load(file='/Users/meganwilliams/Desktop/Research/HANDLS Data/MeasurementData.rdata')
load(file='/Users/meganwilliams/Desktop/Research/HANDLS Data/MeasurementData1.rdata')
Model1.1 = lm(MCLcountry~Age0 + Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + sHealth, MeasurementData1,x=T)
summary(Model1.1)
##
## Call:
## lm(formula = MCLcountry ~ Age0 + Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + sHealth, data = MeasurementData1,
## x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.140 -1.178 -0.158 1.560 6.297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.97866 0.40125 12.41 < 2e-16
## Age0 0.00524 0.00453 1.16 0.24737
## Race1 -0.32980 0.08452 -3.90 9.8e-05
## Sex1 -0.00117 0.08281 -0.01 0.98876
## Educ 0.07142 0.01509 4.73 2.4e-06
## Employment011 0.13360 0.09489 1.41 0.15932
## acasiincomx01 -0.31395 0.09601 -3.27 0.00109
## Neighborhood02 -0.12898 0.04006 -3.22 0.00131
## CES -0.03618 0.00397 -9.12 < 2e-16
## sHealthLow -0.44790 0.11944 -3.75 0.00018
## sHealthMiddle -0.42484 0.09661 -4.40 1.2e-05
##
## Residual standard error: 1.86 on 2066 degrees of freedom
## Multiple R-squared: 0.142, Adjusted R-squared: 0.138
## F-statistic: 34.1 on 10 and 2066 DF, p-value: <2e-16
Model2.1 = lm(MCLcountry~Age0 + Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + sHealth + 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*sHealth + Race*sHealth + Sex*sHealth, MeasurementData1,x=T)
summary(Model2.1)
##
## Call:
## lm(formula = MCLcountry ~ Age0 + Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + sHealth + 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 * sHealth + Race * sHealth + Sex *
## sHealth, data = MeasurementData1, x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.413 -1.424 -0.649 1.316 6.773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.565137 0.649412 8.57 < 2e-16
## Age0 0.006380 0.004523 1.41 0.1586
## Race1 -0.858885 0.873598 -0.98 0.3256
## Sex1 -0.670226 0.893407 -0.75 0.4532
## Educ 0.017642 0.030588 0.58 0.5642
## Employment011 -0.233139 0.161373 -1.44 0.1487
## acasiincomx01 -0.314342 0.165193 -1.90 0.0572
## Neighborhood02 -0.016810 0.072750 -0.23 0.8173
## CES -0.040562 0.006568 -6.18 7.9e-10
## sHealthLow -0.352570 0.197977 -1.78 0.0751
## sHealthMiddle -0.481895 0.169186 -2.85 0.0044
## Race1:Sex1 -0.168538 1.293572 -0.13 0.8964
## Race1:Employment011 0.244699 0.250796 0.98 0.3293
## Sex1:Employment011 0.453590 0.242360 1.87 0.0614
## Race1:Educ 0.055437 0.041707 1.33 0.1839
## Sex1:Educ 0.038522 0.046257 0.83 0.4051
## Race1:acasiincomx01 0.020166 0.257829 0.08 0.9377
## Sex1:acasiincomx01 -0.014352 0.247607 -0.06 0.9538
## Race1:Neighborhood02 -0.160644 0.105584 -1.52 0.1283
## Sex1:Neighborhood02 -0.115465 0.109504 -1.05 0.2918
## Race1:CES 0.000757 0.009947 0.08 0.9393
## Sex1:CES 0.016829 0.010856 1.55 0.1213
## Race1:sHealthLow -0.283742 0.322573 -0.88 0.3792
## Race1:sHealthMiddle 0.209928 0.262201 0.80 0.4234
## Sex1:sHealthLow -0.264385 0.308139 -0.86 0.3910
## Sex1:sHealthMiddle -0.047221 0.252276 -0.19 0.8515
## Race1:Sex1:Employment011 0.396372 0.393441 1.01 0.3138
## Race1:Sex1:Educ 0.001965 0.061664 0.03 0.9746
## Race1:Sex1:acasiincomx01 -0.170436 0.400744 -0.43 0.6707
## Race1:Sex1:Neighborhood02 0.114077 0.162548 0.70 0.4829
## Race1:Sex1:CES -0.011883 0.016638 -0.71 0.4752
## Race1:Sex1:sHealthLow 0.823827 0.490850 1.68 0.0934
## Race1:Sex1:sHealthMiddle 0.146064 0.395698 0.37 0.7121
##
## Residual standard error: 1.84 on 2044 degrees of freedom
## Multiple R-squared: 0.162, Adjusted R-squared: 0.149
## F-statistic: 12.4 on 32 and 2044 DF, p-value: <2e-16
Model2.2 = lm(MCLcountry~Age0 + Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + Race * Educ + Race * Employment01 + Race * acasiincomx01 + Race * Neighborhood02 + Race * CES + Race*sHealth + Sex * Educ + Sex * Employment01 + Sex * acasiincomx01 + Sex * Neighborhood02 + Sex * CES + Sex*sHealth + Race * Sex, MeasurementData1, x=T)
