load(file='/Users/meganwilliams/Desktop/Research/HANDLS Data/MeasurementData.rdata')
load(file='/Users/meganwilliams/Desktop/Research/HANDLS Data/MeasurementData1.rdata')
Model1.1 = lm(MCLcountry~Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + sHealthNum, MeasurementData1,x=T)
summary(Model1.1)
##
## Call:
## lm(formula = MCLcountry ~ Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + sHealthNum, data = MeasurementData1,
## x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.02 -1.10 0.12 1.24 6.34
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.46492 0.34868 12.81 < 2e-16
## Race1 -0.32012 0.08453 -3.79 0.00016
## Sex1 0.00116 0.08290 0.01 0.98882
## Educ 0.07244 0.01510 4.80 1.7e-06
## Employment011 0.09868 0.09382 1.05 0.29304
## acasiincomx01 -0.30926 0.09608 -3.22 0.00131
## Neighborhood02 -0.13306 0.04003 -3.32 0.00090
## CES -0.03624 0.00394 -9.19 < 2e-16
## sHealthNum 0.23306 0.05874 3.97 7.5e-05
##
## Residual standard error: 1.86 on 2068 degrees of freedom
## Multiple R-squared: 0.139, Adjusted R-squared: 0.135
## F-statistic: 41.7 on 8 and 2068 DF, p-value: <2e-16
Model2.1 = lm(MCLcountry~Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + sHealthNum + Race*Sex*Employment01 + Race*Employment01 + Sex*Employment01 + Race*Sex*Educ + Race*Educ + Sex*Educ + Race*Sex*acasiincomx01 + Race*acasiincomx01 + Sex*acasiincomx01 + Race*Sex*Neighborhood02 + Race*Neighborhood02 + Sex*Neighborhood02 + Race*Sex*CES + Race*CES + Sex*CES + Race*Sex + Race*Sex*sHealthNum + Race*sHealthNum + Sex*sHealthNum, MeasurementData1,x=T)
summary(Model2.1)
##
## Call:
## lm(formula = MCLcountry ~ Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + sHealthNum + Race *
## Sex * Employment01 + Race * Employment01 + Sex * Employment01 +
## Race * Sex * Educ + Race * Educ + Sex * Educ + Race * Sex *
## acasiincomx01 + Race * acasiincomx01 + Sex * acasiincomx01 +
## Race * Sex * Neighborhood02 + Race * Neighborhood02 + Sex *
## Neighborhood02 + Race * Sex * CES + Race * CES + Sex * CES +
## Race * Sex + Race * Sex * sHealthNum + Race * sHealthNum +
## Sex * sHealthNum, data = MeasurementData1, x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.219 -1.256 0.957 1.707 6.782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.228184 0.647223 8.08 1.1e-15
## Race1 -1.092569 0.936281 -1.17 0.243
## Sex1 -1.071636 0.951077 -1.13 0.260
## Educ 0.014546 0.030565 0.48 0.634
## Employment011 -0.269148 0.160750 -1.67 0.094
## acasiincomx01 -0.285510 0.164538 -1.74 0.083
## Neighborhood02 -0.020615 0.072775 -0.28 0.777
## CES -0.040907 0.006560 -6.24 5.5e-10
## sHealthNum 0.184586 0.098449 1.87 0.061
## Race1:Sex1 0.994944 1.386640 0.72 0.473
## Race1:Employment011 0.285723 0.247827 1.15 0.249
## Sex1:Employment011 0.452684 0.241926 1.87 0.061
## Race1:Educ 0.057420 0.041631 1.38 0.168
## Sex1:Educ 0.045305 0.046237 0.98 0.327
## Race1:acasiincomx01 -0.002442 0.257137 -0.01 0.992
## Sex1:acasiincomx01 -0.030215 0.247132 -0.12 0.903
## Race1:Neighborhood02 -0.162155 0.105634 -1.54 0.125
## Sex1:Neighborhood02 -0.122386 0.109517 -1.12 0.264
## Race1:CES 0.000337 0.009914 0.03 0.973
## Sex1:CES 0.016772 0.010865 1.54 0.123
## Race1:sHealthNum 0.119439 0.160208 0.75 0.456
## Sex1:sHealthNum 0.131014 0.152698 0.86 0.391
## Race1:Sex1:Employment011 0.351025 0.390795 0.90 0.369
## Race1:Sex1:Educ -0.004484 0.061595 -0.07 0.942
## Race1:Sex1:acasiincomx01 -0.165671 0.400278 -0.41 0.679
## Race1:Sex1:Neighborhood02 0.122944 0.162485 0.76 0.449
## Race1:Sex1:CES -0.011682 0.016620 -0.70 0.482
## Race1:Sex1:sHealthNum -0.389633 0.243209 -1.60 0.109
##
## Residual standard error: 1.84 on 2049 degrees of freedom
## Multiple R-squared: 0.159, Adjusted R-squared: 0.148
## F-statistic: 14.3 on 27 and 2049 DF, p-value: <2e-16
Model2.2 = lm(MCLcountry~Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + sHealthNum + Race * Educ + Race * Employment01 + Race * acasiincomx01 + Race * Neighborhood02 + Race * CES + Race*sHealthNum + Sex * Educ + Sex * Employment01 + Sex * acasiincomx01 + Sex * Neighborhood02 + Sex * CES + Sex*sHealthNum + Race * Sex, MeasurementData1, x=T)
