor(Qual_Visit)2 0.081875 0.191580 0.427 0.66911
## Treatment 0.297941 0.258072 1.154 0.24830
## Receive_Treatment -0.282794 0.309378 -0.914 0.36068
## as.factor(Sex)2 0.302454 0.230474 1.312 0.18942
## as.factor(Race_Ethn)4 0.112887 0.352363 0.320 0.74869
## as.factor(Race_Ethn)6 0.275433 0.263880 1.044 0.29659
## as.factor(Race_Ethn)7 -0.816554 0.308583 -2.646 0.00814 **
## as.factor(Time)2 0.536338 0.236318 2.270 0.02323 *
## Receive_Treatment:as.factor(Sex)2 0.117131 0.286158 0.409 0.68230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.30957 0.43305 0.715 0.4747
## age -0.12980 0.07800 -1.664 0.0961 .
## I(age^2) -0.03226 0.01504 -2.146 0.0319 *
## Treatment -0.12080 0.46854 -0.258 0.7965
## Receive_Treatment -1.30011 0.72989 -1.781 0.0749 .
## as.factor(Sex)2 0.39454 0.41117 0.960 0.3373
## as.factor(Race_Ethn)4 -0.52294 0.65919 -0.793 0.4276
## as.factor(Race_Ethn)6 -0.16257 0.50243 -0.324 0.7463
## as.factor(Race_Ethn)7 -0.10100 0.53990 -0.187 0.8516
## as.factor(Time)2 -0.13324 0.49887 -0.267 0.7894
## Receive_Treatment:as.factor(Sex)2 0.95652 0.75650 1.264 0.2061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula:
## HT ~ age + I(age^2) + as.factor(Qual_Visit) + Treatment + Receive_Treatment *
## as.factor(Sex) + as.factor(Race_Ethn) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + as.factor(Qual_Visit) + Treatment + Receive_Treatment *
## as.factor(Sex) + as.factor(Race_Ethn) + as.factor(Time) + (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 993.8 1080.3 -470.9 941.8 180
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.4094 0.6399
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.2163 0.4651
## Number of obs: 206, groups: Child_ID, 103
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.467416 0.218802 6.707 1.99e-11 ***
## age 0.047310 0.037750 1.253 0.21011
## I(age^2) 0.006138 0.006659 0.922 0.35672
## as.factor(Qual_Visit)2 -0.022183 0.133025 -0.167 0.86756
## Treatment 0.266823 0.227124 1.175 0.24008
## Receive_Treatment -0.166524 0.197568 -0.843 0.39930
## as.factor(Sex)2 0.310444 0.220515 1.408 0.15919
## as.factor(Race_Ethn)4 0.107209 0.352455 0.304 0.76099
## as.factor(Race_Ethn)6 0.326314 0.273615 1.193 0.23303
## as.factor(Race_Ethn)7 -0.836547 0.321238 -2.604 0.00921 **
## as.factor(Time)2 0.401267 0.150361 2.669 0.00761 **
## Receive_Treatment:as.factor(Sex)2 0.001828 0.175278 0.010 0.99168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.28319 0.43956 0.644 0.5194
## age -0.12932 0.07797 -1.659 0.0972 .
## I(age^2) -0.03230 0.01501 -2.152 0.0314 *
## as.factor(Qual_Visit)2 0.21431 0.40477 0.530 0.5965
## Treatment -0.15587 0.46574 -0.335 0.7379
## Receive_Treatment -1.29967 0.72802 -1.785 0.0742 .
## as.factor(Sex)2 0.41185 0.41324 0.997 0.3189
## as.factor(Race_Ethn)4 -0.50993 0.65833 -0.775 0.4386
## as.factor(Race_Ethn)6 -0.14727 0.49880 -0.295 0.7678
## as.factor(Race_Ethn)7 -0.09750 0.53778 -0.181 0.8561
## as.factor(Time)2 -0.13434 0.49713 -0.270 0.7870
## Receive_Treatment:as.factor(Sex)2 0.93813 0.75489 1.243 0.2140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Zero Inflated Random effect model: Reduced Model
## reduced model## Family: nbinom2 ( log )
## Formula:
## HT ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 977.6 1030.9 -472.8 945.6 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.3133 0.5597
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.1891 0.4348
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 4.9
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.614502 0.179023 9.018 <2e-16 ***
