> > ############### Build Models, Raw weighted data, Logistic regression###################################################################################################################################### > > ### Equation > nhc <- svydesign(id=~SDMVPSU, weights=~WTSA2YR, strata=~SDMVSTRA, nest=TRUE, survey.lonely.psu = "adjust", data= DF) > str(nhc) List of 9 $ cluster :'data.frame': 558 obs. of 1 variable: ..$ SDMVPSU: Factor w/ 30 levels "119.1","119.2",..: 13 13 25 16 13 16 5 11 3 8 ... ..- attr(*, "terms")=Classes 'terms', 'formula' language ~SDMVPSU .. .. ..- attr(*, "variables")= language list(SDMVPSU) .. .. ..- attr(*, "factors")= int [1, 1] 1 .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. ..$ : chr "SDMVPSU" .. .. .. .. ..$ : chr "SDMVPSU" .. .. ..- attr(*, "term.labels")= chr "SDMVPSU" .. .. ..- attr(*, "order")= int 1 .. .. ..- attr(*, "intercept")= int 1 .. .. ..- attr(*, "response")= int 0 .. .. ..- attr(*, ".Environment")= .. .. ..- attr(*, "predvars")= language list(SDMVPSU) .. .. ..- attr(*, "dataClasses")= Named chr "factor" .. .. .. ..- attr(*, "names")= chr "SDMVPSU" $ strata :'data.frame': 558 obs. of 1 variable: ..$ SDMVSTRA: num [1:558] 125 125 131 126 125 126 121 124 120 122 ... ..- attr(*, "terms")=Classes 'terms', 'formula' language ~SDMVSTRA .. .. ..- attr(*, "variables")= language list(SDMVSTRA) .. .. ..- attr(*, "factors")= int [1, 1] 1 .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. ..$ : chr "SDMVSTRA" .. .. .. .. ..$ : chr "SDMVSTRA" .. .. ..- attr(*, "term.labels")= chr "SDMVSTRA" .. .. ..- attr(*, "order")= int 1 .. .. ..- attr(*, "intercept")= int 1 .. .. ..- attr(*, "response")= int 0 .. .. ..- attr(*, ".Environment")= .. .. ..- attr(*, "predvars")= language list(SDMVSTRA) .. .. ..- attr(*, "dataClasses")= Named chr "numeric" .. .. .. ..- attr(*, "names")= chr "SDMVSTRA" $ has.strata: logi TRUE $ prob : Named num [1:558] 2.39e-06 1.43e-05 3.30e-05 8.89e-06 1.77e-05 ... ..- attr(*, "names")= chr [1:558] "1" "2" "3" "4" ... $ allprob :'data.frame': 558 obs. of 1 variable: ..$ WTSA2YR: num [1:558] 2.39e-06 1.43e-05 3.30e-05 8.89e-06 1.77e-05 ... $ call : language svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) $ variables :'data.frame': 558 obs. of 11 variables: ..$ Folate : Factor w/ 2 levels "Excessive","Normal": 1 2 2 2 1 2 2 2 2 1 ... ..$ Gender : Factor w/ 2 levels "Female","Male": 2 2 1 1 1 2 2 1 1 1 ... ..$ Age : Factor w/ 4 levels "Elderly","Late Age",..: 2 3 2 3 4 2 3 4 2 3 ... ..$ Ethnicity : Factor w/ 6 levels "Asian","Black",..: 6 6 6 2 3 2 6 5 5 1 ... ..$ BMI : Factor w/ 6 levels "Normal Weight",..: 5 2 4 1 5 2 5 5 3 1 ... ..$ HSCRP : Factor w/ 2 levels "High Risk","Low or Moderate Risk": 2 2 1 2 1 2 1 2 1 2 ... ..$ Serum_Zinc : Factor w/ 2 levels "Deficient","Normal": 2 2 1 2 2 2 2 2 2 2 ... ..$ Alcohol_Consumption: Factor w/ 3 levels "Excessive Consumption",..: 3 1 3 3 3 2 3 2 1 3 ... ..$ SDMVPSU : Factor w/ 2 levels "1","2": 1 1 1 2 1 2 1 1 1 2 ... ..$ SDMVSTRA : num [1:558] 125 125 131 126 125 126 121 124 120 122 ... ..$ WTSA2YR : num [1:558] 417814 69865 30315 112509 56483 ... ..- attr(*, "na.action")= 'omit' Named int [1:3230] 3 6 7 11 13 14 16 18 20 21 ... .. ..- attr(*, "names")= chr [1:3230] "3" "6" "7" "11" ... $ fpc :List of 2 ..$ popsize : NULL ..$ sampsize: int [1:558, 1] 2 2 2 2 2 2 2 2 2 2 ... ..