summary(Model2.2)
##
## Call:
## lm(formula = MCLcountry ~ Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + sHealthNum + Race *
## Educ + Race * Employment01 + Race * acasiincomx01 + Race *
## Neighborhood02 + Race * CES + Race * sHealthNum + Sex * Educ +
## Sex * Employment01 + Sex * acasiincomx01 + Sex * Neighborhood02 +
## Sex * CES + Sex * sHealthNum + Race * Sex, data = MeasurementData1,
## x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.134 -1.247 -0.206 1.710 6.793
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.13934 0.57245 8.98 < 2e-16
## Race1 -0.81851 0.69645 -1.18 0.2400
## Sex1 -0.78632 0.68989 -1.14 0.2545
## Educ 0.01610 0.02644 0.61 0.5426
## Employment011 -0.32036 0.14635 -2.19 0.0287
## acasiincomx01 -0.26618 0.14974 -1.78 0.0756
## Neighborhood02 -0.04357 0.06498 -0.67 0.5026
## CES -0.03931 0.00602 -6.53 8.2e-11
## sHealthNum 0.24235 0.08980 2.70 0.0070
## Race1:Educ 0.05774 0.03059 1.89 0.0593
## Race1:Employment011 0.41807 0.19131 2.19 0.0290
## Race1:acasiincomx01 -0.07870 0.19635 -0.40 0.6886
## Race1:Neighborhood02 -0.11352 0.08003 -1.42 0.1562
## Race1:CES -0.00387 0.00793 -0.49 0.6255
## Race1:sHealthNum -0.04253 0.12017 -0.35 0.7234
## Sex1:Educ 0.03883 0.03025 1.28 0.1994
## Sex1:Employment011 0.58096 0.18985 3.06 0.0022
## Sex1:acasiincomx01 -0.08718 0.19401 -0.45 0.6532
## Sex1:Neighborhood02 -0.06504 0.08062 -0.81 0.4199
## Sex1:CES 0.01221 0.00820 1.49 0.1366
## Sex1:sHealthNum -0.01769 0.11837 -0.15 0.8812
## Race1:Sex1 0.32938 0.17117 1.92 0.0545
##
## Residual standard error: 1.84 on 2055 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.148
## F-statistic: 18.2 on 21 and 2055 DF, p-value: <2e-16
Model2.3 = lm(MCLcountry~Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + sHealthNum + Race * Educ + Race * Employment01 + Sex * Employment01 + Race * Sex , MeasurementData1,x=T)
summary(Model2.3)
##
## Call:
## lm(formula = MCLcountry ~ Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + sHealthNum + Race *
## Educ + Race * Employment01 + Sex * Employment01 + Race *
## Sex, data = MeasurementData1, x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.113 -1.178 0.299 1.976 6.672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.41746 0.40985 13.22 < 2e-16
## Race1 -1.66290 0.36574 -4.55 5.8e-06
## Sex1 -0.54115 0.14325 -3.78 0.00016
## Educ 0.02623 0.02237 1.17 0.24116
## Employment011 -0.33917 0.13621 -2.49 0.01285
## acasiincomx01 -0.32854 0.09542 -3.44 0.00059
## Neighborhood02 -0.12586 0.03980 -3.16 0.00159
## CES -0.03680 0.00392 -9.38 < 2e-16
## sHealthNum 0.21275 0.05849 3.64 0.00028
## Race1:Educ 0.07192 0.02865 2.51 0.01213
## Race1:Employment011 0.43764 0.17459 2.51 0.01227
## Sex1:Employment011 0.61675 0.16782 3.68 0.00024
## Race1:Sex1 0.38476 0.16706 2.30 0.02138
##
## Residual standard error: 1.84 on 2064 degrees of freedom
## Multiple R-squared: 0.154, Adjusted R-squared: 0.149
## F-statistic: 31.3 on 12 and 2064 DF, p-value: <2e-16
## Loading required package: colorspace
##
## Attaching package: 'effects'
##
## The following object is masked from 'package:car':
##
## Prestige
##
## Attaching package: 'psych'
##
## The following object is masked from 'package:Hmisc':
##
## describe
##
## The following object is masked from 'package:ggplot2':
##
## %+%
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## The following object is masked from 'package:car':
##
## logit
