DEM7223_Homework 1
Steven Weeks
10/15/2019
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericPart A
Descriptive Analysis
1) Define your event variable - The event variable is the transition of being a non-United States citizen to becoming a naturalized United States citizen between wave 3 and wave 4.
2) Define a duration or time variable - If the age at becoming a naturalized United States citizen is censored, then the age of an individual at the date of interview is the censored age. If becoming a naturalized United States citizen is not censored, the actual age of an individual when citizenship is gained is the time variable.
3) Define a censoring indicator - If someone has experienced the event the are assigned a “0”, if they experience an event they are assigned a “1”.
## Loading required package: ggplot2## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang## Loading required package: ggpubr## Loading required package: magrittr4) Estimate the following functions of survival time for that variable and plot them:
a. Survival
## Call: survfit(formula = Surv(time = age_transition1, event = transition1) ~
## 1, data = addhealth)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 3939 3 0.999 0.000440 0.998 1.000
## 20 3936 6 0.998 0.000761 0.996 0.999
## 21 3930 6 0.996 0.000981 0.994 0.998
## 22 3924 24 0.990 0.001578 0.987 0.993
## 23 3900 33 0.982 0.002134 0.978 0.986
## 24 3867 43 0.971 0.002682 0.966 0.976
## 25 3823 10 0.968 0.002793 0.963 0.974
## 26 3799 6 0.967 0.002858 0.961 0.972
### b. Hazard
## $time
## [1] 19.5 20.5 21.5 22.5 23.5 24.5 25.5
##
## $haz
## [1] 0.001525553 0.001527884 0.006134989 0.008497541 0.011182154 0.002623578
## [7] 0.001705111
##
## $var
## [1] 3.878856e-07 3.890718e-07 1.568258e-06 2.188140e-06 2.907951e-06
## [6] 6.883184e-07 4.848200e-07
### c. Cumulative Hazard 
## time haz var
## 1 19.5 0.001525553 3.878856e-07
## 2 20.5 0.001527884 3.890718e-07
## 3 21.5 0.006134989 1.568258e-06
## 4 22.5 0.008497541 2.188140e-06
## 5 23.5 0.011182154 2.907951e-06
## 6 24.5 0.002623578 6.883184e-07
## 7 25.5 0.001705111 4.848200e-075) Carry out the following analysis
a. Kaplan-Meier survival analysis of the outcome
## Call: survfit(formula = Surv(age_transition1, transition1) ~ 1, data = addhealth)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 3939 3 0.999 0.000440 0.998 1.000
## 20 3936 6 0.998 0.000761 0.996 0.999
## 21 3930 6 0.996 0.000981 0.994 0.998
## 22 3924 24 0.990 0.001578 0.987 0.993
## 23 3900 33 0.982 0.002134 0.978 0.986
## 24 3867 43 0.971 0.002682 0.966 0.976
## 25 3823 10 0.968 0.002793 0.963 0.974
## 26 3799 6 0.967 0.002858 0.961 0.972b. Define a grouping variable, this can be dichotomous or categorical.
The grouping variabe is sexual orientation. It is categorial: heterosexual, bisexual, homosexual
c. Do you have a research hypothesis about the survival patterns for the levels of the categorical variable? State it.
Research Question: How does resilience influence the outcome of gaining United States citizenship through naturalization for LGB immigrants who reside in the United States? When considering the intersectional nature of LGB immigrant identity.
Proposed Hypothesis: Certian conditions create resilience in some LGB individuals which results in a high likelihood for specific LGB immigrants with intersectional identities to gain U.S. citizenship through naturalization.
d. Comparison of Kaplan-Meier survival across grouping variable in your data. Interpret your results. It would appear across all groups that the age at greatest number of events is at 24 years of age (43 events, 3864 at risk) The age with the lowest number of events is 19.
Citizenship transition based on sexual orienation
## Call: survfit(formula = Surv(age_transition1, transition1) ~ sexorient,
## data = addhealth)
##
## sexorient=a_straight
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 3840 3 0.999 0.000451 0.998 1.000