- attr(*, "class")= chr "survey_fpc" $ pps : logi FALSE - attr(*, "class")= chr [1:2] "survey.design2" "survey.design" > #Summs > model1 <- svyglm(HSCRP ~ Serum_Zinc,design= nhc,rescale = TRUE, family = quasibinomial()) > summary(model1, df= degf(nhc)) Call: svyglm(formula = HSCRP ~ Serum_Zinc, design = nhc, family = quasibinomial(), rescale = TRUE) Survey design: svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2066 0.2371 0.872 0.397 Serum_ZincNormal 0.4134 0.2635 1.569 0.138 (Dispersion parameter for quasibinomial family taken to be 1.001795) Number of Fisher Scoring iterations: 4 > confint(model1) 2.5 % 97.5 % (Intercept) -0.3018735 0.7151504 Serum_ZincNormal -0.1517678 0.9785480 > summ(model1,confint=TRUE, n.sd=1, digits= 6) MODEL INFO: Observations: 558 Dependent Variable: HSCRP Type: Analysis of complex survey design Family: quasibinomial Link function: logit MODEL FIT: Pseudo-R² (Cragg-Uhler) = 0.010185 Pseudo-R² (McFadden) = 0.005672 AIC = NA ------------------------------------------------------------------------------ Est. 2.5% 97.5% t val. p ---------------------- ---------- ----------- ---------- ---------- ---------- (Intercept) 0.206638 -0.258053 0.671330 0.871554 0.398152 Serum_ZincNormal 0.413390 -0.103066 0.929847 1.568825 0.139008 ------------------------------------------------------------------------------ Estimated dispersion parameter = 1.001795 > > > #### Equation > model3 <- svyglm(HSCRP ~ factor(Folate) + factor(Gender) + factor(Age) + factor(Ethnicity) + factor(BMI) + factor(Serum_Zinc) + + factor(Alcohol_Consumption), design= nhc,rescale = TRUE, family = quasibinomial()) > ### Summs > summary (model3, df= degf(nhc)) Call: svyglm(formula = HSCRP ~ factor(Folate) + factor(Gender) + factor(Age) + factor(Ethnicity) + factor(BMI) + factor(Serum_Zinc) + factor(Alcohol_Consumption), design = nhc, family = quasibinomial(), rescale = TRUE) Survey design: svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.69773 0.72118 0.967 0.3486 factor(Folate)Normal 0.32563 0.23457 1.388 0.1853 factor(Gender)Male 0.11907 0.22230 0.536 0.6001 factor(Age)Late Age -0.79222 0.35342 -2.242 0.0405 * factor(Age)Middle Age -0.32829 0.39776 -0.825 0.4221 factor(Age)Young Adult -0.32342 0.36529 -0.885 0.3899 factor(Ethnicity)Black -0.18417 0.55364 -0.333 0.7440 factor(Ethnicity)Mexican American -0.81830 0.50286 -1.627 0.1245 factor(Ethnicity)Other -0.82201 0.81258 -1.012 0.3278 factor(Ethnicity)Other Hispanic -0.11463 0.43549 -0.263 0.7960 factor(Ethnicity)White 0.24946 0.42705 0.584 0.5678 factor(BMI)Obesity Class I 0.50317 0.48271 1.042 0.3137 factor(BMI)Obesity Class II 0.07437 0.42794 0.174 0.8644 factor(BMI)Obesity Class III -0.35399 0.55338 -0.640 0.5320 factor(BMI)Overweight -0.23689 0.35249 -0.672 0.5118 factor(BMI)Underweight -0.44811 0.29712 -1.508 0.1523 factor(Serum_Zinc)Normal 0.37158 0.25308 1.468 0.1627 factor(Alcohol_Consumption)Moderate Consumption -0.27245 0.28089 -0.970 0.3475 factor(Alcohol_Consumption)None or Low Consumption -0.02942 0.24892 -0.118 0.9075 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Zero or negative residual df; p-values not defined (Dispersion parameter for quasibinomial family taken to be 1.00033) Number of Fisher Scoring iterations: 4 > confint(model3, df= degf(nhc)) 2.5 % 97.5 % (Intercept) NaN NaN factor(Folate)Normal NaN NaN factor(Gender)Male NaN NaN factor(Age)Late Age NaN NaN factor(Age)Middle Age NaN NaN factor(Age)Young Adult NaN NaN factor(Ethnicity)Black NaN NaN factor(Ethnicity)Mexican American NaN NaN factor(Ethnicity)Other NaN NaN factor(Ethnicity)Other Hispanic NaN NaN factor(Ethnicity)White NaN NaN factor(BMI)Obesity Class I NaN NaN factor(BMI)Obesity Class II NaN NaN factor(BMI)Obesity Class III NaN NaN factor(BMI)Overweight NaN NaN factor(BMI)Underweight NaN NaN factor(Serum_Zinc)Normal NaN NaN factor(Alcohol_Consumption)Moderate Consumption NaN NaN factor(Alcohol_Consumption)None or Low Consumption NaN NaN > summ(model3,confint=TRUE, n.sd=1, digits= 6) MODEL INFO: Observations: 558 Dependent Variable: HSCRP Type: Analysis of complex survey design