## [1] "Average_CA" "High_CA" "Low_CA"
## Estimate SE t/z
## (Intercept) 0.016404613 0.004296955 3.8177297
## CA_GroupHigh_CA 0.013379548 0.013899663 0.9625808
## CA_GroupLow_CA -0.119798710 0.026736800 -4.4806676
## scale(max_age_MHQ) 0.071659724 0.003190524 22.4601730
## Sex 0.018447293 0.006156951 2.9961736
## I(scale(max_age_MHQ)^2) -0.019158947 0.002997828 -6.3909425
## CA_GroupHigh_CA:scale(max_age_MHQ) 0.002261923 0.010676606 0.2118579
## CA_GroupLow_CA:scale(max_age_MHQ) 0.069955643 0.019046507 3.6728857
## CA_GroupHigh_CA:Sex -0.047734029 0.020276751 -2.3541261
## CA_GroupLow_CA:Sex 0.010575502 0.037987288 0.2783958
## scale(max_age_MHQ):Sex -0.064411243 0.006151204 -10.4713228
## CA_GroupHigh_CA:I(scale(max_age_MHQ)^2) 0.008221997 0.009647441 0.8522464
## CA_GroupLow_CA:I(scale(max_age_MHQ)^2) -0.010879404 0.018271177 -0.5954408
## CA_GroupHigh_CA:scale(max_age_MHQ):Sex -0.008761748 0.020227681 -0.4331563
## CA_GroupLow_CA:scale(max_age_MHQ):Sex 0.019973116 0.037278297 0.5357840
## p Model
## (Intercept) 1.347519e-04 Well-being
## CA_GroupHigh_CA 3.357599e-01 Well-being
## CA_GroupLow_CA 7.447745e-06 Well-being
## scale(max_age_MHQ) 1.715919e-111 Well-being
## Sex 2.734456e-03 Well-being
## I(scale(max_age_MHQ)^2) 1.654583e-10 Well-being
## CA_GroupHigh_CA:scale(max_age_MHQ) 8.322183e-01 Well-being
## CA_GroupLow_CA:scale(max_age_MHQ) 2.399292e-04 Well-being
## CA_GroupHigh_CA:Sex 1.856789e-02 Well-being
## CA_GroupLow_CA:Sex 7.807090e-01 Well-being
## scale(max_age_MHQ):Sex 1.199672e-25 Well-being
## CA_GroupHigh_CA:I(scale(max_age_MHQ)^2) 3.940790e-01 Well-being
## CA_GroupLow_CA:I(scale(max_age_MHQ)^2) 5.515500e-01 Well-being
## CA_GroupHigh_CA:scale(max_age_MHQ):Sex 6.649020e-01 Well-being
## CA_GroupLow_CA:scale(max_age_MHQ):Sex 5.921088e-01 Well-being
## `geom_smooth()` using formula 'y ~ x'
## [1] "Median max age = 65"
## Estimate
## (Intercept) -0.0249063606
## CA_GroupHigh_CA 0.0175026153
## CA_GroupLow_CA -0.2265603563
## Sex 0.0688789390
## scale(max_age_MHQ, scale = FALSE) 0.0129550207
## I(scale((max_age_MHQ), scale = FALSE)^2) 0.0008108164
## CA_GroupHigh_CA:Sex -0.0464351426
## CA_GroupLow_CA:Sex 0.0296300479
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 0.0005061278
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) 0.0098426114
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0001305262
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0012418134
## SE
## (Intercept) 0.0062597060
## CA_GroupHigh_CA 0.0204373669
## CA_GroupLow_CA 0.0379318390
## Sex 0.0086388550
## scale(max_age_MHQ, scale = FALSE) 0.0008794176
## I(scale((max_age_MHQ), scale = FALSE)^2) 0.0001785870
## CA_GroupHigh_CA:Sex 0.0274840984
## CA_GroupLow_CA:Sex 0.0526365675
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 0.0028473113
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) 0.0053620085
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0005713422
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0010981322
## t/z
## (Intercept) -3.9788387
## CA_GroupHigh_CA 0.8564027
## CA_GroupLow_CA -5.9728282
## Sex 7.9731561
## scale(max_age_MHQ, scale = FALSE) 14.7313647
## I(scale((max_age_MHQ), scale = FALSE)^2) 4.5401772
## CA_GroupHigh_CA:Sex -1.6895276
## CA_GroupLow_CA:Sex 0.5629176
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 0.1777564
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) 1.8356202
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.2284554
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 1.1308415
## p
## (Intercept) 6.932935e-05
## CA_GroupHigh_CA 3.917783e-01
## CA_GroupLow_CA 2.344087e-09
## Sex 1.572241e-15
## scale(max_age_MHQ, scale = FALSE) 4.884576e-49
## I(scale((max_age_MHQ), scale = FALSE)^2) 5.630982e-06
## CA_GroupHigh_CA:Sex 9.112327e-02
## CA_GroupLow_CA:Sex 5.734930e-01
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 8.589148e-01
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) 6.641850e-02
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 8.192930e-01
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 2.581261e-01
## Model
## (Intercept) Well-being
## CA_GroupHigh_CA Well-being
## CA_GroupLow_CA Well-being
## Sex Well-being
## scale(max_age_MHQ, scale = FALSE) Well-being
## I(scale((max_age_MHQ), scale = FALSE)^2) Well-being
## CA_GroupHigh_CA:Sex Well-being
## CA_GroupLow_CA:Sex Well-being
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) Well-being