## 20 3837 6 0.998 0.000780 0.996 0.999
## 21 3831 6 0.996 0.001007 0.994 0.998
## 22 3825 24 0.990 0.001618 0.987 0.993
## 23 3801 31 0.982 0.002159 0.978 0.986
## 24 3770 43 0.971 0.002727 0.965 0.976
## 25 3726 10 0.968 0.002842 0.962 0.974
## 26 3703 6 0.966 0.002908 0.961 0.972
##
## sexorient=b_bisexual
## time n.risk n.event survival std.err
## 23.000 62.000 1.000 0.984 0.016
## lower 95% CI upper 95% CI
## 0.953 1.000
##
## sexorient=c_LGB
## time n.risk n.event survival std.err
## 23.0000 37.0000 1.0000 0.9730 0.0267
## lower 95% CI upper 95% CI
## 0.9221 1.0000It appears that 37 LGB individuals are at risk with one event occuring.
## observed expected o-e
## a_straight 129 127.691886 1.3081143
## b_bisexual 1 2.077664 -1.0776637
## c_LGB 1 1.230451 -0.2304506## Call: survfit(formula = Surv(time = age_transition1, event = transition1) ~
## sexorient + sex, data = addhealth)
##
## sexorient=a_straight, sex=a_male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 1704 2 0.999 0.000829 0.997 1.000
## 20 1702 1 0.998 0.001016 0.996 1.000
## 21 1701 3 0.996 0.001435 0.994 0.999
## 22 1698 10 0.991 0.002336 0.986 0.995
## 23 1688 8 0.986 0.002855 0.980 0.992
## 24 1680 14 0.978 0.003577 0.971 0.985
## 25 1666 5 0.975 0.003799 0.967 0.982
## 26 1659 5 0.972 0.004009 0.964 0.980
##
## sexorient=a_straight, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 2136 1 1.000 0.000468 0.999 1.000
## 20 2135 5 0.997 0.001145 0.995 0.999
## 21 2130 3 0.996 0.001402 0.993 0.999
## 22 2127 14 0.989 0.002233 0.985 0.994
## 23 2113 23 0.978 0.003141 0.972 0.985
## 24 2090 29 0.965 0.003983 0.957 0.973
## 25 2060 5 0.963 0.004108 0.955 0.971
## 26 2044 1 0.962 0.004133 0.954 0.970
##
## sexorient=b_bisexual, sex=a_male
## time n.risk n.event survival std.err
## 23.000 7.000 1.000 0.857 0.132
## lower 95% CI upper 95% CI
## 0.633 1.000
##
## sexorient=b_bisexual, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
##
## sexorient=c_LGB, sex=a_male
## time n.risk n.event survival std.err
## 23.0000 23.0000 1.0000 0.9565 0.0425
## lower 95% CI upper 95% CI
## 0.8767 1.0000
##
## sexorient=c_LGB, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CIe. Plot the hazard function for the analysis for each level of the group variable (continued assignment from part d)
# Part B ## Parametric models
1) Carry out the following analysis: Define your outcome as in part A. Also consider what covariates are hypothesized to affect the outcome variable. Define these and construct a parametric model for your outcome. Fit the parametric model of your choosing to the data.
The outcome is becoming a naturalized United States citizen between wave 3 and wave 4.
Hypothesis: Certian conditions create resilience in some LGB individuals which results in a high likelihood for specific LGB immigrants with intersectional identities to gain U.S. citizenship through naturalization.
a. Did you choose an AFT or PH model and why? I chose a Cox Proportional Hazard Model after looking at the results of exponential, Weibull, and piecewise exponential models. The Weibull had the lowest AIC score and fit somewhat, but the Cox model is a better fit.
b. Justify what parametric distribution you choose. The Cox Model is good for survival analysis and analysing the relationship of the covariates and survival time, or obtaining US citizenship via naturalization.
c. Carry out model fit diagnostics for the model
d. Include all main effects in the model
e. Test for an interaction between at least two of the predictors
## Loading required package: grid## Loading required package: Matrix##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchart## Stratified 1 - level Cluster Sampling design (with replacement)
## With (131) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = age_transition1, event = transition1) ~
## sex + sexorient, design = des2)
##
## n= 3939, number of events= 131
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female 0.2479 1.2813 0.3120 0.795 0.427
## sexorientb_bisexual -0.7927 0.4526 1.0598 -0.748 0.454
## sexorientc_LGB 0.9911 2.6943 1.0189 0.973 0.331
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 1.2813 0.7805 0.69520 2.362
## sexorientb_bisexual 0.4526 2.2093 0.05671 3.613
## sexorientc_LGB 2.6943 0.3712 0.36572 19.850
##
## Concordance= 0.547 (se = 0.035 )
## Likelihood ratio test= NA on 3 df, p=NA
## Wald test = 2.13 on 3 df, p=0.5
## Score (logrank) test = NA on 3 df, p=NA
###model with demographic variables: sex, race/ethnicity, sexual orientation
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (131) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = age_transition1, event = transition1) ~
## sex + racethnic + sexorient, design = des2)
##
## n= 3939, number of events= 131
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female 0.33663 1.40022 0.27919 1.206 0.2279
## racethnicb-nhblack -1.16020 0.31342 0.76873 -1.509 0.1312
## racethnicc-hispanic 3.19780 24.47862 0.40446 7.906 2.65e-15 ***
## racethnicd-asian 4.36620 78.74394 0.40474 10.788 < 2e-16 ***
## racethnice-native_american 2.44679 11.55126 1.21614 2.012 0.0442 *
## racethnicf-other 3.04037 20.91297 0.71533 4.250 2.13e-05 ***
## sexorientb_bisexual 0.03357 1.03414 1.08482 0.031 0.9753
## sexorientc_LGB 0.39040 1.47757 0.85949 0.454 0.6497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 1.4002 0.71417 0.81012 2.420
## racethnicb-nhblack 0.3134 3.19058 0.06947 1.414
## racethnicc-hispanic 24.4786 0.04085 11.07928 54.083
## racethnicd-asian 78.7439 0.01270 35.62089 174.072
## racethnice-native_american 11.5513 0.08657 1.06524 125.260
## racethnicf-other 20.9130 0.04782 5.14666 84.978
## sexorientb_bisexual 1.0341 0.96699 0.12336 8.669
## sexorientc_LGB 1.4776 0.67679 0.27413 7.964
##
## Concordance= 0.89 (se = 0.023 )
## Likelihood ratio test= NA on 8 df, p=NA
## Wald test = 183.9 on 8 df, p=<2e-16
## Score (logrank) test = NA on 8 df, p=NAmodel with demographic variables (sex, race/ethnicity, sexual orientation) plus demographic variables: education, marriage, income
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (131) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = age_transition1, event = transition1) ~
## sex + racethnic + sexorient + educ + +marriedW4a + incomeW4,
## design = des2)
##
## n= 3939, number of events= 131
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female 0.31923 1.37607 0.26607 1.200 0.230