## age 0.042714 0.037713 1.133 0.2574
## I(age^2) 0.004906 0.006712 0.731 0.4648
## Receive_Treatment 0.035794 0.240230 0.149 0.8816
## as.factor(Sex)2 0.396148 0.233311 1.698 0.0895 .
## as.factor(Time)2 0.337106 0.200267 1.683 0.0923 .
## Receive_Treatment:as.factor(Sex)2 0.071788 0.296982 0.242 0.8090
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.09399 0.28038 0.335 0.7374
## age -0.11080 0.07611 -1.456 0.1454
## I(age^2) -0.02989 0.01485 -2.012 0.0442 *
## Receive_Treatment -1.00335 0.47876 -2.096 0.0361 *
## as.factor(Sex)2 0.60853 0.34773 1.750 0.0801 .
## as.factor(Time)2 -0.09917 0.41327 -0.240 0.8103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: poisson ( log )
## Formula:
## HT ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 191
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.8311 0.9117
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.4584 0.6771
## Number of obs: 206, groups: Child_ID, 103
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.226557 0.124332 9.865 < 2e-16 ***
## age 0.020136 0.015411 1.307 0.1913
## I(age^2) 0.008692 0.002014 4.317 1.59e-05 ***
## Receive_Treatment 0.020851 0.188151 0.111 0.9118
## as.factor(Sex)2 0.185815 0.165080 1.126 0.2603
## as.factor(Time)2 0.298772 0.143178 2.087 0.0369 *
## Receive_Treatment:as.factor(Sex)2 -0.064997 0.148672 -0.437 0.6620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.16862 0.31598 -0.534 0.594
## age -0.24566 0.02531 -9.708 < 2e-16 ***
## I(age^2) -0.04865 0.01209 -4.025 5.69e-05 ***
## Receive_Treatment -0.52575 0.51778 -1.015 0.310
## as.factor(Sex)2 0.28642 0.31381 0.913 0.361
## as.factor(Time)2 -0.49417 0.51898 -0.952 0.341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: nbinom1 ( log )
## Formula:
## HT ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.654 0.8087
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.1097 0.3311
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom1 family (): 1.18
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.556233 0.177371 8.774 <2e-16 ***
## age 0.048942 0.023212 2.108 0.035 *
## I(age^2) 0.008726 0.007647 1.141 0.254
## Receive_Treatment 0.115753 0.239926 0.482 0.629
## as.factor(Sex)2 0.181026 0.409372 0.442 0.658
## as.factor(Time)2 0.215642 0.198478 1.086 0.277
## Receive_Treatment:as.factor(Sex)2 -0.096110 0.632726 -0.152 0.879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.001387 0.269985 0.005 0.9959
## age -0.159863 0.119492 -1.338 0.1809
## I(age^2) -0.034336 0.021960 -1.563 0.1179
## Receive_Treatment -1.367650 0.663211 -2.062 0.0392 *
## as.factor(Sex)2 0.205546 0.437136 0.470 0.6382
## as.factor(Time)2 -0.035536 0.478748 -0.074 0.9408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Random effect model: Binary Model
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00443251 (tol = 0.002, component 1)## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) -0.27886715 0.41174346 -0.6772837 0.49822595 0.741
