DEM 7223 Homework 1
Steven Weeks
10/15/2019
## Installing package into '/storage01/users/izf381/R/x86_64-redhat-linux-gnu-library/3.6'
## (as 'lib' is unspecified)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
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## recodeDefinitions
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 becomeing 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”.
##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numericDescriptive estimates
## 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 7945 6 0.999 0.000308 0.999 1.000
## 20 7939 16 0.997 0.000590 0.996 0.998
## 21 7923 29 0.994 0.000896 0.992 0.995
## 22 7894 62 0.986 0.001328 0.983 0.988
## 23 7832 92 0.974 0.001779 0.971 0.978
## 24 7740 89 0.963 0.002118 0.959 0.967
## 25 7650 20 0.960 0.002186 0.956 0.965
## 26 7597 8 0.959 0.002213 0.955 0.964
#### b. Hazard
## $time
## [1] 19.5 20.5 21.5 22.5 23.5 24.5 25.5
##
## $haz
## [1] 0.002017401 0.003666945 0.007885072 0.011816218 0.011565517 0.002621860
## [7] 0.001129844
##
## $var
## [1] 2.543692e-07 4.636724e-07 1.002817e-06 1.517659e-06 1.502952e-06
## [6] 3.437087e-07 1.597407e-07
#### c. Cumulative Hazard 
## time haz var
## 1 19.5 0.002017401 2.543692e-07
## 2 20.5 0.003666945 4.636724e-07
## 3 21.5 0.007885072 1.002817e-06
## 4 22.5 0.011816218 1.517659e-06
## 5 23.5 0.011565517 1.502952e-06
## 6 24.5 0.002621860 3.437087e-07
## 7 25.5 0.001129844 1.597407e-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 7945 6 0.999 0.000308 0.999 1.000
## 20 7939 16 0.997 0.000590 0.996 0.998
## 21 7923 29 0.994 0.000896 0.992 0.995
## 22 7894 62 0.986 0.001328 0.983 0.988
## 23 7832 92 0.974 0.001779 0.971 0.978
## 24 7740 89 0.963 0.002118 0.959 0.967
## 25 7650 20 0.960 0.002186 0.956 0.965
## 26 7597 8 0.959 0.002213 0.955 0.964b. 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. LGB individuals experience a higher rate of citizenship naturalization due to resiliance and support networks than heterosexual individuals.
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 highest risk is around 23-24 years old.
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 7695 6 0.999 0.000318 0.999 1.000
## 20 7689 16 0.997 0.000609 0.996 0.998
## 21 7673 28 0.994 0.000916 0.992 0.995
## 22 7645 60 0.986 0.001353 0.983 0.988
## 23 7585 88 0.974 0.001805 0.971 0.978
## 24 7497 87 0.963 0.002153 0.959 0.967
## 25 7409 20 0.960 0.002224 0.956 0.965
## 26 7357 8 0.959 0.002252 0.955 0.964
##
## sexorient=b_bisexual
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 22 131 1 0.992 0.0076 0.978 1
## 23 130 1 0.985 0.0107 0.964 1
## 24 129 1 0.977 0.0131 0.952 1
##
## sexorient=c_LGB
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 21 119 1 0.992 0.00837 0.975 1.000
## 22 118 1 0.983 0.01178 0.960 1.000
## 23 117 3 0.958 0.01839 0.923 0.995
## 24 114 1 0.950 0.02006 0.911 0.990
## observed expected o-e
## a_straight 313 311.858997 1.141003
## b_bisexual 3 5.358724 -2.358724
## c_LGB 6 4.782279 1.217721## 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 3543 4 0.999 0.000564 0.998 1.000
## 20 3539 3 0.998 0.000746 0.997 0.999
## 21 3536 13 0.994 0.001259 0.992 0.997
## 22 3523 30 0.986 0.001982 0.982 0.990
## 23 3493 39 0.975 0.002629 0.970 0.980
## 24 3454 33 0.966 0.003063 0.960 0.972
## 25 3421 11 0.962 0.003193 0.956 0.969
## 26 3404 6 0.961 0.003262 0.954 0.967
##
## sexorient=a_straight, sex=b_female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 19 4152 2 1.000 0.000341 0.999 1.000
## 20 4150 13 0.996 0.000931 0.995 0.998
## 21 4137 15 0.993 0.001314 0.990 0.995
## 22 4122 30 0.986 0.001852 0.982 0.989
## 23 4092 49 0.974 0.002481 0.969 0.979
## 24 4043 54 0.961 0.003014 0.955 0.967
## 25 3988 9 0.959 0.003093 0.953 0.965
## 26 3953 2 0.958 0.003110 0.952 0.964
##
## sexorient=b_bisexual, sex=a_male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 23 21 1 0.952 0.0465 0.866 1
## 24 20 1 0.905 0.0641 0.788 1
##
## sexorient=b_bisexual, sex=b_female
## time n.risk n.event survival std.err
## 2.20e+01 1.10e+02 1.00e+00 9.91e-01 9.05e-03
## lower 95% CI upper 95% CI
## 9.73e-01 1.00e+00
##
## sexorient=c_LGB, sex=a_male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 21 75 1 0.987 0.0132 0.961 1.000
## 23 74 3 0.947 0.0259 0.897 0.999
## 24 71 1 0.933 0.0288 0.879 0.992
##
## sexorient=c_LGB, sex=b_female
## time n.risk n.event survival std.err
## 22.0000 44.0000 1.0000 0.9773 0.0225
## lower 95% CI upper 95% CI
## 0.9342 1.0000e. 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.
####The outcome variable is the transition of non-U.S. citizen in Wave 3 to naturalized United States citizen in Wave 4 ####The predictior variables are chosen based on previous literature and what may effect gaining citizenship for LGB individuals. The variables are sex, race/ethicity, sexual orientation, education, income, married, friendships, religiosity, parent/caretaker love, depression, insurance status, general health, mentor, volunteer
Fit the parametric model of your choosing to the data.
a. Did you choose an AFT or PH model and why?