## racethnicb-nhblack -0.98884 0.37201 0.74458 -1.328 0.184
## racethnicc-hispanic 3.33446 28.06331 0.40172 8.300 < 2e-16 ***
## racethnicd-asian 4.33261 76.14248 0.39883 10.863 < 2e-16 ***
## racethnice-native_american 2.63428 13.93322 1.19866 2.198 0.028 *
## racethnicf-other 3.06283 21.38809 0.69933 4.380 1.19e-05 ***
## sexorientb_bisexual 0.39289 1.48125 1.08240 0.363 0.717
## sexorientc_LGB 0.65317 1.92162 0.87766 0.744 0.457
## educb_highschool_grad -0.06876 0.93355 0.67821 -0.101 0.919
## educc_college_bach 0.38075 1.46338 0.72257 0.527 0.598
## educd_college+ 0.58944 1.80299 0.69851 0.844 0.399
## marriedW4ab_married -0.41631 0.65947 0.31353 -1.328 0.184
## incomeW4b_$25,000>$50,000 -0.77411 0.46112 0.53796 -1.439 0.150
## incomeW4c_$50,000>$100,000 -0.21178 0.80914 0.45443 -0.466 0.641
## incomeW4e_$100,000+ 0.08549 1.08925 0.42406 0.202 0.840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 1.3761 0.72671 0.81689 2.318
## racethnicb-nhblack 0.3720 2.68810 0.08645 1.601
## racethnicc-hispanic 28.0633 0.03563 12.77012 61.671
## racethnicd-asian 76.1425 0.01313 34.84497 166.385
## racethnice-native_american 13.9332 0.07177 1.32969 146.000
## racethnicf-other 21.3881 0.04675 5.43126 84.226
## sexorientb_bisexual 1.4812 0.67511 0.17754 12.359
## sexorientc_LGB 1.9216 0.52039 0.34403 10.733
## educb_highschool_grad 0.9335 1.07118 0.24708 3.527
## educc_college_bach 1.4634 0.68335 0.35506 6.031
## educd_college+ 1.8030 0.55464 0.45859 7.089
## marriedW4ab_married 0.6595 1.51636 0.35671 1.219
## incomeW4b_$25,000>$50,000 0.4611 2.16865 0.16066 1.323
## incomeW4c_$50,000>$100,000 0.8091 1.23587 0.33206 1.972
## incomeW4e_$100,000+ 1.0893 0.91806 0.47443 2.501
##
## Concordance= 0.899 (se = 0.023 )
## Likelihood ratio test= NA on 15 df, p=NA
## Wald test = 266.7 on 15 df, p=<2e-16
## Score (logrank) test = NA on 15 df, p=NAschoenfeld residuals
## Response sexb_female :
##
## Call:
## lm(formula = sexb_female ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6664 -0.6108 0.3655 0.3827 0.4514
##
## Coefficients:
## Estimate
## (Intercept) -0.34984
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01719
## Std. Error
## (Intercept) 0.67193
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02901
## t value
## (Intercept) -0.521
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.593
## Pr(>|t|)
## (Intercept) 0.604
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.554
##
## Residual standard error: 0.49 on 129 degrees of freedom
## Multiple R-squared: 0.002716, Adjusted R-squared: -0.005015
## F-statistic: 0.3513 on 1 and 129 DF, p-value: 0.5544
##
##
## Response racethnicb-nhblack :
##
## Call:
## lm(formula = `racethnicb-nhblack` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15458 -0.07781 -0.05414 -0.03099 1.01573
##
## Coefficients:
## Estimate
## (Intercept) 0.59141
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.02371
## Std. Error
## (Intercept) 0.30704
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01326
## t value
## (Intercept) 1.926
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.788
## Pr(>|t|)
## (Intercept) 0.0563 .
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.0761 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2239 on 129 degrees of freedom
## Multiple R-squared: 0.02419, Adjusted R-squared: 0.01663
## F-statistic: 3.198 on 1 and 129 DF, p-value: 0.07608
##
##
## Response racethnicc-hispanic :
##
## Call:
## lm(formula = `racethnicc-hispanic` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32832 -0.23532 -0.19973 -0.09828 0.90483
##
## Coefficients:
## Estimate
## (Intercept) -0.88835
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.03559
## Std. Error
## (Intercept) 0.55488
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02396
## t value
## (Intercept) -1.601
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.486
## Pr(>|t|)
## (Intercept) 0.112
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.140
##
## Residual standard error: 0.4046 on 129 degrees of freedom
## Multiple R-squared: 0.01683, Adjusted R-squared: 0.009204
## F-statistic: 2.208 on 1 and 129 DF, p-value: 0.1398
##
##
## Response racethnicd-asian :
##
## Call:
## lm(formula = `racethnicd-asian` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5826 -0.5061 0.4211 0.4957 0.6123
##
## Coefficients:
## Estimate
## (Intercept) 0.33799
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01529
## Std. Error
## (Intercept) 0.69649
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.03007
## t value
## (Intercept) 0.485
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.508
## Pr(>|t|)
## (Intercept) 0.628
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.612
##
## Residual standard error: 0.5079 on 129 degrees of freedom
## Multiple R-squared: 0.001999, Adjusted R-squared: -0.005737
## F-statistic: 0.2584 on 1 and 129 DF, p-value: 0.6121
##
##
## Response racethnice-native_american :
##
## Call:
## lm(formula = `racethnice-native_american` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01876 -0.01050 -0.00733 -0.00417 0.98994
##
## Coefficients:
## Estimate
## (Intercept) -0.073089
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.003165
## Std. Error
## (Intercept) 0.120149
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.005187
## t value
## (Intercept) -0.608
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.610
## Pr(>|t|)
## (Intercept) 0.544
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.543
##
## Residual standard error: 0.08762 on 129 degrees of freedom
## Multiple R-squared: 0.002878, Adjusted R-squared: -0.004852
## F-statistic: 0.3723 on 1 and 129 DF, p-value: 0.5428
##
##
## Response racethnicf-other :
##
## Call:
## lm(formula = `racethnicf-other` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04850 -0.04008 -0.03797 -0.03596 0.96319
##
## Coefficients:
## Estimate
## (Intercept) 0.058222
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.002104
## Std. Error
## (Intercept) 0.264522
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.011421
## t value
## (Intercept) 0.220
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.184
## Pr(>|t|)
## (Intercept) 0.826
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.854
##
## Residual standard error: 0.1929 on 129 degrees of freedom
## Multiple R-squared: 0.0002631, Adjusted R-squared: -0.007487
## F-statistic: 0.03396 on 1 and 129 DF, p-value: 0.8541
##
##
## Response sexorientb_bisexual :
##
## Call:
## lm(formula = sexorientb_bisexual ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01876 -0.01050 -0.00733 -0.00417 0.98994
##
## Coefficients:
## Estimate
## (Intercept) -0.073089
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.003165
## Std. Error
## (Intercept) 0.120149
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.005187
## t value
## (Intercept) -0.608
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.610
## Pr(>|t|)
## (Intercept) 0.544
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.543