## as.factor(Qual_Visit)2 -0.21038291 0.38561636 -0.5455757 0.58535764 0.818
## as.factor(Sex)2 -0.60977439 0.35284152 -1.7281821 0.08395559 0.073
## age 0.12615674 0.07588085 1.6625636 0.09639982 0.093
## I(age^2) 0.03200379 0.01478862 2.1640820 0.03045806 0.010
## as.factor(Race_Ethn)4 0.49227044 0.64964109 0.7577575 0.44859614 0.686
## as.factor(Race_Ethn)6 0.13765101 0.48587198 0.2833072 0.77694138 0.924
## as.factor(Race_Ethn)7 -0.17635269 0.47098724 -0.3744320 0.70808295 0.895
## Treatment 0.16579101 0.44590377 0.3718089 0.71003511 0.896
## Receive_Treatment 0.86452299 0.62098955 1.3921700 0.16387094 0.216
## as.factor(Time)2 0.20647059 0.48007348 0.4300812 0.66713656 0.873## Data: dat
## Models:
## model0: I(HT != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + as.factor(Race_Ethn) +
## model0: (1 | Child_ID) + Treatment + Receive_Treatment + as.factor(Time)
## model1: I(HT != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model1: as.factor(Race_Ethn) + (1 | Child_ID) + Treatment + Receive_Treatment +
## model1: as.factor(Time)
## model2: I(HT != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model2: I(age^2) + as.factor(Race_Ethn) + (1 | Child_ID) + Treatment +
## model2: Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model0 10 290.44 323.72 -135.22 270.44
## model1 11 292.44 329.05 -135.22 270.44 0.0003 1 0.98606
## model2 12 289.14 329.07 -132.57 265.14 5.3078 1 0.02123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: dat
## Models:
## model3: I(HT != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model3: Receive_Treatment + as.factor(Time)
## model4: I(HT != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model4: Treatment + Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model3 7 280.43 303.72 -133.21 266.43
## model4 8 282.31 308.94 -133.16 266.31 0.1146 1 0.7349## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) -0.22299214 0.26520177 -0.8408396 0.40043783 0.625
## as.factor(Sex)2 -0.52906666 0.33663716 -1.5716229 0.11603804 0.126
## age 0.11880038 0.07445296 1.5956434 0.11056844 0.117
## I(age^2) 0.03081625 0.01455145 2.1177453 0.03419665 0.013
## Receive_Treatment 0.98623380 0.45978718 2.1449789 0.03195453 0.011
## as.factor(Time)2 0.13922908 0.40026545 0.3478419 0.72795894 0.904Treatment, “the treatment & sex”, “the treatment & categorical age” have no significant influence.
Outcome 2: Random effect model: Does treatment have effect on HTKS?
Zero Inflated effect model: Full Model
We consider negetaive binomial, poisson, quasi-poisson models, if not inluded, then it doesn’t converge.
## full model## Family: nbinom2 ( log )
## Formula:
## HTKS ~ age + I(age^2) + as.factor(Qual_Visit) + Treatment + Receive_Treatment *
## as.factor(Sex) + as.factor(Race_Ethn) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Treatment + Receive_Treatment * as.factor(Sex) +
## as.factor(Race_Ethn) + as.factor(Time) + (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 1098.1 1184.6 -523.0 1046.1 180
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.471 0.6863
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.2154 0.4641
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 2.51
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.712934 0.326228 5.251 1.52e-07 ***
## age 0.066670 0.050130 1.330 0.1835
## I(age^2) 0.005830 0.008877 0.657 0.5114
## as.factor(Qual_Visit)2 0.064155 0.253879 0.253 0.8005
## Treatment 0.287278 0.342411 0.839 0.4015
## Receive_Treatment -0.259775 0.398238 -0.652 0.5142
## as.factor(Sex)2 0.312444 0.316258 0.988 0.3232
## as.factor(Race_Ethn)4 -0.032379 0.486988 -0.066 0.9470
## as.factor(Race_Ethn)6 0.444681 0.365304 1.217 0.2235
## as.factor(Race_Ethn)7 -0.770613 0.401156 -1.921 0.0547 .
## as.factor(Time)2 0.641397 0.303548 2.113 0.0346 *
## Receive_Treatment:as.factor(Sex)2 0.486362 0.367556 1.323 0.1858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.26772 0.44438 0.602 0.5469
