library(pacman)
p_load(kirkegaard, haven, rms)
d = read_spss("Data 02072018.sav")
#SIRE gaps
GG_group_means(d, "g", "African_Am")
#just SIRE
rms::ols(g ~ Gender + African_Am + Asian_Am + Hisp_Latino + Native_Am + Pacific_Is + White + Other, data = d)
## Linear Regression Model
##
## rms::ols(formula = g ~ Gender + African_Am + Asian_Am + Hisp_Latino +
## Native_Am + Pacific_Is + White + Other, data = d)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 193 LR chi2 21.19 R2 0.104
## sigma0.8176 d.f. 8 R2 adj 0.065
## d.f. 184 Pr(> chi2) 0.0067 g 0.251
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.5204 -0.5139 -0.1359 0.5064 2.4469
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.8730 0.4493 -1.94 0.0535
## Gender 0.1364 0.1356 1.01 0.3157
## African_Am 0.4835 0.3941 1.23 0.2214
## Asian_Am 1.2329 0.8323 1.48 0.1403
## Hisp_Latino 0.6035 0.4028 1.50 0.1358
## Native_Am 0.2306 0.3158 0.73 0.4663
## Pacific_Is 0.3748 0.8323 0.45 0.6530
## White 0.3816 0.1676 2.28 0.0239
## Other -0.7618 0.8808 -0.86 0.3882
##
#just ancestry
rms::ols(g ~ Gender + Adj_Results_Africa + Adj_Results_Native + Adj_Results_Other, data = d)
## Frequencies of Missing Values Due to Each Variable
## g Gender Adj_Results_Africa
## 0 0 1
## Adj_Results_Native Adj_Results_Other
## 1 1
##
## Linear Regression Model
##
## rms::ols(formula = g ~ Gender + Adj_Results_Africa + Adj_Results_Native +
## Adj_Results_Other, data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 192 LR chi2 19.45 R2 0.096
## sigma0.8066 d.f. 4 R2 adj 0.077
## d.f. 187 Pr(> chi2) 0.0006 g 0.294
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.6237 -0.5936 -0.1609 0.6156 2.1467
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.2503 0.2736 0.91 0.3614
## Gender 0.1477 0.1324 1.12 0.2661
## Adj_Results_Africa -0.6844 0.1871 -3.66 0.0003
## Adj_Results_Native -1.0037 0.2676 -3.75 0.0002
## Adj_Results_Other -0.9497 0.7438 -1.28 0.2033
##
#combine with some left out SIRE
rms::ols(g ~ Gender + African_Am + Hisp_Latino + Adj_Results_Africa + Adj_Results_Native + Adj_Results_Other, data = d)
## Frequencies of Missing Values Due to Each Variable
## g Gender African_Am
## 0 0 0
## Hisp_Latino Adj_Results_Africa Adj_Results_Native
## 0 1 1
## Adj_Results_Other
## 1
##
## Linear Regression Model
##
## rms::ols(formula = g ~ Gender + African_Am + Hisp_Latino + Adj_Results_Africa +
## Adj_Results_Native + Adj_Results_Other, data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 192 LR chi2 24.35 R2 0.119
## sigma0.8007 d.f. 6 R2 adj 0.091
## d.f. 185 Pr(> chi2) 0.0005 g 0.320
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.6350 -0.5849 -0.1254 0.5089 2.1601
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.5329 0.4493 -1.19 0.2372
## Gender 0.1246 0.1322 0.94 0.3472
## African_Am 0.7479 0.3926 1.90 0.0583
## Hisp_Latino 0.8083 0.3695 2.19 0.0299
## Adj_Results_Africa -0.6336 0.2793 -2.27 0.0244
## Adj_Results_Native -0.9715 0.2681 -3.62 0.0004
## Adj_Results_Other -0.9599 0.7397 -1.30 0.1960
##
#all
rms::ols(g ~ Gender + African_Am + Hisp_Latino + Native_Am + White + Adj_Results_Africa + Adj_Results_Native + Adj_Results_Other, data = d)
## Frequencies of Missing Values Due to Each Variable
## g Gender African_Am
## 0 0 0
## Hisp_Latino Native_Am White
## 0 0 0
## Adj_Results_Africa Adj_Results_Native Adj_Results_Other
## 1 1 1
##
## Linear Regression Model
##
## rms::ols(formula = g ~ Gender + African_Am + Hisp_Latino + Native_Am +
## White + Adj_Results_Africa + Adj_Results_Native + Adj_Results_Other,
## data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 192 LR chi2 26.71 R2 0.130
## sigma0.8001 d.f. 8 R2 adj 0.092
## d.f. 183 Pr(> chi2) 0.0008 g 0.330
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.5671 -0.5545 -0.1073 0.5165 2.2590
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.4025 0.4666 -0.86 0.3894
## Gender 0.0978 0.1333 0.73 0.4640
## African_Am 0.5582 0.4126 1.35 0.1777
## Hisp_Latino 0.6172 0.3917 1.58 0.1169
## Native_Am 0.2869 0.2953 0.97 0.3325
## White 0.1864 0.1792 1.04 0.2996
## Adj_Results_Africa -0.5155 0.3006 -1.71 0.0881
## Adj_Results_Native -0.9074 0.2918 -3.11 0.0022
## Adj_Results_Other -0.8293 0.7452 -1.11 0.2672
##