##
## Residual standard error: 0.08762 on 129 degrees of freedom
## Multiple R-squared: 0.002878, Adjusted R-squared: -0.004852
## F-statistic: 0.3723 on 1 and 129 DF, p-value: 0.5428
##
##
## Response sexorientc_LGB :
##
## Call:
## lm(formula = sexorientc_LGB ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.02955 -0.01273 -0.00713 -0.00126 0.98649
##
## Coefficients:
## Estimate
## (Intercept) 0.082356
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.004204
## Std. Error
## (Intercept) 0.120122
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.005186
## t value
## (Intercept) 0.686
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.811
## Pr(>|t|)
## (Intercept) 0.494
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.419
##
## Residual standard error: 0.0876 on 129 degrees of freedom
## Multiple R-squared: 0.005067, Adjusted R-squared: -0.002645
## F-statistic: 0.657 on 1 and 129 DF, p-value: 0.4191
##
##
## Response educb_highschool_grad :
##
## Call:
## lm(formula = educb_highschool_grad ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5462 -0.4843 -0.4361 0.5157 0.5756
##
## Coefficients:
## Estimate
## (Intercept) -0.22913
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01175
## Std. Error
## (Intercept) 0.68824
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02971
## t value
## (Intercept) -0.333
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.396
## Pr(>|t|)
## (Intercept) 0.740
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.693
##
## Residual standard error: 0.5019 on 129 degrees of freedom
## Multiple R-squared: 0.001212, Adjusted R-squared: -0.006531
## F-statistic: 0.1565 on 1 and 129 DF, p-value: 0.6931
##
##
## Response educc_college_bach :
##
## Call:
## lm(formula = educc_college_bach ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3254 -0.3145 -0.3043 0.6820 0.7390
##
## Coefficients:
## Estimate
## (Intercept) -0.089969
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.003142
## Std. Error
## (Intercept) 0.633784
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.027364
## t value
## (Intercept) -0.142
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.115
## Pr(>|t|)
## (Intercept) 0.887
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.909
##
## Residual standard error: 0.4622 on 129 degrees of freedom
## Multiple R-squared: 0.0001022, Adjusted R-squared: -0.007649
## F-statistic: 0.01319 on 1 and 129 DF, p-value: 0.9088
##
##
## Response educd_college+ :
##
## Call:
## lm(formula = `educd_college+` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2367 -0.1933 -0.1772 -0.1605 0.8501
##
## Coefficients:
## Estimate
## (Intercept) 0.25514
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01065
## Std. Error
## (Intercept) 0.53425
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02307
## t value
## (Intercept) 0.478
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.462
## Pr(>|t|)
## (Intercept) 0.634
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.645
##
## Residual standard error: 0.3896 on 129 degrees of freedom
## Multiple R-squared: 0.00165, Adjusted R-squared: -0.006089
## F-statistic: 0.2132 on 1 and 129 DF, p-value: 0.645
##
##
## Response marriedW4ab_married :
##
## Call:
## lm(formula = marriedW4ab_married ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5347 -0.4314 -0.4007 0.5783 0.6090
##
## Coefficients:
## Estimate
## (Intercept) 0.261341
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.009729
## Std. Error
## (Intercept) 0.689343
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.029762
## t value
## (Intercept) 0.379
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.327
## Pr(>|t|)
## (Intercept) 0.705
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.744
##
## Residual standard error: 0.5027 on 129 degrees of freedom
## Multiple R-squared: 0.0008276, Adjusted R-squared: -0.006918
## F-statistic: 0.1069 on 1 and 129 DF, p-value: 0.7443
##
##
## Response incomeW4b_$25,000>$50,000 :
##
## Call:
## lm(formula = `incomeW4b_$25,000>$50,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25721 -0.14650 -0.11392 -0.08575 0.94365
##
## Coefficients:
## Estimate
## (Intercept) 0.68011
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.02990
## Std. Error
## (Intercept) 0.45915
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01982
## t value
## (Intercept) 1.481
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.508
## Pr(>|t|)
## (Intercept) 0.141
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.134
##
## Residual standard error: 0.3348 on 129 degrees of freedom
## Multiple R-squared: 0.01733, Adjusted R-squared: 0.009708
## F-statistic: 2.274 on 1 and 129 DF, p-value: 0.134
##
##
## Response incomeW4c_$50,000>$100,000 :
##
## Call:
## lm(formula = `incomeW4c_$50,000>$100,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5886 -0.4423 -0.3155 0.5542 0.7308
##
## Coefficients:
## Estimate
## (Intercept) -1.08542
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.04636
## Std. Error
## (Intercept) 0.67492
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02914
## t value
## (Intercept) -1.608
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.591
## Pr(>|t|)
## (Intercept) 0.110
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.114
##
## Residual standard error: 0.4922 on 129 degrees of freedom
## Multiple R-squared: 0.01924, Adjusted R-squared: 0.01164
## F-statistic: 2.531 on 1 and 129 DF, p-value: 0.1141
##
##
## Response incomeW4e_$100,000+ :
##
## Call:
## lm(formula = `incomeW4e_$100,000+` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5055 -0.3988 -0.3434 0.5948 0.6985
##
## Coefficients:
## Estimate
## (Intercept) 0.81651
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.03242
## Std. Error
## (Intercept) 0.66887
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02888
## t value
## (Intercept) 1.221
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.123
## Pr(>|t|)
## (Intercept) 0.224
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.264
##
## Residual standard error: 0.4878 on 129 degrees of freedom
## Multiple R-squared: 0.009675, Adjusted R-squared: 0.001998
## F-statistic: 1.26 on 1 and 129 DF, p-value: 0.2637
##
##
## Response lang_used.mostb_spanish :
##
## Call:
## lm(formula = lang_used.mostb_spanish ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3763 -0.3260 -0.3010 0.6692 0.7534
##
## Coefficients:
## Estimate
## (Intercept) -0.36862
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01589
## Std. Error
## (Intercept) 0.64044
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02765
## t value
## (Intercept) -0.576
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.575
## Pr(>|t|)
## (Intercept) 0.566
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.567