## age -0.12659 0.07959 -1.591 0.1117
## I(age^2) -0.03209 0.01542 -2.081 0.0374 *
## Treatment -0.13767 0.48324 -0.285 0.7757
## Receive_Treatment -1.34402 0.76779 -1.750 0.0800 .
## as.factor(Sex)2 0.40701 0.42294 0.962 0.3359
## as.factor(Race_Ethn)4 -0.53025 0.67996 -0.780 0.4355
## as.factor(Race_Ethn)6 -0.14873 0.51551 -0.288 0.7730
## as.factor(Race_Ethn)7 -0.09500 0.55214 -0.172 0.8634
## as.factor(Time)2 -0.12472 0.50920 -0.245 0.8065
## Receive_Treatment:as.factor(Sex)2 1.04140 0.78607 1.325 0.1852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Zero Inflated Random effect model: Reduced Model
## reduced model## Family: nbinom2 ( log )
## Formula: HTKS ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 1088.6 1141.9 -528.3 1056.6 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.5446 0.738
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.1904 0.4364
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 2.4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.827079 0.226394 8.070 7.01e-16 ***
## age 0.069730 0.049550 1.407 0.1593
## I(age^2) 0.006496 0.008791 0.739 0.4600
## Receive_Treatment 0.049833 0.312835 0.159 0.8734
## as.factor(Sex)2 0.432536 0.305524 1.416 0.1569
## as.factor(Time)2 0.467626 0.261496 1.788 0.0737 .
## Receive_Treatment:as.factor(Sex)2 0.414647 0.372859 1.112 0.2661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02992 0.29218 0.102 0.9184
## age -0.10606 0.07748 -1.369 0.1710
## I(age^2) -0.02981 0.01525 -1.955 0.0506 .
## Receive_Treatment -1.00247 0.49121 -2.041 0.0413 *
## as.factor(Sex)2 0.64775 0.35798 1.809 0.0704 .
## as.factor(Time)2 -0.08014 0.42320 -0.189 0.8498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: nbinom1 ( log )
## Formula: HTKS ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 1078.8 1132.0 -523.4 1046.8 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.521 0.7218
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.3065 0.5536
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom1 family (): 5.12
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.821505 0.217508 8.374 <2e-16 ***
## age 0.047796 0.050521 0.946 0.3441
## I(age^2) 0.003563 0.008441 0.422 0.6729
## Receive_Treatment 0.076588 0.279428 0.274 0.7840
## as.factor(Sex)2 0.382016 0.318488 1.199 0.2303
## as.factor(Time)2 0.445403 0.248577 1.792 0.0732 .
## Receive_Treatment:as.factor(Sex)2 0.271226 0.338785 0.801 0.4234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22164 0.36698 -0.604 0.5459
## age -0.09543 0.08687 -1.099 0.2720
## I(age^2) -0.02991 0.01717 -1.742 0.0816 .
## Receive_Treatment -1.00067 0.55183 -1.813 0.0698 .
## as.factor(Sex)2 0.79625 0.47185 1.688 0.0915 .
## as.factor(Time)2 -0.03809 0.48873 -0.078 0.9379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Random effect model: Binary Model
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00443251 (tol = 0.002, component 1)## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) -0.27886715 0.41174346 -0.6772837 0.49822595 0.741
## as.factor(Qual_Visit)2 -0.21038291 0.38561636 -0.5455757 0.58535764 0.818
## as.factor(Sex)2 -0.60977439 0.35284152 -1.7281821 0.08395559 0.073
## age 0.12615674 0.07588085 1.6625636 0.09639982 0.093
## I(age^2) 0.03200379 0.01478862 2.1640820 0.03045806 0.010
## as.factor(Race_Ethn)4 0.49227044 0.64964109 0.7577575 0.44859614 0.686
## as.factor(Race_Ethn)6 0.13765101 0.48587198 0.2833072 0.77694138 0.924
## as.factor(Race_Ethn)7 -0.17635269 0.47098724 -0.3744320 0.70808295 0.895
## Treatment 0.16579101 0.44590377 0.3718089 0.71003511 0.896
## Receive_Treatment 0.86452299 0.62098955 1.3921700 0.16387094 0.216
## as.factor(Time)2 0.20647059 0.48007348 0.4300812 0.66713656 0.873## Data: dat
## Models:
## model0: I(HTKS != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + as.factor(Race_Ethn) +
## model0: (1 | Child_ID) + Treatment + Receive_Treatment + as.factor(Time)
## model1: I(HTKS != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model1: as.factor(Race_Ethn) + (1 | Child_ID) + Treatment + Receive_Treatment +
## model1: as.factor(Time)
## model2: I(HTKS != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model2: I(age^2) + as.factor(Race_Ethn) + (1 | Child_ID) + Treatment +
## model2: Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model0 10 290.44 323.72 -135.22 270.44
## model1 11 292.44 329.05 -135.22 270.44 0.0003 1 0.98606
## model2 12 289.14 329.07 -132.57 265.14 5.3078 1 0.02123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: dat
## Models:
## model3: I(HTKS != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model3: Receive_Treatment + as.factor(Time)
## model4: I(HTKS != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model4: Treatment + Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model3 7 280.43 303.72 -133.21 266.43
## model4 8 282.31 308.94 -133.16 266.31 0.1146 1 0.7349## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) -0.22299214 0.26520177 -0.8408396 0.40043783 0.625
## as.factor(Sex)2 -0.52906666 0.33663716 -1.5716229 0.11603804 0.126
## age 0.11880038 0.07445296 1.5956434 0.11056844 0.117
## I(age^2) 0.03081625 0.01455145 2.1177453 0.03419665 0.013
## Receive_Treatment 0.98623380 0.45978718 2.1449789 0.03195453 0.011
## as.factor(Time)2 0.13922908 0.40026545 0.3478419 0.72795894 0.904Outcome3 : Random effect model: Does treatment have effect on HT_P?