I chose a Cox Proportional Hazard Model to analyzing the effect of several covariates 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 proved to be better.
b. Justify what parametric distribution you choose
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
f. Interpret your results and write them up
## 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 (132) 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= 7945, number of events= 322
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sexb_female -0.08028 0.92286 0.23034 -0.349 0.7275
## sexorientb_bisexual -1.48861 0.22569 0.89110 -1.671 0.0948 .
## sexorientc_LGB 0.67177 1.95771 0.55298 1.215 0.2244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.9229 1.0836 0.58758 1.449
## sexorientb_bisexual 0.2257 4.4309 0.03936 1.294
## sexorientc_LGB 1.9577 0.5108 0.66229 5.787
##
## Concordance= 0.517 (se = 0.023 )
## Likelihood ratio test= NA on 3 df, p=NA
## Wald test = 4.6 on 3 df, p=0.2
## Score (logrank) test = NA on 3 df, p=NA
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (132) 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 + incomeW4 + marriedW4a +
## friendships + church.attend + unloved_bycaretakerW4 +
## depressionW4 + insurance_status + general_health + mentor +
## volunteer, design = des2)
##
## n= 7945, number of events= 322
##
## coef exp(coef) se(coef) z
## sexb_female -0.12886 0.87909 0.22662 -0.569
## racethnicb-nhblack -0.54969 0.57713 0.53100 -1.035
## racethnicc-hispanic 2.65967 14.29161 0.34787 7.645
## racethnicd-asian 3.65356 38.61176 0.31206 11.708
## racethnice-native_american 0.62556 1.86928 1.15041 0.544
## racethnicf-other 2.07231 7.94315 0.66720 3.106
## sexorientb_bisexual -0.77553 0.46046 0.92700 -0.837
## sexorientc_LGB 0.88024 2.41148 0.44145 1.994
## educb_highschool_grad 0.50054 1.64961 0.48516 1.032
## educc_college_bach 0.91013 2.48465 0.50954 1.786
## educd_college+ 1.52586 4.59909 0.54409 2.804
## incomeW4b_$15,000<$30,000 1.44367 4.23620 0.52677 2.741
## incomeW4c_$30,000<$50,000 0.95228 2.59162 0.48269 1.973
## incomeW4d_$50,000<$75,000 1.35427 3.87394 0.53631 2.525
## incomeW4e_$75,000<$100,000 1.52772 4.60766 0.53021 2.881
## incomeW4f_$100,000<$150,000 1.48554 4.41734 0.52407 2.835
## incomeW4g_$150,000+ 1.03380 2.81172 0.43035 2.402
## marriedW4ab_married -0.57154 0.56465 0.20538 -2.783
## friendshipsb_1or2 -0.31740 0.72804 0.55736 -0.569
## friendshipsc_3to9 -0.53338 0.58662 0.46819 -1.139
## friendshipsd_>10 -0.80679 0.44629 0.56068 -1.439
## church.attendb_once.week 0.45228 1.57189 0.25857 1.749
## church.attendc_2plus.week -0.09653 0.90798 0.38585 -0.250
## unloved_bycaretakerW4b_10+_times -0.21099 0.80978 0.24910 -0.847
## unloved_bycaretakerW4c_never -0.44985 0.63772 0.19710 -2.282
## depressionW4b_yes -0.79930 0.44964 0.33510 -2.385
## insurance_statusb_yes_insurance 0.05401 1.05550 0.24744 0.218
## general_healthpoor/bad 0.30088 1.35105 0.32373 0.929
## mentorb_yes -0.43363 0.64815 0.22844 -1.898
## volunteerb_yes 0.11127 1.11770 0.16095 0.691
## Pr(>|z|)
## sexb_female 0.56961
## racethnicb-nhblack 0.30058
## racethnicc-hispanic 2.08e-14 ***
## racethnicd-asian < 2e-16 ***
## racethnice-native_american 0.58660
## racethnicf-other 0.00190 **
## sexorientb_bisexual 0.40281
## sexorientc_LGB 0.04616 *
## educb_highschool_grad 0.30221
## educc_college_bach 0.07407 .
## educd_college+ 0.00504 **
## incomeW4b_$15,000<$30,000 0.00613 **
## incomeW4c_$30,000<$50,000 0.04851 *
## incomeW4d_$50,000<$75,000 0.01156 *
## incomeW4e_$75,000<$100,000 0.00396 **
## incomeW4f_$100,000<$150,000 0.00459 **
## incomeW4g_$150,000+ 0.01630 *
## marriedW4ab_married 0.00539 **
## friendshipsb_1or2 0.56904
## friendshipsc_3to9 0.25460
## friendshipsd_>10 0.15016
## church.attendb_once.week 0.08026 .
## church.attendc_2plus.week 0.80246
## unloved_bycaretakerW4b_10+_times 0.39699
## unloved_bycaretakerW4c_never 0.02247 *
## depressionW4b_yes 0.01707 *
## insurance_statusb_yes_insurance 0.82720
## general_healthpoor/bad 0.35267
## mentorb_yes 0.05767 .
## volunteerb_yes 0.48935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.8791 1.13753 0.56382 1.3707
## racethnicb-nhblack 0.5771 1.73271 0.20384 1.6340
## racethnicc-hispanic 14.2916 0.06997 7.22720 28.2613
## racethnicd-asian 38.6118 0.02590 20.94563 71.1780
## racethnice-native_american 1.8693 0.53496 0.19608 17.8200
## racethnicf-other 7.9432 0.12589 2.14820 29.3705
## sexorientb_bisexual 0.4605 2.17175 0.07484 2.8330
## sexorientc_LGB 2.4115 0.41468 1.01512 5.7286
## educb_highschool_grad 1.6496 0.60621 0.63741 4.2692
## educc_college_bach 2.4846 0.40247 0.91526 6.7451
## educd_college+ 4.5991 0.21743 1.58323 13.3598
## incomeW4b_$15,000<$30,000 4.2362 0.23606 1.50865 11.8950
## incomeW4c_$30,000<$50,000 2.5916 0.38586 1.00625 6.6748
## incomeW4d_$50,000<$75,000 3.8739 0.25814 1.35409 11.0830
## incomeW4e_$75,000<$100,000 4.6077 0.21703 1.62991 13.0256
## incomeW4f_$100,000<$150,000 4.4173 0.22638 1.58151 12.3382
## incomeW4g_$150,000+ 2.8117 0.35565 1.20964 6.5356
## marriedW4ab_married 0.5647 1.77100 0.37754 0.8445
## friendshipsb_1or2 0.7280 1.37355 0.24419 2.1706
## friendshipsc_3to9 0.5866 1.70469 0.23433 1.4685
## friendshipsd_>10 0.4463 2.24070 0.14872 1.3393
## church.attendb_once.week 1.5719 0.63618 0.94695 2.6093
## church.attendc_2plus.week 0.9080 1.10134 0.42623 1.9343
## unloved_bycaretakerW4b_10+_times 0.8098 1.23490 0.49698 1.3195
## unloved_bycaretakerW4c_never 0.6377 1.56808 0.43337 0.9384
## depressionW4b_yes 0.4496 2.22399 0.23315 0.8672
## insurance_statusb_yes_insurance 1.0555 0.94742 0.64989 1.7143
## general_healthpoor/bad 1.3511 0.74016 0.71633 2.5482
## mentorb_yes 0.6482 1.54285 0.41421 1.0142
## volunteerb_yes 1.1177 0.89469 0.81531 1.5322
##
## Concordance= 0.864 (se = 0.019 )
## Likelihood ratio test= NA on 30 df, p=NA
## Wald test = 463.6 on 30 df, p=<2e-16
## Score (logrank) test = NA on 30 df, p=NA## Response sexb_female :
##
## Call:
## lm(formula = sexb_female ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5845 -0.5428 0.4321 0.4541 0.4827
##
## Coefficients:
## Estimate
## (Intercept) 0.27257
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.00852