##
## Residual standard error: 0.467 on 129 degrees of freedom
## Multiple R-squared: 0.002554, Adjusted R-squared: -0.005178
## F-statistic: 0.3303 on 1 and 129 DF, p-value: 0.5665
##
##
## Response lang_used.mostc_other :
##
## Call:
## lm(formula = lang_used.mostc_other ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4296 -0.3151 -0.2668 0.6566 0.8520
##
## Coefficients:
## Estimate
## (Intercept) 0.60397
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.02863
## Std. Error
## (Intercept) 0.63342
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02735
## t value
## (Intercept) 0.954
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.047
## Pr(>|t|)
## (Intercept) 0.342
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.297
##
## Residual standard error: 0.4619 on 129 degrees of freedom
## Multiple R-squared: 0.008426, Adjusted R-squared: 0.0007397
## F-statistic: 1.096 on 1 and 129 DF, p-value: 0.2971
##
##
## Response parents_careb_very_much :
##
## Call:
## lm(formula = parents_careb_very_much ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9222 0.1275 0.1950 0.2247 0.3058
##
## Coefficients:
## Estimate
## (Intercept) 0.71925
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.02974
## Std. Error
## (Intercept) 0.55980
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02417
## t value
## (Intercept) 1.285
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.230
## Pr(>|t|)
## (Intercept) 0.201
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.221
##
## Residual standard error: 0.4082 on 129 degrees of freedom
## Multiple R-squared: 0.0116, Adjusted R-squared: 0.003937
## F-statistic: 1.514 on 1 and 129 DF, p-value: 0.2208
##
##
## Response verbalabuse_bycaretakerW4c_no :
##
## Call:
## lm(formula = verbalabuse_bycaretakerW4c_no ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5678 -0.4476 -0.4069 0.5524 0.6186
##
## Coefficients:
## Estimate
## (Intercept) 0.48472
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01779
## Std. Error
## (Intercept) 0.68756
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02969
## t value
## (Intercept) 0.705
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.599
## Pr(>|t|)
## (Intercept) 0.482
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.550
##
## Residual standard error: 0.5014 on 129 degrees of freedom
## Multiple R-squared: 0.002777, Adjusted R-squared: -0.004954
## F-statistic: 0.3592 on 1 and 129 DF, p-value: 0.55
##
##
## Response friendshipsb_1to5 :
##
## Call:
## lm(formula = friendshipsb_1to5 ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7410 -0.5492 0.3376 0.4278 0.5457
##
## Coefficients:
## Estimate
## (Intercept) 0.93454
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.03931
## Std. Error
## (Intercept) 0.67590
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02918
## t value
## (Intercept) 1.383
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.347
## Pr(>|t|)
## (Intercept) 0.169
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.180
##
## Residual standard error: 0.4929 on 129 degrees of freedom
## Multiple R-squared: 0.01387, Adjusted R-squared: 0.006225
## F-statistic: 1.814 on 1 and 129 DF, p-value: 0.1804
##
##
## Response friendshipsc_6+ :
##
## Call:
## lm(formula = `friendshipsc_6+` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4836 -0.3900 -0.3447 0.5798 0.7349
##
## Coefficients:
## Estimate
## (Intercept) -0.72882
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.03121
## Std. Error
## (Intercept) 0.67142
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02899
## t value
## (Intercept) -1.085
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.077
## Pr(>|t|)
## (Intercept) 0.280
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.284
##
## Residual standard error: 0.4896 on 129 degrees of freedom
## Multiple R-squared: 0.008906, Adjusted R-squared: 0.001224
## F-statistic: 1.159 on 1 and 129 DF, p-value: 0.2836
##
##
## Response mentorb_yes :
##
## Call:
## lm(formula = mentorb_yes ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6613 -0.6383 0.3455 0.3587 0.4041
##
## Coefficients:
## Estimate
## (Intercept) -0.059539
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.003376
## Std. Error
## (Intercept) 0.661778
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.028572
## t value
## (Intercept) -0.090
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.118
## Pr(>|t|)
## (Intercept) 0.928
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.906
##
## Residual standard error: 0.4826 on 129 degrees of freedom
## Multiple R-squared: 0.0001082, Adjusted R-squared: -0.007643
## F-statistic: 0.01396 on 1 and 129 DF, p-value: 0.9061
##
##
## Response insurance_statusb_yes_insurance :
##
## Call:
## lm(formula = insurance_statusb_yes_insurance ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8414 0.1692 0.1803 0.1897 0.2096
##
## Coefficients:
## Estimate
## (Intercept) -0.134939
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.005861
## Std. Error
## (Intercept) 0.534668
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.023084
## t value
## (Intercept) -0.252
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.254
## Pr(>|t|)
## (Intercept) 0.801
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.800
##
## Residual standard error: 0.3899 on 129 degrees of freedom
## Multiple R-squared: 0.0004994, Adjusted R-squared: -0.007249
## F-statistic: 0.06445 on 1 and 129 DF, p-value: 0.8
##
##
## Response general_healthb_fair :
##
## Call:
## lm(formula = general_healthb_fair ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3964 -0.3704 -0.3578 0.6297 0.6528
##
## Coefficients:
## Estimate
## (Intercept) -0.161524
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.005304
## Std. Error
## (Intercept) 0.666681
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.028784
## t value
## (Intercept) -0.242
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.184
## Pr(>|t|)
## (Intercept) 0.809
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.854
##
## Residual standard error: 0.4862 on 129 degrees of freedom
## Multiple R-squared: 0.0002631, Adjusted R-squared: -0.007487
## F-statistic: 0.03395 on 1 and 129 DF, p-value: 0.8541
##
##
## Response general_healthc_excellent :
##
## Call:
## lm(formula = general_healthc_excellent ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6347 -0.6157 0.3704 0.3843 0.4168
##
## Coefficients:
## Estimate
## (Intercept) 0.158726
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.004743
## Std. Error
## (Intercept) 0.671859
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.029007
## t value
## (Intercept) 0.236
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.164
## Pr(>|t|)
## (Intercept) 0.814
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.870