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) Well-being
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) Well-being
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) Well-being
## `geom_smooth()` using formula 'y ~ x'
## [1] "Median max age = 65"
## Estimate
## (Intercept) 0.0117226317
## CA_GroupHigh_CA 0.0156112614
## CA_GroupLow_CA -0.0527064339
## Sex -0.0414844170
## scale(max_age_MHQ, scale = FALSE) 0.0004186762
## I(scale((max_age_MHQ), scale = FALSE)^2) -0.0011500145
## CA_GroupHigh_CA:Sex -0.0468037129
## CA_GroupLow_CA:Sex -0.0149158392
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 0.0041912220
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) -0.0096068919
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0006815719
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0008995977
## SE
## (Intercept) 0.0060661949
## CA_GroupHigh_CA 0.0208312196
## CA_GroupLow_CA 0.0373263926
## Sex 0.0088118034
## scale(max_age_MHQ, scale = FALSE) 0.0014737209
## I(scale((max_age_MHQ), scale = FALSE)^2) 0.0004099812
## CA_GroupHigh_CA:Sex 0.0301085138
## CA_GroupLow_CA:Sex 0.0551070063
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 0.0049045748
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) 0.0091155642
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0013427996
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.0024860874
## t/z
## (Intercept) 1.9324522
## CA_GroupHigh_CA 0.7494166
## CA_GroupLow_CA -1.4120420
## Sex -4.7078237
## scale(max_age_MHQ, scale = FALSE) 0.2840946
## I(scale((max_age_MHQ), scale = FALSE)^2) -2.8050421
## CA_GroupHigh_CA:Sex -1.5545009
## CA_GroupLow_CA:Sex -0.2706705
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 0.8545536
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) -1.0538999
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.5075753
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 0.3618528
## p
## (Intercept) 5.330855e-02
## CA_GroupHigh_CA 4.536092e-01
## CA_GroupLow_CA 1.579429e-01
## Sex 2.509499e-06
## scale(max_age_MHQ, scale = FALSE) 7.763389e-01
## I(scale((max_age_MHQ), scale = FALSE)^2) 5.032665e-03
## CA_GroupHigh_CA:Sex 1.200704e-01
## CA_GroupLow_CA:Sex 7.866454e-01
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) 3.928018e-01
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) 2.919331e-01
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 6.117531e-01
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) 7.174633e-01
## Model
## (Intercept) Well-being
## CA_GroupHigh_CA Well-being
## CA_GroupLow_CA Well-being
## Sex Well-being
## scale(max_age_MHQ, scale = FALSE) Well-being
## I(scale((max_age_MHQ), scale = FALSE)^2) Well-being
## CA_GroupHigh_CA:Sex Well-being
## CA_GroupLow_CA:Sex Well-being
## CA_GroupHigh_CA:scale(max_age_MHQ, scale = FALSE) Well-being
## CA_GroupLow_CA:scale(max_age_MHQ, scale = FALSE) Well-being
## CA_GroupHigh_CA:I(scale((max_age_MHQ), scale = FALSE)^2) Well-being
## CA_GroupLow_CA:I(scale((max_age_MHQ), scale = FALSE)^2) Well-being
## `geom_smooth()` using formula 'y ~ x'
## Estimate SE t/z p
## (Intercept) 0.01519169 0.004296898 3.535503 4.071696e-04
## scale(max_age_MHQ) 0.07140002 0.003194698 22.349540 2.172925e-110
## Sex 0.01822676 0.006159386 2.959184 3.085215e-03
## I(scale(max_age_MHQ)^2) -0.01910673 0.002994769 -6.380034 1.777614e-10
## scale(max_age_MHQ):Sex -0.06428339 0.006149491 -10.453449 1.453161e-25
## Model
## (Intercept) Well-being
## scale(max_age_MHQ) Well-being
## Sex Well-being
## I(scale(max_age_MHQ)^2) Well-being
## scale(max_age_MHQ):Sex Well-being
## [1] "beta = 0.0714000224485863 SE = 0.00319469763176148 OR = 1.07401076836668 p = 2.1729246786779e-110"
## Estimate SE t/z p
## (Intercept) 0.02359570 0.02618383 0.9011557 3.675786e-01
## scale(max_age_MHQ) 0.12393633 0.01903355 6.5114684 8.706314e-11
## Sex 0.02448490 0.03729585 0.6565047 5.115503e-01
## I(scale(max_age_MHQ)^2) -0.02675916 0.01817421 -1.4723698 1.410270e-01
## scale(max_age_MHQ):Sex -0.03933544 0.03683905 -1.0677648 2.857134e-01
## Model
## (Intercept) Well-being
## scale(max_age_MHQ) Well-being
## Sex Well-being
## I(scale(max_age_MHQ)^2) Well-being
## scale(max_age_MHQ):Sex Well-being
## [1] "beta = 0.12393633373694 SE = 0.019033546180348 OR = 1.13194380207315 p = 8.70631384130346e-11"