Zero Inflated effect model: Full Model
## full model## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, : NA/
## NaN function evaluation## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: nbinom2 ( log )
## Formula:
## HT_P ~ age + I(age^2) + as.factor(Qual_Visit) + Treatment + Receive_Treatment *
## as.factor(Sex) + as.factor(Race_Ethn) + as.factor(Time) +
## (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Treatment + Receive_Treatment * as.factor(Sex) +
## as.factor(Race_Ethn) + as.factor(Time) + (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 180
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.5312 0.7288
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.3047 0.552
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 4.45
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.595466 0.217360 7.340 2.13e-13 ***
## age 0.079635 0.046065 1.729 0.08385 .
## I(age^2) 0.017216 0.009837 1.750 0.08009 .
## as.factor(Qual_Visit)2 -0.171973 0.158202 -1.087 0.27702
## Treatment 0.345099 0.306786 1.125 0.26064
## Receive_Treatment -0.069936 0.285114 -0.245 0.80623
## as.factor(Sex)2 -0.025724 0.224462 -0.115 0.90876
## as.factor(Race_Ethn)4 0.178528 0.349568 0.511 0.60955
## as.factor(Race_Ethn)6 0.408517 0.274475 1.488 0.13666
## as.factor(Race_Ethn)7 -0.519971 1.508401 -0.345 0.73031
## as.factor(Time)2 0.549963 0.201027 2.736 0.00622 **
## Receive_Treatment:as.factor(Sex)2 -0.022404 0.243334 -0.092 0.92664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.504581 0.713084 -3.512 0.000444 ***
## age -0.005113 0.115733 -0.044 0.964763
## I(age^2) 0.001300 0.018598 0.070 0.944277
## Treatment 0.335844 0.753957 0.445 0.656001
## Receive_Treatment -1.807761 2.403924 -0.752 0.452049
## as.factor(Sex)2 -0.213682 0.514236 -0.416 0.677752
## as.factor(Race_Ethn)4 0.884418 0.897059 0.986 0.324178
## as.factor(Race_Ethn)6 1.296004 0.617292 2.100 0.035773 *
## as.factor(Race_Ethn)7 0.888718 3.143708 0.283 0.777409
## as.factor(Time)2 0.620372 0.888166 0.698 0.484873
## Receive_Treatment:as.factor(Sex)2 0.232914 1.848805 0.126 0.899747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Zero Inflated Random effect model: Reduced Model
## reduced model## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: nbinom2 ( log )
## Formula: HT_P ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.6217 0.7885
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.7753 0.8805
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 3.91
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.773268 0.164202 10.799 < 2e-16 ***
## age 0.108179 0.046859 2.309 0.02097 *
## I(age^2) 0.022332 0.007521 2.969 0.00299 **
## Receive_Treatment 0.190610 0.267347 0.713 0.47587
## as.factor(Sex)2 -0.112851 0.441929 -0.255 0.79844
## as.factor(Time)2 0.341087 0.220499 1.547 0.12189
## Receive_Treatment:as.factor(Sex)2 -0.034334 0.700206 -0.049 0.96089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.933080 0.383221 -5.044 4.55e-07 ***
## age 0.103445 0.102771 1.007 0.3141
## I(age^2) 0.010309 0.019969 0.516 0.6057
## Receive_Treatment -1.434151 0.582117 -2.464 0.0138 *
## as.factor(Sex)2 0.003785 0.428096 0.009 0.9929
## as.factor(Time)2 0.384759 0.595367 0.646 0.5181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')
## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: nbinom1 ( log )
## Formula: HT_P ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.451 0.6716
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.2425 0.4924
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom1 family (): 2.61
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.724067 0.140869 12.239 <2e-16 ***