##
## Residual standard error: 0.4899 on 129 degrees of freedom
## Multiple R-squared: 0.0002072, Adjusted R-squared: -0.007543
## F-statistic: 0.02674 on 1 and 129 DF, p-value: 0.8704
##
##
## Response depressionW4b_yes :
##
## Call:
## lm(formula = depressionW4b_yes ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11049 -0.08213 -0.07461 -0.06709 0.94494
##
## Coefficients:
## Estimate
## (Intercept) 0.206851
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.007517
## Std. Error
## (Intercept) 0.366270
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.015814
## t value
## (Intercept) 0.565
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.475
## Pr(>|t|)
## (Intercept) 0.573
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.635
##
## Residual standard error: 0.2671 on 129 degrees of freedom
## Multiple R-squared: 0.001748, Adjusted R-squared: -0.00599
## F-statistic: 0.2259 on 1 and 129 DF, p-value: 0.6354
##
##
## Response able.change.lifeb_neutral :
##
## Call:
## lm(formula = able.change.lifeb_neutral ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20910 -0.10988 -0.07636 -0.05974 0.96545
##
## Coefficients:
## Estimate
## (Intercept) 0.59856
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.02519
## Std. Error
## (Intercept) 0.39353
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01699
## t value
## (Intercept) 1.521
## des2$variables$age_transition1[des2$variables$transition1 == 1] -1.483
## Pr(>|t|)
## (Intercept) 0.131
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.141
##
## Residual standard error: 0.287 on 129 degrees of freedom
## Multiple R-squared: 0.01675, Adjusted R-squared: 0.009132
## F-statistic: 2.198 on 1 and 129 DF, p-value: 0.1406
##
##
## Response able.change.lifec_agree :
##
## Call:
## lm(formula = able.change.lifec_agree ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9381 0.0865 0.1141 0.1530 0.2782
##
## Coefficients:
## Estimate
## (Intercept) -0.81092
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.03470
## Std. Error
## (Intercept) 0.46812
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02021
## t value
## (Intercept) -1.732
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.717
## Pr(>|t|)
## (Intercept) 0.0856 .
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.0884 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3414 on 129 degrees of freedom
## Multiple R-squared: 0.02235, Adjusted R-squared: 0.01477
## F-statistic: 2.948 on 1 and 129 DF, p-value: 0.08836
##
##
## Response many.solutions.probb_neutral :
##
## Call:
## lm(formula = many.solutions.probb_neutral ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2209 -0.1405 -0.1228 -0.1061 0.8933
##
## Coefficients:
## Estimate
## (Intercept) 0.41799
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01649
## Std. Error
## (Intercept) 0.46542
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02009
## t value
## (Intercept) 0.898
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.821
## Pr(>|t|)
## (Intercept) 0.371
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.413
##
## Residual standard error: 0.3394 on 129 degrees of freedom
## Multiple R-squared: 0.005192, Adjusted R-squared: -0.00252
## F-statistic: 0.6733 on 1 and 129 DF, p-value: 0.4134
##
##
## Response many.solutions.probc_disagree :
##
## Call:
## lm(formula = many.solutions.probc_disagree ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07990 -0.04401 -0.03502 -0.02576 0.97500
##
## Coefficients:
## Estimate
## (Intercept) 0.193417
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.008994
## Std. Error
## (Intercept) 0.264001
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.011398
## t value
## (Intercept) 0.733
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.789
## Pr(>|t|)
## (Intercept) 0.465
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.432
##
## Residual standard error: 0.1925 on 129 degrees of freedom
## Multiple R-squared: 0.004803, Adjusted R-squared: -0.002911
## F-statistic: 0.6226 on 1 and 129 DF, p-value: 0.4315
##
##
## Response risk.behaviorb_dislikes_to.party :
##
## Call:
## lm(formula = risk.behaviorb_dislikes_to.party ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7970 -0.6262 0.2778 0.3191 0.4325
##
## Coefficients:
## Estimate
## (Intercept) -0.88433
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.03736
## Std. Error
## (Intercept) 0.63629
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02747
## t value
## (Intercept) -1.39
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.36
## Pr(>|t|)
## (Intercept) 0.167
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.176
##
## Residual standard error: 0.464 on 129 degrees of freedom
## Multiple R-squared: 0.01413, Adjusted R-squared: 0.006492
## F-statistic: 1.85 on 1 and 129 DF, p-value: 0.1762Grambsch and Therneau test
## rho chisq p
## sexb_female -0.01240 0.1011 7.51e-01
## racethnicb-nhblack 0.07889 23.0492 1.58e-06
## racethnicc-hispanic -0.09396 11.2602 7.92e-04
## racethnicd-asian 0.13420 76.1593 2.62e-18
## racethnice-native_american 0.08989 5.2790 2.16e-02
## racethnicf-other 0.10034 36.8405 1.28e-09
## sexorientb_bisexual 0.01283 0.0862 7.69e-01
## sexorientc_LGB 0.00807 0.0534 8.17e-01
## educb_highschool_grad -0.02420 0.8550 3.55e-01
## educc_college_bach -0.03843 2.3926 1.22e-01
## educd_college+ 0.02777 1.1244 2.89e-01
## marriedW4ab_married -0.26708 82.8837 8.70e-20
## incomeW4b_$25,000>$50,000 0.07334 5.7825 1.62e-02
## incomeW4c_$50,000>$100,000 0.02226 0.2073 6.49e-01
## incomeW4e_$100,000+ 0.09603 27.5960 1.49e-07
## lang_used.mostb_spanish 0.20744 42.2937 7.85e-11
## lang_used.mostc_other -0.09789 49.2779 2.22e-12
## parents_careb_very_much -0.12363 16.3177 5.36e-05
## verbalabuse_bycaretakerW4c_no -0.06757 5.7374 1.66e-02
## friendshipsb_1to5 -0.03390 1.3371 2.48e-01
## friendshipsc_6+ -0.06444 5.3454 2.08e-02
## mentorb_yes -0.11131 30.4529 3.42e-08
## insurance_statusb_yes_insurance -0.09799 14.1763 1.66e-04
## general_healthb_fair 0.15108 18.4418 1.75e-05
## general_healthc_excellent 0.08861 6.3384 1.18e-02
## depressionW4b_yes 0.03472 0.9131 3.39e-01
## able.change.lifeb_neutral 0.12995 23.2065 1.46e-06
## able.change.lifec_agree 0.06087 2.3488 1.25e-01
## many.solutions.probb_neutral -0.22501 15.6103 7.78e-05
## many.solutions.probc_disagree 0.20334 50.8978 9.73e-13
## risk.behaviorb_dislikes_to.party 0.09320 4.9993 2.54e-02
## GLOBAL NA 195.0219 1.09e-25Plot of residuals
###Martingale residuals
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## FALSE, : There are other near singularities as well. 4.0401
stratify by sexual orientation
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (131) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = age_transition1, event = transition1) ~