## Std. Error
## (Intercept) 0.44741
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01952
## t value
## (Intercept) 0.609
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.437
## Pr(>|t|)
## (Intercept) 0.543
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.663
##
## Residual standard error: 0.4992 on 320 degrees of freedom
## Multiple R-squared: 0.0005951, Adjusted R-squared: -0.002528
## F-statistic: 0.1905 on 1 and 320 DF, p-value: 0.6628
##
##
## Response racethnicb-nhblack :
##
## Call:
## lm(formula = `racethnicb-nhblack` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05529 -0.04626 -0.04358 -0.04043 0.96713
##
## Coefficients:
## Estimate
## (Intercept) -0.043359
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.002679
## Std. Error
## (Intercept) 0.183331
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.007998
## t value
## (Intercept) -0.237
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.335
## Pr(>|t|)
## (Intercept) 0.813
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.738
##
## Residual standard error: 0.2046 on 320 degrees of freedom
## Multiple R-squared: 0.0003506, Adjusted R-squared: -0.002773
## F-statistic: 0.1122 on 1 and 320 DF, p-value: 0.7379
##
##
## Response racethnicc-hispanic :
##
## Call:
## lm(formula = `racethnicc-hispanic` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2465 -0.2127 -0.2059 -0.1949 0.8160
##
## Coefficients:
## Estimate
## (Intercept) 0.05870
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.00547
## Std. Error
## (Intercept) 0.36552
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01595
## t value
## (Intercept) 0.161
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.343
## Pr(>|t|)
## (Intercept) 0.873
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.732
##
## Residual standard error: 0.4079 on 320 degrees of freedom
## Multiple R-squared: 0.0003677, Adjusted R-squared: -0.002756
## F-statistic: 0.1177 on 1 and 320 DF, p-value: 0.7318
##
##
## Response racethnicd-asian :
##
## Call:
## lm(formula = `racethnicd-asian` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5641 -0.4963 -0.4189 0.5065 0.6243
##
## Coefficients:
## Estimate
## (Intercept) -0.19825
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01239
## Std. Error
## (Intercept) 0.45257
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01974
## t value
## (Intercept) -0.438
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.628
## Pr(>|t|)
## (Intercept) 0.662
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.531
##
## Residual standard error: 0.505 on 320 degrees of freedom
## Multiple R-squared: 0.00123, Adjusted R-squared: -0.001892
## F-statistic: 0.394 on 1 and 320 DF, p-value: 0.5307
##
##
## Response racethnice-native_american :
##
## Call:
## lm(formula = `racethnice-native_american` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00894 -0.00501 -0.00329 -0.00156 0.99499
##
## Coefficients:
## Estimate
## (Intercept) -0.039644
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.001725
## Std. Error
## (Intercept) 0.049973
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.002180
## t value
## (Intercept) -0.793
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.791
## Pr(>|t|)
## (Intercept) 0.428
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.429
##
## Residual standard error: 0.05576 on 320 degrees of freedom
## Multiple R-squared: 0.001954, Adjusted R-squared: -0.001165
## F-statistic: 0.6264 on 1 and 320 DF, p-value: 0.4293
##
##
## Response racethnicf-other :
##
## Call:
## lm(formula = `racethnicf-other` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04567 -0.02869 -0.02412 -0.01955 0.98114
##
## Coefficients:
## Estimate
## (Intercept) 0.105957
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.004567
## Std. Error
## (Intercept) 0.139841
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.006100
## t value
## (Intercept) 0.758
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.749
## Pr(>|t|)
## (Intercept) 0.449
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.455
##
## Residual standard error: 0.156 on 320 degrees of freedom
## Multiple R-squared: 0.001749, Adjusted R-squared: -0.001371
## F-statistic: 0.5606 on 1 and 320 DF, p-value: 0.4546
##
##
## Response sexorientb_bisexual :
##
## Call:
## lm(formula = sexorientb_bisexual ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01636 -0.01163 -0.00953 -0.00743 0.99064
##
## Coefficients:
## Estimate
## (Intercept) -0.042485
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.002097
## Std. Error
## (Intercept) 0.086325
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.003766
## t value
## (Intercept) -0.492
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.557
## Pr(>|t|)
## (Intercept) 0.623
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.578
##
## Residual standard error: 0.09633 on 320 degrees of freedom
## Multiple R-squared: 0.0009682, Adjusted R-squared: -0.002154
## F-statistic: 0.3101 on 1 and 320 DF, p-value: 0.578
##
##
## Response sexorientc_LGB :
##
## Call:
## lm(formula = sexorientc_LGB ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03535 -0.02315 -0.01922 -0.01516 0.98484
##
## Coefficients:
## Estimate
## (Intercept) -0.103022
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.004064
## Std. Error
## (Intercept) 0.121408
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.005296
## t value
## (Intercept) -0.849
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.767
## Pr(>|t|)
## (Intercept) 0.397
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.443
##
## Residual standard error: 0.1355 on 320 degrees of freedom
## Multiple R-squared: 0.001837, Adjusted R-squared: -0.001282
## F-statistic: 0.5889 on 1 and 320 DF, p-value: 0.4434
##
##
## Response educb_highschool_grad :
##
## Call:
## lm(formula = educb_highschool_grad ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5242 -0.4873 -0.4576 0.5127 0.5525
##
## Coefficients:
## Estimate
## (Intercept) 0.21739
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.00859