## age 0.052298 0.034116 1.533 0.1253
## I(age^2) 0.014095 0.006655 2.118 0.0342 *
## Receive_Treatment 0.147181 0.202262 0.728 0.4668
## as.factor(Sex)2 0.267979 0.192093 1.395 0.1630
## as.factor(Time)2 0.320474 0.164944 1.943 0.0520 .
## Receive_Treatment:as.factor(Sex)2 -0.010927 0.134021 -0.082 0.9350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.338662 0.491494 -4.758 1.95e-06 ***
## age 0.024992 0.109060 0.229 0.8187
## I(age^2) 0.008616 0.015046 0.573 0.5669
## Receive_Treatment -1.241055 0.577358 -2.150 0.0316 *
## as.factor(Sex)2 0.442203 0.517677 0.854 0.3930
## as.factor(Time)2 0.419607 0.534867 0.785 0.4327
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Random effect model: Binary Model
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0813713 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.123917 (tol = 0.002, component 1)## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) 1.999299989 0.65002680 3.0757193 0.002099954 0.000
## as.factor(Qual_Visit)2 0.736267749 0.60979499 1.2074021 0.227277328 0.339
## as.factor(Sex)2 -0.168855304 0.45010415 -0.3751472 0.707551007 0.894
## age 0.039304707 0.10220509 0.3845670 0.700558229 0.891
## I(age^2) 0.003181186 0.01872112 0.1699250 0.865069130 0.952
## as.factor(Race_Ethn)4 -0.511748614 0.81737630 -0.6260869 0.531257929 0.773
## as.factor(Race_Ethn)6 -0.993509107 0.60104383 -1.6529728 0.098336395 0.096
## as.factor(Race_Ethn)7 -0.790579101 0.58985047 -1.3403043 0.180146443 0.248
## Treatment -0.196019157 0.58352370 -0.3359232 0.736928772 0.908
## Receive_Treatment 1.189840391 0.82752709 1.4378265 0.150483284 0.191
## as.factor(Time)2 -0.297976708 0.59960613 -0.4969541 0.619221442 0.843## Data: dat
## Models:
## model0: I(HT_P != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + as.factor(Race_Ethn) +
## model0: (1 | Child_ID) + Treatment + Receive_Treatment + as.factor(Time)
## model1: I(HT_P != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model1: as.factor(Race_Ethn) + (1 | Child_ID) + Treatment + Receive_Treatment +
## model1: as.factor(Time)
## model2: I(HT_P != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model2: I(age^2) + as.factor(Race_Ethn) + (1 | Child_ID) + Treatment +
## model2: Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model0 10 193.33 226.60 -86.663 173.33
## model1 11 195.17 231.78 -86.584 173.17 0.1574 1 0.6916
## model2 12 197.13 237.06 -86.565 173.13 0.0394 1 0.8426## Data: dat
## Models:
## model3: I(HT_P != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model3: Receive_Treatment + as.factor(Time)
## model4: I(HT_P != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model4: Treatment + Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model3 7 192.68 215.97 -89.338 178.68
## model4 8 194.67 221.29 -89.334 178.67 0.0076 1 0.9304## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) 1.814284766 0.47196602 3.8441004 0.0001209955 0.000
## as.factor(Sex)2 -0.352011566 0.46062957 -0.7641966 0.4447500759 0.681
## age 0.048137616 0.10422774 0.4618503 0.6441886709 0.859
## I(age^2) 0.009718066 0.01931425 0.5031553 0.6148550885 0.840
## Receive_Treatment 1.178592652 0.64502752 1.8271975 0.0676700857 0.049
## as.factor(Time)2 -0.325604098 0.50288567 -0.6474714 0.5173268909 0.760Outcome4 : Random effect model: Does treatment have effect on HTKS_P?