## strata(sexorient) + sex + racethnic + educ + +marriedW4a +
## incomeW4 + friendships + mentor + insurance_status +
## general_health + depressionW4 + able.change.life, design = des2)
##
## n= 3939, number of events= 131
##
## coef exp(coef) se(coef) z
## sexb_female 0.44471 1.56004 0.27802 1.600
## racethnicb-nhblack -1.04336 0.35227 0.74215 -1.406
## racethnicc-hispanic 3.22762 25.21957 0.37225 8.671
## racethnicd-asian 4.35106 77.56047 0.38400 11.331
## racethnice-native_american 2.67860 14.56470 1.15815 2.313
## racethnicf-other 3.09735 22.13911 0.68852 4.499
## educb_highschool_grad -0.10257 0.90252 0.62868 -0.163
## educc_college_bach 0.26300 1.30082 0.63827 0.412
## educd_college+ 0.55497 1.74189 0.63697 0.871
## marriedW4ab_married -0.35715 0.69967 0.36475 -0.979
## incomeW4b_$25,000>$50,000 -0.48461 0.61594 0.53914 -0.899
## incomeW4c_$50,000>$100,000 0.17892 1.19593 0.41496 0.431
## incomeW4e_$100,000+ 0.46643 1.59429 0.45851 1.017
## friendshipsb_1to5 -1.19362 0.30312 0.58326 -2.046
## friendshipsc_6+ -1.11151 0.32906 0.57755 -1.925
## mentorb_yes -0.56305 0.56947 0.29116 -1.934
## insurance_statusb_yes_insurance -0.28175 0.75447 0.37997 -0.741
## general_healthb_fair -1.46459 0.23117 0.97545 -1.501
## general_healthc_excellent -1.09726 0.33378 0.96007 -1.143
## depressionW4b_yes -1.08978 0.33629 0.61730 -1.765
## able.change.lifeb_neutral -0.09545 0.90896 0.80111 -0.119
## able.change.lifec_agree 0.46411 1.59060 0.63596 0.730
## Pr(>|z|)
## sexb_female 0.1097
## racethnicb-nhblack 0.1598
## racethnicc-hispanic < 2e-16 ***
## racethnicd-asian < 2e-16 ***
## racethnice-native_american 0.0207 *
## racethnicf-other 6.84e-06 ***
## educb_highschool_grad 0.8704
## educc_college_bach 0.6803
## educd_college+ 0.3836
## marriedW4ab_married 0.3275
## incomeW4b_$25,000>$50,000 0.3687
## incomeW4c_$50,000>$100,000 0.6663
## incomeW4e_$100,000+ 0.3090
## friendshipsb_1to5 0.0407 *
## friendshipsc_6+ 0.0543 .
## mentorb_yes 0.0531 .
## insurance_statusb_yes_insurance 0.4584
## general_healthb_fair 0.1332
## general_healthc_excellent 0.2531
## depressionW4b_yes 0.0775 .
## able.change.lifeb_neutral 0.9052
## able.change.lifec_agree 0.4655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 1.5600 0.64101 0.90465 2.6902
## racethnicb-nhblack 0.3523 2.83875 0.08225 1.5087
## racethnicc-hispanic 25.2196 0.03965 12.15850 52.3113
## racethnicd-asian 77.5605 0.01289 36.54120 164.6259
## racethnice-native_american 14.5647 0.06866 1.50482 140.9670
## racethnicf-other 22.1391 0.04517 5.74240 85.3546
## educb_highschool_grad 0.9025 1.10801 0.26322 3.0945
## educc_college_bach 1.3008 0.76874 0.37232 4.5448
## educd_college+ 1.7419 0.57409 0.49985 6.0703
## marriedW4ab_married 0.6997 1.42924 0.34231 1.4301
## incomeW4b_$25,000>$50,000 0.6159 1.62355 0.21410 1.7719
## incomeW4c_$50,000>$100,000 1.1959 0.83617 0.53027 2.6972
## incomeW4e_$100,000+ 1.5943 0.62724 0.64906 3.9160
## friendshipsb_1to5 0.3031 3.29900 0.09664 0.9508
## friendshipsc_6+ 0.3291 3.03894 0.10609 1.0207
## mentorb_yes 0.5695 1.75602 0.32183 1.0077
## insurance_statusb_yes_insurance 0.7545 1.32544 0.35827 1.5888
## general_healthb_fair 0.2312 4.32577 0.03417 1.5640
## general_healthc_excellent 0.3338 2.99596 0.05085 2.1912
## depressionW4b_yes 0.3363 2.97361 0.10029 1.1276
## able.change.lifeb_neutral 0.9090 1.10016 0.18908 4.3697
## able.change.lifec_agree 1.5906 0.62869 0.45733 5.5321
##
## Concordance= 0.924 (se = 0.015 )
## Likelihood ratio test= NA on 22 df, p=NA
## Wald test = 548.9 on 22 df, p=<2e-16
## Score (logrank) test = NA on 22 df, p=NAstratify by sex
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (131) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = age_transition1, event = transition1) ~