## Std. Error
## (Intercept) 0.44815
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01955
## t value
## (Intercept) 0.485
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.439
## Pr(>|t|)
## (Intercept) 0.628
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.661
##
## Residual standard error: 0.5001 on 320 degrees of freedom
## Multiple R-squared: 0.000603, Adjusted R-squared: -0.00252
## F-statistic: 0.1931 on 1 and 320 DF, p-value: 0.6607
##
##
## Response educc_college_bach :
##
## Call:
## lm(formula = educc_college_bach ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3813 -0.3139 -0.2872 0.6640 0.7791
##
## Coefficients:
## Estimate
## (Intercept) -0.47221
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.02211
## Std. Error
## (Intercept) 0.41194
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01797
## t value
## (Intercept) -1.146
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.230
## Pr(>|t|)
## (Intercept) 0.253
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.220
##
## Residual standard error: 0.4597 on 320 degrees of freedom
## Multiple R-squared: 0.004707, Adjusted R-squared: 0.001597
## F-statistic: 1.513 on 1 and 320 DF, p-value: 0.2195
##
##
## Response educd_college+ :
##
## Call:
## lm(formula = `educd_college+` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2089 -0.1808 -0.1740 -0.1641 0.8436
##
## Coefficients:
## Estimate
## (Intercept) -0.139803
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.004386
## Std. Error
## (Intercept) 0.342857
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.014957
## t value
## (Intercept) -0.408
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.293
## Pr(>|t|)
## (Intercept) 0.684
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.770
##
## Residual standard error: 0.3826 on 320 degrees of freedom
## Multiple R-squared: 0.0002687, Adjusted R-squared: -0.002855
## F-statistic: 0.08599 on 1 and 320 DF, p-value: 0.7695
##
##
## Response incomeW4b_$15,000<$30,000 :
##
## Call:
## lm(formula = `incomeW4b_$15,000<$30,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09695 -0.08596 -0.08110 -0.07541 0.93432
##
## Coefficients:
## Estimate
## (Intercept) -0.137010
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.004865
## Std. Error
## (Intercept) 0.244856
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.010682
## t value
## (Intercept) -0.560
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.455
## Pr(>|t|)
## (Intercept) 0.576
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.649
##
## Residual standard error: 0.2732 on 320 degrees of freedom
## Multiple R-squared: 0.0006479, Adjusted R-squared: -0.002475
## F-statistic: 0.2075 on 1 and 320 DF, p-value: 0.6491
##
##
## Response incomeW4c_$30,000<$50,000 :
##
## Call:
## lm(formula = `incomeW4c_$30,000<$50,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1619 -0.1476 -0.1418 -0.1350 0.8711
##
## Coefficients:
## Estimate
## (Intercept) -0.076776
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.002635
## Std. Error
## (Intercept) 0.314564
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.013723
## t value
## (Intercept) -0.244
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.192
## Pr(>|t|)
## (Intercept) 0.807
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.848
##
## Residual standard error: 0.351 on 320 degrees of freedom
## Multiple R-squared: 0.0001152, Adjusted R-squared: -0.003009
## F-statistic: 0.03688 on 1 and 320 DF, p-value: 0.8478
##
##
## Response incomeW4d_$50,000<$75,000 :
##
## Call:
## lm(formula = `incomeW4d_$50,000<$75,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2992 -0.2598 -0.2477 0.7118 0.7671
##
## Coefficients:
## Estimate
## (Intercept) 0.077298
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.004017
## Std. Error
## (Intercept) 0.391807
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.017092
## t value
## (Intercept) 0.197
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.235
## Pr(>|t|)
## (Intercept) 0.844
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.814
##
## Residual standard error: 0.4372 on 320 degrees of freedom
## Multiple R-squared: 0.0001726, Adjusted R-squared: -0.002952
## F-statistic: 0.05523 on 1 and 320 DF, p-value: 0.8144
##
##
## Response incomeW4e_$75,000<$100,000 :
##
## Call:
## lm(formula = `incomeW4e_$75,000<$100,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2248 -0.2152 -0.2044 -0.1882 0.8207
##
## Coefficients:
## Estimate
## (Intercept) -0.07020
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.00285
## Std. Error
## (Intercept) 0.36488
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01592
## t value
## (Intercept) -0.192
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.179
## Pr(>|t|)
## (Intercept) 0.848
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.858
##
## Residual standard error: 0.4072 on 320 degrees of freedom
## Multiple R-squared: 0.0001002, Adjusted R-squared: -0.003025
## F-statistic: 0.03205 on 1 and 320 DF, p-value: 0.858
##
##
## Response incomeW4f_$100,000<$150,000 :
##
## Call:
## lm(formula = `incomeW4f_$100,000<$150,000` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2624 -0.2100 -0.1912 -0.1684 0.8606
##
## Coefficients:
## Estimate
## (Intercept) 0.35130
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01450
## Std. Error
## (Intercept) 0.36045
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01572
## t value
## (Intercept) 0.975
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.922
## Pr(>|t|)
## (Intercept) 0.330
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.357
##
## Residual standard error: 0.4022 on 320 degrees of freedom
## Multiple R-squared: 0.002651, Adjusted R-squared: -0.0004655
## F-statistic: 0.8506 on 1 and 320 DF, p-value: 0.3571
##
##
## Response incomeW4g_$150,000+ :
##
## Call:
## lm(formula = `incomeW4g_$150,000+` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13838 -0.11354 -0.10282 -0.09207 0.92934
##
## Coefficients:
## Estimate
## (Intercept) -0.20593
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01072