Zero Inflated effect model: Full Model
## full model## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: poisson ( log )
## Formula:
## HTKS_P ~ age + I(age^2) + as.factor(Qual_Visit) + Treatment +
## Receive_Treatment * as.factor(Sex) + as.factor(Race_Ethn) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Treatment + Receive_Treatment * as.factor(Sex) +
## as.factor(Race_Ethn) + as.factor(Time) + (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 181
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 1.211 1.101
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.7055 0.84
## Number of obs: 206, groups: Child_ID, 103
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.740528 0.242440 7.179 7.01e-13 ***
## age 0.090669 0.051418 1.763 0.077840 .
## I(age^2) 0.022010 0.009327 2.360 0.018287 *
## as.factor(Qual_Visit)2 -0.262957 0.070627 -3.723 0.000197 ***
## Treatment 0.222053 0.239885 0.926 0.354621
## Receive_Treatment -0.023688 0.107619 -0.220 0.825788
## as.factor(Sex)2 0.187423 0.232663 0.806 0.420498
## as.factor(Race_Ethn)4 0.108875 0.458765 0.237 0.812408
## as.factor(Race_Ethn)6 0.540365 0.365466 1.479 0.139257
## as.factor(Race_Ethn)7 -0.533422 0.266062 -2.005 0.044976 *
## as.factor(Time)2 0.619235 0.088204 7.021 2.21e-12 ***
## Receive_Treatment:as.factor(Sex)2 0.063712 0.092328 0.690 0.490157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.999509 0.461896 -4.329 1.5e-05 ***
## age 0.025015 0.106580 0.235 0.814
## I(age^2) 0.006551 0.018912 0.346 0.729
## Treatment -0.402058 0.567460 -0.709 0.479
## Receive_Treatment -0.667694 0.816506 -0.818 0.414
## as.factor(Sex)2 0.294784 0.525106 0.561 0.575
## as.factor(Race_Ethn)4 0.204733 0.784017 0.261 0.794
## as.factor(Race_Ethn)6 0.981787 0.636930 1.541 0.123
## as.factor(Race_Ethn)7 0.314459 0.599973 0.524 0.600
## as.factor(Time)2 -0.055249 0.063567 -0.869 0.385
## Receive_Treatment:as.factor(Sex)2 0.193924 1.004581 0.193 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Zero Inflated Random effect model: Reduced Model
## reduced model## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: nbinom2 ( log )
## Formula:
## HTKS_P ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.6723 0.8199
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 2.603 1.613
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom2 family (): 0.892
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.949716 0.174795 11.154 <2e-16 ***
## age 0.107428 0.052685 2.039 0.0414 *
## I(age^2) 0.018813 0.009463 1.988 0.0468 *
## Receive_Treatment 0.312599 0.339883 0.920 0.3577
## as.factor(Sex)2 0.039089 0.280863 0.139 0.8893
## as.factor(Time)2 0.578750 0.261820 2.210 0.0271 *
## Receive_Treatment:as.factor(Sex)2 0.175951 0.419638 0.419 0.6750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.146 1.389 -2.985 0.002835 **
