vac_comp_all
model <- glm(vac_comp_all ~factor(new_class)+sexident3+race+age, family = binomial(link = "probit"), data = merged_data)
summary(model)
##
## Call:
## glm(formula = vac_comp_all ~ factor(new_class) + sexident3 +
## race + age, family = binomial(link = "probit"), data = merged_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.92036 0.69103 -2.779 0.00545 **
## factor(new_class)1 -0.30085 0.37233 -0.808 0.41909
## factor(new_class)2 0.35168 0.37742 0.932 0.35144
## factor(new_class)3 0.19203 0.35975 0.534 0.59349
## sexident3 -0.11792 0.09268 -1.272 0.20324
## race -0.06161 0.03028 -2.034 0.04192 *
## age 0.03073 0.02527 1.216 0.22396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 647.77 on 1221 degrees of freedom
## Residual deviance: 621.46 on 1215 degrees of freedom
## (5 observations deleted due to missingness)
## AIC: 635.46
##
## Number of Fisher Scoring iterations: 6
odds_ratios <- exp(coef(model))
print(odds_ratios)
## (Intercept) factor(new_class)1 factor(new_class)2 factor(new_class)3
## 0.1465544 0.7401922 1.4214562 1.2117103
## sexident3 race age
## 0.8887632 0.9402535 1.0312042
exp(cbind(OR = coef(model), confint(model)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.1465544 0.03604343 0.5584559
## factor(new_class)1 0.7401922 0.37590092 1.6551975
## factor(new_class)2 1.4214562 0.71371803 3.2059712
## factor(new_class)3 1.2117103 0.63429105 2.6575621
## sexident3 0.8887632 0.73723757 1.0614893
## race 0.9402535 0.88409909 0.9961046
## age 1.0312042 0.98146154 1.0846031
model <- glm(vac_comp_all ~factor(classtwo)+sexident3+race+age,
family = binomial(link = "probit"), data = merged_data)
summary(model)
##
## Call:
## glm(formula = vac_comp_all ~ factor(classtwo) + sexident3 + race +
## age, family = binomial(link = "probit"), data = merged_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.17152 0.59429 -3.654 0.000258 ***
## factor(classtwo)1 0.50724 0.14030 3.615 0.000300 ***
## factor(classtwo)2 0.54515 0.16342 3.336 0.000850 ***
## sexident3 -0.11904 0.09270 -1.284 0.199072
## race -0.06288 0.03022 -2.080 0.037497 *
## age 0.02869 0.02517 1.140 0.254337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 647.77 on 1221 degrees of freedom
## Residual deviance: 622.88 on 1216 degrees of freedom
## (5 observations deleted due to missingness)
## AIC: 634.88
##
## Number of Fisher Scoring iterations: 6
odds_ratios <- exp(coef(model))
print(odds_ratios)
## (Intercept) factor(classtwo)1 factor(classtwo)2 sexident3
## 0.1140037 1.6606945 1.7248751 0.8877727
## race age
## 0.9390589 1.0291097
exp(cbind(OR = coef(model), confint(model)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.1140037 0.03463712 0.3633420
## factor(classtwo)1 1.6606945 1.26945383 2.2042810
## factor(classtwo)2 1.7248751 1.25491718 2.3859373
## sexident3 0.8877727 0.73637578 1.0603393
## race 0.9390589 0.88306612 0.9947287
## age 1.0291097 0.97966908 1.0821213
vac_init_all
model <- glm(vac_init_all ~factor(new_class)+sexident3+race+age, family = binomial(link = "probit"), data = merged_data)
summary(model)
##
## Call:
## glm(formula = vac_init_all ~ factor(new_class) + sexident3 +
## race + age, family = binomial(link = "probit"), data = merged_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.874484 0.476255 -1.836 0.0663 .
## factor(new_class)1 0.264528 0.269531 0.981 0.3264
## factor(new_class)2 0.580122 0.281583 2.060 0.0394 *
## factor(new_class)3 0.467867 0.266368 1.756 0.0790 .
## sexident3 -0.057956 0.059964 -0.967 0.3338
## race -0.027697 0.018840 -1.470 0.1415
## age 0.007293 0.017000 0.429 0.6679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1542.4 on 1221 degrees of freedom
## Residual deviance: 1527.5 on 1215 degrees of freedom
## (5 observations deleted due to missingness)
## AIC: 1541.5
##
## Number of Fisher Scoring iterations: 4
odds_ratios <- exp(coef(model))
print(odds_ratios)
## (Intercept) factor(new_class)1 factor(new_class)2 factor(new_class)3
## 0.4170774 1.3028155 1.7862567 1.5965850
## sexident3 race age
## 0.9436913 0.9726831 1.0073195
exp(cbind(OR = coef(model), confint(model)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.4170774 0.1616093 1.058109
## factor(new_class)1 1.3028155 0.7798768 2.254622
## factor(new_class)2 1.7862567 1.0428225 3.160046
## factor(new_class)3 1.5965850 0.9621169 2.747146
## sexident3 0.9436913 0.8383069 1.061205
## race 0.9726831 0.9370776 1.009262
## age 1.0073195 0.9743462 1.041512
model <- glm(vac_init_all ~factor(classtwo)+sexident3+race+age, family = binomial(link = "probit"), data = merged_data)
summary(model)
##
## Call:
## glm(formula = vac_init_all ~ factor(classtwo) + sexident3 + race +
## age, family = binomial(link = "probit"), data = merged_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.568985 0.395505 -1.439 0.1503
## factor(classtwo)1 0.203705 0.085109 2.393 0.0167 *
## factor(classtwo)2 0.221161 0.106746 2.072 0.0383 *
## sexident3 -0.056179 0.059920 -0.938 0.3485
## race -0.028282 0.018788 -1.505 0.1322
## age 0.005398 0.016969 0.318 0.7504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1542.4 on 1221 degrees of freedom
## Residual deviance: 1532.0 on 1216 degrees of freedom
## (5 observations deleted due to missingness)
## AIC: 1544
##
## Number of Fisher Scoring iterations: 4
odds_ratios <- exp(coef(model))
print(odds_ratios)
## (Intercept) factor(classtwo)1 factor(classtwo)2 sexident3
## 0.5660996 1.2259363 1.2475238 0.9453696
## race age
## 0.9721141 1.0054130
exp(cbind(OR = coef(model), confint(model)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.5660996 0.2606018 1.226134
## factor(classtwo)1 1.2259363 1.0379249 1.449002
## factor(classtwo)2 1.2475238 1.0117580 1.537683
## sexident3 0.9453696 0.8399326 1.062936
## race 0.9721141 0.9366107 1.008586
## age 1.0054130 0.9725670 1.039462