## Std. Error
## (Intercept) 0.27227
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01188
## t value
## (Intercept) -0.756
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.903
## Pr(>|t|)
## (Intercept) 0.450
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.367
##
## Residual standard error: 0.3038 on 320 degrees of freedom
## Multiple R-squared: 0.00254, Adjusted R-squared: -0.0005771
## F-statistic: 0.8148 on 1 and 320 DF, p-value: 0.3674
##
##
## Response marriedW4ab_married :
##
## Call:
## lm(formula = marriedW4ab_married ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6181 -0.4588 -0.3595 0.5345 0.7257
##
## Coefficients:
## Estimate
## (Intercept) -0.96459
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.04458
## Std. Error
## (Intercept) 0.44523
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01942
## t value
## (Intercept) -2.167
## des2$variables$age_transition1[des2$variables$transition1 == 1] 2.295
## Pr(>|t|)
## (Intercept) 0.0310 *
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.0224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4968 on 320 degrees of freedom
## Multiple R-squared: 0.0162, Adjusted R-squared: 0.01312
## F-statistic: 5.268 on 1 and 320 DF, p-value: 0.02236
##
##
## Response friendshipsb_1or2 :
##
## Call:
## lm(formula = friendshipsb_1or2 ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2127 -0.1853 -0.1768 -0.1658 0.8408
##
## Coefficients:
## Estimate
## (Intercept) -0.176335
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.006514
## Std. Error
## (Intercept) 0.345086
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.015054
## t value
## (Intercept) -0.511
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.433
## Pr(>|t|)
## (Intercept) 0.610
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.666
##
## Residual standard error: 0.3851 on 320 degrees of freedom
## Multiple R-squared: 0.0005848, Adjusted R-squared: -0.002538
## F-statistic: 0.1872 on 1 and 320 DF, p-value: 0.6655
##
##
## Response friendshipsc_3to9 :
##
## Call:
## lm(formula = friendshipsc_3to9 ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7067 -0.6393 0.3281 0.3514 0.4059
##
## Coefficients:
## Estimate
## (Intercept) 0.37293
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01629
## Std. Error
## (Intercept) 0.42654
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01861
## t value
## (Intercept) 0.874
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.875
## Pr(>|t|)
## (Intercept) 0.383
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.382
##
## Residual standard error: 0.476 on 320 degrees of freedom
## Multiple R-squared: 0.002389, Adjusted R-squared: -0.0007287
## F-statistic: 0.7662 on 1 and 320 DF, p-value: 0.382
##
##
## Response friendshipsd_>10 :
##
## Call:
## lm(formula = `friendshipsd_>10` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1715 -0.1509 -0.1411 -0.1219 0.8896
##
## Coefficients:
## Estimate
## (Intercept) -0.185150
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.009814
## Std. Error
## (Intercept) 0.311500
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.013589
## t value
## (Intercept) -0.594
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.722
## Pr(>|t|)
## (Intercept) 0.553
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.471
##
## Residual standard error: 0.3476 on 320 degrees of freedom
## Multiple R-squared: 0.001627, Adjusted R-squared: -0.001493
## F-statistic: 0.5216 on 1 and 320 DF, p-value: 0.4707
##
##
## Response church.attendb_once.week :
##
## Call:
## lm(formula = church.attendb_once.week ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1947 -0.1690 -0.1639 -0.1607 0.8470
##
## Coefficients:
## Estimate
## (Intercept) 0.027908
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.002376
## Std. Error
## (Intercept) 0.335653
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.014642
## t value
## (Intercept) 0.083
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.162
## Pr(>|t|)
## (Intercept) 0.934
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.871
##
## Residual standard error: 0.3745 on 320 degrees of freedom
## Multiple R-squared: 8.229e-05, Adjusted R-squared: -0.003042
## F-statistic: 0.02633 on 1 and 320 DF, p-value: 0.8712
##
##
## Response church.attendc_2plus.week :
##
## Call:
## lm(formula = church.attendc_2plus.week ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10400 -0.06996 -0.05452 -0.03908 0.97536
##
## Coefficients:
## Estimate
## (Intercept) -0.359444
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.015444
## Std. Error
## (Intercept) 0.199851
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.008718
## t value
## (Intercept) -1.799
## des2$variables$age_transition1[des2$variables$transition1 == 1] 1.771
## Pr(>|t|)
## (Intercept) 0.0730 .
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.0774 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.223 on 320 degrees of freedom
## Multiple R-squared: 0.009711, Adjusted R-squared: 0.006616
## F-statistic: 3.138 on 1 and 320 DF, p-value: 0.07744
##
##
## Response unloved_bycaretakerW4b_10+_times :
##
## Call:
## lm(formula = `unloved_bycaretakerW4b_10+_times` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12458 -0.11039 -0.10434 -0.09764 0.91427
##
## Coefficients:
## Estimate
## (Intercept) -0.100186
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.004651
## Std. Error
## (Intercept) 0.276141
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.012046
## t value
## (Intercept) -0.363
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.386
## Pr(>|t|)
## (Intercept) 0.717
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.700
##
## Residual standard error: 0.3081 on 320 degrees of freedom
## Multiple R-squared: 0.0004656, Adjusted R-squared: -0.002658
## F-statistic: 0.1491 on 1 and 320 DF, p-value: 0.6997
##
##
## Response unloved_bycaretakerW4c_never :
##
## Call:
## lm(formula = unloved_bycaretakerW4c_never ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5861 -0.4976 0.4047 0.5024 0.5576
##
## Coefficients:
## Estimate
## (Intercept) 0.49416
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01840