## age -3.333 2.766 -1.205 0.228179
## I(age^2) -2.411 1.682 -1.434 0.151571
## Receive_Treatment -4.600 1.388 -3.315 0.000917 ***
## as.factor(Sex)2 1.059 1.744 0.607 0.543570
## as.factor(Time)2 3.702 1.363 2.717 0.006585 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
## Hessian matrix. See vignette('troubleshooting')
## Warning in fitTMB(TMBStruc): Model convergence problem; false convergence (8).
## See vignette('troubleshooting')## Family: nbinom1 ( log )
## Formula:
## HTKS_P ~ age + I(age^2) + Receive_Treatment * as.factor(Sex) +
## as.factor(Time) + (1 | Child_ID)
## Zero inflation:
## ~age + I(age^2) + Receive_Treatment + as.factor(Sex) + as.factor(Time) +
## (1 | Child_ID)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 190
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.667 0.8167
## Number of obs: 206, groups: Child_ID, 103
##
## Zero-inflation model:
## Groups Name Variance Std.Dev.
## Child_ID (Intercept) 0.6289 0.793
## Number of obs: 206, groups: Child_ID, 103
##
## Overdispersion parameter for nbinom1 family (): 7.76
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.985724 0.204202 9.724 <2e-16 ***
## age 0.063411 0.047773 1.327 0.1844
## I(age^2) 0.019490 0.009475 2.057 0.0397 *
## Receive_Treatment 0.151842 0.375730 0.404 0.6861
## as.factor(Sex)2 0.158892 0.427585 0.372 0.7102
## as.factor(Time)2 0.401972 0.209148 1.922 0.0546 .
## Receive_Treatment:as.factor(Sex)2 0.088453 0.344725 0.257 0.7975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.40042 0.73592 -3.262 0.00111 **
## age 0.09747 0.11798 0.826 0.40874
## I(age^2) 0.02824 0.01812 1.559 0.11911
## Receive_Treatment -1.42640 0.59736 -2.388 0.01695 *
## as.factor(Sex)2 -0.01465 1.00078 -0.015 0.98832
## as.factor(Time)2 0.14314 0.66739 0.214 0.83018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Random effect model: Binary Model
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0813713 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.123917 (tol = 0.002, component 1)## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) 1.999299989 0.65002680 3.0757193 0.002099954 0.000
## as.factor(Qual_Visit)2 0.736267749 0.60979499 1.2074021 0.227277328 0.339
## as.factor(Sex)2 -0.168855304 0.45010415 -0.3751472 0.707551007 0.894
## age 0.039304707 0.10220509 0.3845670 0.700558229 0.891
## I(age^2) 0.003181186 0.01872112 0.1699250 0.865069130 0.952
## as.factor(Race_Ethn)4 -0.511748614 0.81737630 -0.6260869 0.531257929 0.773
## as.factor(Race_Ethn)6 -0.993509107 0.60104383 -1.6529728 0.098336395 0.096
## as.factor(Race_Ethn)7 -0.790579101 0.58985047 -1.3403043 0.180146443 0.248
## Treatment -0.196019157 0.58352370 -0.3359232 0.736928772 0.908
## Receive_Treatment 1.189840391 0.82752709 1.4378265 0.150483284 0.191
## as.factor(Time)2 -0.297976708 0.59960613 -0.4969541 0.619221442 0.843## Data: dat
## Models:
## model0: I(HTKS_P != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + as.factor(Race_Ethn) +
## model0: (1 | Child_ID) + Treatment + Receive_Treatment + as.factor(Time)
## model1: I(HTKS_P != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model1: as.factor(Race_Ethn) + (1 | Child_ID) + Treatment + Receive_Treatment +
## model1: as.factor(Time)
## model2: I(HTKS_P != 0) ~ as.factor(Qual_Visit) + as.factor(Sex) + (age) +
## model2: I(age^2) + as.factor(Race_Ethn) + (1 | Child_ID) + Treatment +
## model2: Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model0 10 193.33 226.60 -86.663 173.33
## model1 11 195.17 231.78 -86.584 173.17 0.1574 1 0.6916
## model2 12 197.13 237.06 -86.565 173.13 0.0394 1 0.8426## Data: dat
## Models:
## model3: I(HTKS_P != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model3: Receive_Treatment + as.factor(Time)
## model4: I(HTKS_P != 0) ~ as.factor(Sex) + (age) + I(age^2) + (1 | Child_ID) +
## model4: Treatment + Receive_Treatment + as.factor(Time)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model3 7 192.68 215.97 -89.338 178.68
## model4 8 194.67 221.29 -89.334 178.67 0.0076 1 0.9304## Estimate Std. Error z value Pr(>|z|) p.value
## (Intercept) 1.814284766 0.47196602 3.8441004 0.0001209955 0.000
## as.factor(Sex)2 -0.352011566 0.46062957 -0.7641966 0.4447500759 0.681
## age 0.048137616 0.10422774 0.4618503 0.6441886709 0.859
## I(age^2) 0.009718066 0.01931425 0.5031553 0.6148550885 0.840
## Receive_Treatment 1.178592652 0.64502752 1.8271975 0.0676700857 0.049
## as.factor(Time)2 -0.325604098 0.50288567 -0.6474714 0.5173268909 0.760