summary(Model2.2)
##
## Call:
## lm(formula = MCLcountry ~ Age0 + Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + Race * Educ + Race *
## Employment01 + Race * acasiincomx01 + Race * Neighborhood02 +
## Race * CES + Race * sHealth + Sex * Educ + Sex * Employment01 +
## Sex * acasiincomx01 + Sex * Neighborhood02 + Sex * CES +
## Sex * sHealth + Race * Sex, data = MeasurementData1, x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.310 -1.138 -0.636 1.170 6.808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.667816 0.580837 9.76 < 2e-16
## Age0 0.006125 0.004508 1.36 0.1744
## Race1 -1.060060 0.650633 -1.63 0.1034
## Sex1 -0.837575 0.647246 -1.29 0.1958
## Educ 0.016552 0.026438 0.63 0.5313
## Employment011 -0.290292 0.146822 -1.98 0.0482
## acasiincomx01 -0.290572 0.150100 -1.94 0.0530
## Neighborhood02 -0.037581 0.065004 -0.58 0.5632
## CES -0.039034 0.006025 -6.48 1.2e-10
## sHealthLow -0.470297 0.181062 -2.60 0.0095
## sHealthMiddle -0.497687 0.152459 -3.26 0.0011
## Race1:Educ 0.058689 0.030654 1.91 0.0557
## Race1:Employment011 0.401558 0.192529 2.09 0.0371
## Race1:acasiincomx01 -0.055197 0.196607 -0.28 0.7789
## Race1:Neighborhood02 -0.117662 0.079968 -1.47 0.1413
## Race1:CES -0.003719 0.007946 -0.47 0.6398
## Race1:sHealthLow 0.057775 0.242453 0.24 0.8117
## Race1:sHealthMiddle 0.267438 0.195653 1.37 0.1718
## Sex1:Educ 0.037076 0.030244 1.23 0.2204
## Sex1:Employment011 0.596623 0.190658 3.13 0.0018
## Sex1:acasiincomx01 -0.077974 0.194046 -0.40 0.6878
## Sex1:Neighborhood02 -0.062600 0.080614 -0.78 0.4375
## Sex1:CES 0.012379 0.008199 1.51 0.1313
## Sex1:sHealthLow 0.047624 0.238837 0.20 0.8420
## Sex1:sHealthMiddle 0.000622 0.193555 0.00 0.9974
## Race1:Sex1 0.314666 0.171512 1.83 0.0667
##
## Residual standard error: 1.84 on 2051 degrees of freedom
## Multiple R-squared: 0.16, Adjusted R-squared: 0.15
## F-statistic: 15.7 on 25 and 2051 DF, p-value: <2e-16
Model2.3 = lm(MCLcountry~Age0 + Race + Sex + Educ + Employment01 + acasiincomx01 + Neighborhood02 + CES + Race * Educ + Race * Employment01 + Sex * Employment01 , MeasurementData1,x=T)
summary(Model2.3)
##
## Call:
## lm(formula = MCLcountry ~ Age0 + Race + Sex + Educ + Employment01 +
## acasiincomx01 + Neighborhood02 + CES + Race * Educ + Race *
## Employment01 + Sex * Employment01, data = MeasurementData1,
## x = T)
##
## Residuals:
## MCL: SES Standing in country
## Min 1Q Median 3Q Max
## -5.331 -1.529 -0.438 1.408 6.786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.67638 0.44386 12.79 < 2e-16
## Age0 0.00398 0.00448 0.89 0.37444
## Race1 -1.61200 0.36084 -4.47 8.3e-06
## Sex1 -0.41459 0.12973 -3.20 0.00142
## Educ 0.02800 0.02246 1.25 0.21268
## Employment011 -0.31011 0.13651 -2.27 0.02320
## acasiincomx01 -0.33941 0.09571 -3.55 0.00040
## Neighborhood02 -0.14314 0.03964 -3.61 0.00031
## CES -0.03965 0.00387 -10.25 < 2e-16
## Race1:Educ 0.07804 0.02876 2.71 0.00671
## Race1:Employment011 0.49787 0.17430 2.86 0.00433
## Sex1:Employment011 0.67174 0.16805 4.00 6.6e-05
##
## Residual standard error: 1.85 on 2065 degrees of freedom
## Multiple R-squared: 0.147, Adjusted R-squared: 0.142
## F-statistic: 32.4 on 11 and 2065 DF, p-value: <2e-16
library(effects)
## Loading required package: colorspace
##
## Attaching package: 'effects'
##
## The following object is masked from 'package:car':
##
## Prestige
plot(effect("Sex:Employment01", Model2.3, list(wt=c(2.2,3.2,4.2))), multiline=TRUE)
plot(effect("Race:Employment01", Model2.3, list(wt=c(2.2,3.2,4.2))), multiline=TRUE)
plot(effect("Race:Educ", Model2.3, list(wt=c(2.2,3.2,4.2))), multiline=TRUE)