## Std. Error
## (Intercept) 0.44956
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01961
## t value
## (Intercept) 1.099
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.938
## Pr(>|t|)
## (Intercept) 0.273
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.349
##
## Residual standard error: 0.5016 on 320 degrees of freedom
## Multiple R-squared: 0.002744, Adjusted R-squared: -0.0003729
## F-statistic: 0.8803 on 1 and 320 DF, p-value: 0.3488
##
##
## Response depressionW4b_yes :
##
## Call:
## lm(formula = depressionW4b_yes ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06511 -0.06288 -0.06224 -0.06108 0.94019
##
## Coefficients:
## Estimate
## (Intercept) -0.012711
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.000632
## Std. Error
## (Intercept) 0.216950
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.009464
## t value
## (Intercept) -0.059
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.067
## Pr(>|t|)
## (Intercept) 0.953
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.947
##
## Residual standard error: 0.2421 on 320 degrees of freedom
## Multiple R-squared: 1.394e-05, Adjusted R-squared: -0.003111
## F-statistic: 0.00446 on 1 and 320 DF, p-value: 0.9468
##
##
## Response insurance_statusb_yes_insurance :
##
## Call:
## lm(formula = insurance_statusb_yes_insurance ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8660 0.1408 0.1474 0.1528 0.1721
##
## Coefficients:
## Estimate
## (Intercept) -0.077440
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.003054
## Std. Error
## (Intercept) 0.320079
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.013963
## t value
## (Intercept) -0.242
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.219
## Pr(>|t|)
## (Intercept) 0.809
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.827
##
## Residual standard error: 0.3572 on 320 degrees of freedom
## Multiple R-squared: 0.0001495, Adjusted R-squared: -0.002975
## F-statistic: 0.04784 on 1 and 320 DF, p-value: 0.827
##
##
## Response general_healthpoor/bad :
##
## Call:
## lm(formula = `general_healthpoor/bad` ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10322 -0.10048 -0.09910 -0.09748 0.90282
##
## Coefficients:
## Estimate
## (Intercept) -0.0073978
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.0002799
## Std. Error
## (Intercept) 0.2689020
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.0117305
## t value
## (Intercept) -0.028
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.024
## Pr(>|t|)
## (Intercept) 0.978
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.981
##
## Residual standard error: 0.3001 on 320 degrees of freedom
## Multiple R-squared: 1.779e-06, Adjusted R-squared: -0.003123
## F-statistic: 0.0005694 on 1 and 320 DF, p-value: 0.981
##
##
## Response mentorb_yes :
##
## Call:
## lm(formula = mentorb_yes ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7124 -0.6796 0.3056 0.3173 0.3428
##
## Coefficients:
## Estimate
## (Intercept) -0.181606
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.007408
## Std. Error
## (Intercept) 0.416932
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.018188
## t value
## (Intercept) -0.436
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.407
## Pr(>|t|)
## (Intercept) 0.663
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.684
##
## Residual standard error: 0.4652 on 320 degrees of freedom
## Multiple R-squared: 0.0005182, Adjusted R-squared: -0.002605
## F-statistic: 0.1659 on 1 and 320 DF, p-value: 0.684
##
##
## Response volunteerb_yes :
##
## Call:
## lm(formula = volunteerb_yes ~ des2$variables$age_transition1[des2$variables$transition1 ==
## 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5544 -0.4980 0.4433 0.4969 0.5423
##
## Coefficients:
## Estimate
## (Intercept) 0.27444
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.01284
## Std. Error
## (Intercept) 0.44831
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.01956
## t value
## (Intercept) 0.612
## des2$variables$age_transition1[des2$variables$transition1 == 1] -0.656
## Pr(>|t|)
## (Intercept) 0.541
## des2$variables$age_transition1[des2$variables$transition1 == 1] 0.512
##
## Residual standard error: 0.5002 on 320 degrees of freedom
## Multiple R-squared: 0.001345, Adjusted R-squared: -0.001776
## F-statistic: 0.4309 on 1 and 320 DF, p-value: 0.512Grambsch and Therneau test
## rho chisq p
## sexb_female -0.11605 38.5118 5.44e-10
## racethnicb-nhblack 0.04713 3.9413 4.71e-02
## racethnicc-hispanic 0.03361 3.1250 7.71e-02
## racethnicd-asian 0.09604 24.2164 8.61e-07
## racethnice-native_american 0.02292 1.0427 3.07e-01
## racethnicf-other -0.06005 8.8977 2.86e-03
## sexorientb_bisexual 0.00708 0.0986 7.54e-01
## sexorientc_LGB -0.01171 0.1808 6.71e-01
## educb_highschool_grad 0.11950 38.8473 4.58e-10
## educc_college_bach 0.08438 17.2918 3.21e-05
## educd_college+ 0.13313 49.2969 2.20e-12
## incomeW4b_$15,000<$30,000 0.11680 19.3426 1.09e-05
## incomeW4c_$30,000<$50,000 0.07209 7.3298 6.78e-03
## incomeW4d_$50,000<$75,000 0.06417 6.4491 1.11e-02
## incomeW4e_$75,000<$100,000 0.05776 4.4264 3.54e-02
## incomeW4f_$100,000<$150,000 0.09151 12.2103 4.75e-04
## incomeW4g_$150,000+ 0.08602 7.5803 5.90e-03
## marriedW4ab_married -0.21585 103.5136 2.59e-24
## friendshipsb_1or2 0.17534 60.0803 9.11e-15
## friendshipsc_3to9 0.15045 43.7214 3.79e-11
## friendshipsd_>10 0.13533 40.1325 2.37e-10
## church.attendb_once.week 0.09456 26.1975 3.08e-07
## church.attendc_2plus.week 0.17536 50.7624 1.04e-12
## unloved_bycaretakerW4b_10+_times -0.06982 4.5790 3.24e-02
## unloved_bycaretakerW4c_never -0.16953 75.9162 2.96e-18
## depressionW4b_yes 0.12471 16.8199 4.11e-05
## insurance_statusb_yes_insurance -0.16382 49.5440 1.94e-12
## general_healthpoor/bad -0.12790 47.8091 4.70e-12
## mentorb_yes -0.00649 0.0689 7.93e-01
## volunteerb_yes -0.15561 31.2885 2.22e-08
## GLOBAL NA 230.3985 8.81e-33Plot 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
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (132) 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 + incomeW4 + marriedW4a +