## strata(sex) + racethnic + sexorient + educ + +marriedW4a +
## incomeW4 + friendships + mentor + insurance_status +
## general_health + depressionW4 + able.change.life, design = des2)
##
## n= 3939, number of events= 131
##
## coef exp(coef) se(coef) z
## racethnicb-nhblack -1.06055 0.34627 0.74234 -1.429
## racethnicc-hispanic 3.21702 24.95354 0.36917 8.714
## racethnicd-asian 4.35801 78.10122 0.38412 11.345
## racethnice-native_american 2.69932 14.86964 1.23150 2.192
## racethnicf-other 3.08702 21.91171 0.68358 4.516
## sexorientb_bisexual 0.29946 1.34913 1.10776 0.270
## sexorientc_LGB 1.09772 2.99733 0.77827 1.410
## educb_highschool_grad -0.08264 0.92068 0.62531 -0.132
## educc_college_bach 0.28450 1.32910 0.63606 0.447
## educd_college+ 0.55204 1.73679 0.63925 0.864
## marriedW4ab_married -0.35416 0.70176 0.36573 -0.968
## incomeW4b_$25,000>$50,000 -0.54227 0.58143 0.54922 -0.987
## incomeW4c_$50,000>$100,000 0.12958 1.13835 0.42374 0.306
## incomeW4e_$100,000+ 0.42064 1.52293 0.46608 0.903
## friendshipsb_1to5 -1.18946 0.30439 0.57494 -2.069
## friendshipsc_6+ -1.12250 0.32547 0.57296 -1.959
## mentorb_yes -0.58053 0.55960 0.29307 -1.981
## insurance_statusb_yes_insurance -0.27868 0.75678 0.37731 -0.739
## general_healthb_fair -1.46284 0.23158 0.96777 -1.512
## general_healthc_excellent -1.10204 0.33219 0.95425 -1.155
## depressionW4b_yes -1.11825 0.32685 0.61842 -1.808
## able.change.lifeb_neutral -0.08619 0.91742 0.80678 -0.107
## able.change.lifec_agree 0.46968 1.59948 0.64086 0.733
## Pr(>|z|)
## racethnicb-nhblack 0.1531
## racethnicc-hispanic < 2e-16 ***
## racethnicd-asian < 2e-16 ***
## racethnice-native_american 0.0284 *
## racethnicf-other 6.3e-06 ***
## sexorientb_bisexual 0.7869
## sexorientc_LGB 0.1584
## educb_highschool_grad 0.8949
## educc_college_bach 0.6547
## educd_college+ 0.3878
## marriedW4ab_married 0.3329
## incomeW4b_$25,000>$50,000 0.3235
## incomeW4c_$50,000>$100,000 0.7598
## incomeW4e_$100,000+ 0.3668
## friendshipsb_1to5 0.0386 *
## friendshipsc_6+ 0.0501 .
## mentorb_yes 0.0476 *
## insurance_statusb_yes_insurance 0.4601
## general_healthb_fair 0.1306
## general_healthc_excellent 0.2481
## depressionW4b_yes 0.0706 .
## able.change.lifeb_neutral 0.9149
## able.change.lifec_agree 0.4636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## racethnicb-nhblack 0.3463 2.88795 0.08082 1.4835
## racethnicc-hispanic 24.9535 0.04007 12.10302 51.4482
## racethnicd-asian 78.1012 0.01280 36.78668 165.8154
## racethnice-native_american 14.8696 0.06725 1.33060 166.1703
## racethnicf-other 21.9117 0.04564 5.73871 83.6640
## sexorientb_bisexual 1.3491 0.74122 0.15386 11.8299
## sexorientc_LGB 2.9973 0.33363 0.65204 13.7783
## educb_highschool_grad 0.9207 1.08615 0.27030 3.1360
## educc_college_bach 1.3291 0.75239 0.38207 4.6236
## educd_college+ 1.7368 0.57577 0.49616 6.0796
## marriedW4ab_married 0.7018 1.42498 0.34267 1.4372
## incomeW4b_$25,000>$50,000 0.5814 1.71991 0.19815 1.7061
## incomeW4c_$50,000>$100,000 1.1384 0.87846 0.49612 2.6120
## incomeW4e_$100,000+ 1.5229 0.65663 0.61088 3.7967
## friendshipsb_1to5 0.3044 3.28531 0.09864 0.9393
## friendshipsc_6+ 0.3255 3.07251 0.10588 1.0005
## mentorb_yes 0.5596 1.78699 0.31508 0.9939
## insurance_statusb_yes_insurance 0.7568 1.32139 0.36125 1.5854
## general_healthb_fair 0.2316 4.31821 0.03475 1.5434
## general_healthc_excellent 0.3322 3.01030 0.05118 2.1560
## depressionW4b_yes 0.3269 3.05948 0.09726 1.0984
## able.change.lifeb_neutral 0.9174 1.09001 0.18873 4.4597
## able.change.lifec_agree 1.5995 0.62520 0.45549 5.6167
##
## Concordance= 0.925 (se = 0.015 )
## Likelihood ratio test= NA on 23 df, p=NA
## Wald test = 585.4 on 23 df, p=<2e-16
## Score (logrank) test = NA on 23 df, p=NA