## friendships + church.attend + unloved_bycaretakerW4 +
## depressionW4 + insurance_status + general_health + mentor +
## volunteer, design = des2)
##
## n= 7945, number of events= 322
##
## coef exp(coef) se(coef) z
## sexb_female -0.12676 0.88094 0.22730 -0.558
## racethnicb-nhblack -0.54776 0.57825 0.53088 -1.032
## racethnicc-hispanic 2.66254 14.33263 0.34729 7.667
## racethnicd-asian 3.64873 38.42596 0.31185 11.700
## racethnice-native_american 0.63428 1.88566 1.14525 0.554
## racethnicf-other 2.07505 7.96492 0.66639 3.114
## educb_highschool_grad 0.49799 1.64542 0.48585 1.025
## educc_college_bach 0.91258 2.49074 0.50811 1.796
## educd_college+ 1.52587 4.59914 0.54418 2.804
## incomeW4b_$15,000<$30,000 1.41935 4.13442 0.52627 2.697
## incomeW4c_$30,000<$50,000 0.94458 2.57174 0.48028 1.967
## incomeW4d_$50,000<$75,000 1.34472 3.83713 0.53381 2.519
## incomeW4e_$75,000<$100,000 1.51927 4.56888 0.52750 2.880
## incomeW4f_$100,000<$150,000 1.47862 4.38687 0.52235 2.831
## incomeW4g_$150,000+ 1.02727 2.79342 0.42947 2.392
## marriedW4ab_married -0.57187 0.56447 0.20520 -2.787
## friendshipsb_1or2 -0.31783 0.72773 0.55795 -0.570
## friendshipsc_3to9 -0.53307 0.58680 0.46824 -1.138
## friendshipsd_>10 -0.80223 0.44833 0.55993 -1.433
## church.attendb_once.week 0.45418 1.57489 0.25769 1.763
## church.attendc_2plus.week -0.09724 0.90734 0.38562 -0.252
## unloved_bycaretakerW4b_10+_times -0.20859 0.81173 0.24889 -0.838
## unloved_bycaretakerW4c_never -0.44837 0.63867 0.19776 -2.267
## depressionW4b_yes -0.79798 0.45024 0.33448 -2.386
## insurance_statusb_yes_insurance 0.04969 1.05095 0.24657 0.202
## general_healthpoor/bad 0.30262 1.35340 0.32449 0.933
## mentorb_yes -0.42526 0.65360 0.22957 -1.852
## volunteerb_yes 0.10515 1.11087 0.16044 0.655
## Pr(>|z|)
## sexb_female 0.57706
## racethnicb-nhblack 0.30217
## racethnicc-hispanic 1.77e-14 ***
## racethnicd-asian < 2e-16 ***
## racethnice-native_american 0.57969
## racethnicf-other 0.00185 **
## educb_highschool_grad 0.30537
## educc_college_bach 0.07249 .
## educd_college+ 0.00505 **
## incomeW4b_$15,000<$30,000 0.00700 **
## incomeW4c_$30,000<$50,000 0.04922 *
## incomeW4d_$50,000<$75,000 0.01177 *
## incomeW4e_$75,000<$100,000 0.00398 **
## incomeW4f_$100,000<$150,000 0.00464 **
## incomeW4g_$150,000+ 0.01676 *
## marriedW4ab_married 0.00532 **
## friendshipsb_1or2 0.56892
## friendshipsc_3to9 0.25493
## friendshipsd_>10 0.15193
## church.attendb_once.week 0.07798 .
## church.attendc_2plus.week 0.80091
## unloved_bycaretakerW4b_10+_times 0.40198
## unloved_bycaretakerW4c_never 0.02337 *
## depressionW4b_yes 0.01705 *
## insurance_statusb_yes_insurance 0.84028
## general_healthpoor/bad 0.35102
## mentorb_yes 0.06396 .
## volunteerb_yes 0.51224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexb_female 0.8809 1.13514 0.5643 1.3754
## racethnicb-nhblack 0.5782 1.72937 0.2043 1.6368
## racethnicc-hispanic 14.3326 0.06977 7.2563 28.3099
## racethnicd-asian 38.4260 0.02602 20.8533 70.8068
## racethnice-native_american 1.8857 0.53032 0.1998 17.7950
## racethnicf-other 7.9649 0.12555 2.1575 29.4043
## educb_highschool_grad 1.6454 0.60775 0.6349 4.2641
## educc_college_bach 2.4907 0.40149 0.9201 6.7426
## educd_college+ 4.5991 0.21743 1.5830 13.3623
## incomeW4b_$15,000<$30,000 4.1344 0.24187 1.4738 11.5979
## incomeW4c_$30,000<$50,000 2.5717 0.38884 1.0033 6.5924
## incomeW4d_$50,000<$75,000 3.8371 0.26061 1.3478 10.9242
## incomeW4e_$75,000<$100,000 4.5689 0.21887 1.6248 12.8475
## incomeW4f_$100,000<$150,000 4.3869 0.22795 1.5759 12.2119
## incomeW4g_$150,000+ 2.7934 0.35798 1.2038 6.4819
## marriedW4ab_married 0.5645 1.77157 0.3776 0.8439
## friendshipsb_1or2 0.7277 1.37414 0.2438 2.1722
## friendshipsc_3to9 0.5868 1.70416 0.2344 1.4691
## friendshipsd_>10 0.4483 2.23052 0.1496 1.3434
## church.attendb_once.week 1.5749 0.63497 0.9504 2.6097
## church.attendc_2plus.week 0.9073 1.10213 0.4261 1.9320
## unloved_bycaretakerW4b_10+_times 0.8117 1.23194 0.4984 1.3221
## unloved_bycaretakerW4c_never 0.6387 1.56575 0.4335 0.9410
## depressionW4b_yes 0.4502 2.22104 0.2337 0.8673
## insurance_statusb_yes_insurance 1.0509 0.95152 0.6482 1.7040
## general_healthpoor/bad 1.3534 0.73888 0.7165 2.5564
## mentorb_yes 0.6536 1.52999 0.4168 1.0250
## volunteerb_yes 1.1109 0.90019 0.8111 1.5214
##
## Concordance= 0.863 (se = 0.019 )
## Likelihood ratio test= NA on 28 df, p=NA
## Wald test = 426.1 on 28 df, p=<2e-16
## Score (logrank) test = NA on 28 df, p=NA## Stratified 1 - level Cluster Sampling design (with replacement)
## With (132) clusters.
## svydesign(ids = ~psuscid, strata = ~region, weights = ~GSWGT4,
## data = addhealth, nest = T)## Call:
## svycoxph(formula = Surv(time = age_transition1, event = transition1) ~
## strata(sex) + sexorient + racethnic + educ + incomeW4 + marriedW4a +
## friendships + church.attend + unloved_bycaretakerW4 +
## depressionW4 + insurance_status + general_health + mentor +
## volunteer, design = des2)
##
## n= 7945, number of events= 322
##
## coef exp(coef) se(coef) z
## sexorientb_bisexual -0.76996 0.46303 0.92661 -0.831
## sexorientc_LGB 0.88135 2.41415 0.44595 1.976
## racethnicb-nhblack -0.54940 0.57730 0.53112 -1.034
## racethnicc-hispanic 2.65803 14.26811 0.34799 7.638
## racethnicd-asian 3.65298 38.58941 0.31127 11.736
## racethnice-native_american 0.62399 1.86636 1.15076 0.542
## racethnicf-other 2.07294 7.94817 0.66589 3.113
## educb_highschool_grad 0.50199 1.65200 0.48130 1.043
## educc_college_bach 0.91619 2.49975 0.50499 1.814
## educd_college+ 1.52827 4.61018 0.54199 2.820
## incomeW4b_$15,000<$30,000 1.44976 4.26209 0.52931 2.739
## incomeW4c_$30,000<$50,000 0.95994 2.61153 0.48575 1.976
## incomeW4d_$50,000<$75,000 1.36463 3.91427 0.53995 2.527
## incomeW4e_$75,000<$100,000 1.53502 4.64140 0.53312 2.879
## incomeW4f_$100,000<$150,000 1.49061 4.43980 0.52572 2.835
## incomeW4g_$150,000+ 1.03239 2.80777 0.42712 2.417
## marriedW4ab_married -0.56870 0.56626 0.20603 -2.760
## friendshipsb_1or2 -0.31808 0.72755 0.55785 -0.570
## friendshipsc_3to9 -0.53661 0.58473 0.46845 -1.145
## friendshipsd_>10 -0.80946 0.44510 0.56118 -1.442
## church.attendb_once.week 0.45077 1.56952 0.25853 1.744
## church.attendc_2plus.week -0.09707 0.90749 0.38604 -0.251
## unloved_bycaretakerW4b_10+_times -0.21180 0.80912 0.24885 -0.851
## unloved_bycaretakerW4c_never -0.45158 0.63662 0.19563 -2.308
## depressionW4b_yes -0.79792 0.45026 0.33545 -2.379
## insurance_statusb_yes_insurance 0.05570 1.05728 0.24632 0.226
## general_healthpoor/bad 0.29670 1.34541 0.32414 0.915
## mentorb_yes -0.43475 0.64743 0.22816 -1.905
## volunteerb_yes 0.11005 1.11633 0.16046 0.686
## Pr(>|z|)
## sexorientb_bisexual 0.40600
## sexorientc_LGB 0.04812 *
## racethnicb-nhblack 0.30094
## racethnicc-hispanic 2.2e-14 ***
## racethnicd-asian < 2e-16 ***
## racethnice-native_american 0.58765
## racethnicf-other 0.00185 **
## educb_highschool_grad 0.29696
## educc_college_bach 0.06964 .
## educd_college+ 0.00481 **
## incomeW4b_$15,000<$30,000 0.00616 **
## incomeW4c_$30,000<$50,000 0.04813 *
## incomeW4d_$50,000<$75,000 0.01149 *
## incomeW4e_$75,000<$100,000 0.00399 **
## incomeW4f_$100,000<$150,000 0.00458 **
## incomeW4g_$150,000+ 0.01564 *
## marriedW4ab_married 0.00578 **
## friendshipsb_1or2 0.56855
## friendshipsc_3to9 0.25201
## friendshipsd_>10 0.14918
## church.attendb_once.week 0.08123 .
## church.attendc_2plus.week 0.80146
## unloved_bycaretakerW4b_10+_times 0.39469
## unloved_bycaretakerW4c_never 0.02098 *
## depressionW4b_yes 0.01738 *
## insurance_statusb_yes_insurance 0.82110
## general_healthpoor/bad 0.36002
## mentorb_yes 0.05672 .
## volunteerb_yes 0.49282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sexorientb_bisexual 0.4630 2.15969 0.07531 2.8467
## sexorientc_LGB 2.4141 0.41422 1.00732 5.7857
## racethnicb-nhblack 0.5773 1.73221 0.20385 1.6349
## racethnicc-hispanic 14.2681 0.07009 7.21373 28.2210
## racethnicd-asian 38.5894 0.02591 20.96578 71.0273
## racethnice-native_american 1.8664 0.53580 0.19564 17.8041
## racethnicf-other 7.9482 0.12582 2.15506 29.3140
## educb_highschool_grad 1.6520 0.60533 0.64317 4.2432
## educc_college_bach 2.4998 0.40004 0.92907 6.7258
## educd_college+ 4.6102 0.21691 1.59359 13.3370
## incomeW4b_$15,000<$30,000 4.2621 0.23463 1.51032 12.0275
## incomeW4c_$30,000<$50,000 2.6115 0.38292 1.00792 6.7665
## incomeW4d_$50,000<$75,000 3.9143 0.25548 1.35845 11.2787
## incomeW4e_$75,000<$100,000 4.6414 0.21545 1.63252 13.1959
## incomeW4f_$100,000<$150,000 4.4398 0.22524 1.58441 12.4411
## incomeW4g_$150,000+ 2.8078 0.35615 1.21563 6.4852
## marriedW4ab_married 0.5663 1.76596 0.37813 0.8480
## friendshipsb_1or2 0.7275 1.37448 0.24379 2.1712
## friendshipsc_3to9 0.5847 1.71020 0.23346 1.4645
## friendshipsd_>10 0.4451 2.24669 0.14818 1.3370
## church.attendb_once.week 1.5695 0.63714 0.94560 2.6051
## church.attendc_2plus.week 0.9075 1.10194 0.42584 1.9339
## unloved_bycaretakerW4b_10+_times 0.8091 1.23591 0.49682 1.3177
## unloved_bycaretakerW4c_never 0.6366 1.57080 0.43387 0.9341
## depressionW4b_yes 0.4503 2.22092 0.23331 0.8690
## insurance_statusb_yes_insurance 1.0573 0.94582 0.65241 1.7134
## general_healthpoor/bad 1.3454 0.74327 0.71276 2.5396
## mentorb_yes 0.6474 1.54457 0.41398 1.0125
## volunteerb_yes 1.1163 0.89579 0.81510 1.5289
##
## Concordance= 0.864 (se = 0.018 )
## Likelihood ratio test= NA on 29 df, p=NA
## Wald test = 436.3 on 29 df, p=<2e-16
## Score (logrank) test = NA on 29 df, p=NA