Family: quasibinomial Link function: logit MODEL FIT: Pseudo-R² (Cragg-Uhler) = 0.119937 Pseudo-R² (McFadden) = 0.069658 AIC = NA ------------------------------------------------------------------------------------------- Est. 2.5% 97.5% t val. p --------------------------------------- ----------- ----------- ----------- ----------- --- (Intercept) 0.697729 -0.715765 2.111223 0.967478 factor(Folate)Normal 0.325629 -0.134112 0.785370 1.388217 factor(Gender)Male 0.119070 -0.316627 0.554767 0.535631 factor(Age)Late Age -0.792223 -1.484915 -0.099531 -2.241585 factor(Age)Middle Age -0.328290 -1.107883 0.451304 -0.825348 factor(Age)Young Adult -0.323422 -1.039377 0.392534 -0.885383 factor(Ethnicity)Black -0.184167 -1.269278 0.900944 -0.332649 factor(Ethnicity)Mexican -0.818299 -1.803883 0.167284 -1.627297 American factor(Ethnicity)Other -0.822012 -2.414639 0.770614 -1.011609 factor(Ethnicity)Other -0.114635 -0.968173 0.738904 -0.263233 Hispanic factor(Ethnicity)White 0.249458 -0.587552 1.086468 0.584137 factor(BMI)Obesity Class I 0.503171 -0.442926 1.449268 1.042385 factor(BMI)Obesity Class 0.074368 -0.764380 0.913116 0.173781 II factor(BMI)Obesity Class -0.353995 -1.438606 0.730617 -0.639692 III factor(BMI)Overweight -0.236891 -0.927758 0.453977 -0.672050 factor(BMI)Underweight -0.448107 -1.030455 0.134241 -1.508160 factor(Serum_Zinc)Normal 0.371577 -0.124455 0.867609 1.468208 factor(Alcohol_Consumption)Moderate -0.272448 -0.822977 0.278081 -0.969955 Consumption factor(Alcohol_Consumption)None -0.029419 -0.517289 0.458451 -0.118188 or Low Consumption ------------------------------------------------------------------------------------------- Estimated dispersion parameter = 1.00033 > > #### Equation > model4 <- svyglm(HSCRP ~ relevel(Folate,"Excessive") + relevel(Gender, "Female") + relevel(Age,"Elderly") + relevel(Ethnicity, "Asian") + relevel(BMI,"Normal Weight") + relevel(Serum_Zinc,"Deficient") + + relevel(Alcohol_Consumption,"Excessive Consumption"), design= nhc,rescale = TRUE, family = quasibinomial()) > > ###Summs > summary (model4, df= degf(nhc)) Call: svyglm(formula = HSCRP ~ relevel(Folate, "Excessive") + relevel(Gender, "Female") + relevel(Age, "Elderly") + relevel(Ethnicity, "Asian") + relevel(BMI, "Normal Weight") + relevel(Serum_Zinc, "Deficient") + relevel(Alcohol_Consumption, "Excessive Consumption"), design = nhc, family = quasibinomial(), rescale = TRUE) Survey design: svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.69773 0.72118 0.967 0.3486 relevel(Folate, "Excessive")Normal 0.32563 0.23457 1.388 0.1853 relevel(Gender, "Female")Male 0.11907 0.22230 0.536 0.6001 relevel(Age, "Elderly")Late Age -0.79222 0.35342 -2.242 0.0405 * relevel(Age, "Elderly")Middle Age -0.32829 0.39776 -0.825 0.4221 relevel(Age, "Elderly")Young Adult -0.32342 0.36529 -0.885 0.3899 relevel(Ethnicity, "Asian")Black -0.18417 0.55364 -0.333 0.7440 relevel(Ethnicity, "Asian")Mexican American -0.81830 0.50286 -1.627 0.1245 relevel(Ethnicity, "Asian")Other -0.82201 0.81258 -1.012 0.3278 relevel(Ethnicity, "Asian")Other Hispanic -0.11463 0.43549 -0.263 0.7960 relevel(Ethnicity, "Asian")White 0.24946 0.42705 0.584 0.5678 relevel(BMI, "Normal Weight")Obesity Class I 0.50317 0.48271 1.042 0.3137 relevel(BMI, "Normal Weight")Obesity Class II 0.07437 0.42794 0.174 0.8644 relevel(BMI, "Normal Weight")Obesity Class III -0.35399 0.55338 -0.640 0.5320 relevel(BMI, "Normal Weight")Overweight -0.23689 0.35249 -0.672 0.5118 relevel(BMI, "Normal Weight")Underweight -0.44811 0.29712 -1.508 0.1523 relevel(Serum_Zinc, "Deficient")Normal 0.37158 0.25308 1.468 0.1627 relevel(Alcohol_Consumption, "Excessive Consumption")Moderate Consumption -0.27245 0.28089 -0.970 0.3475 relevel(Alcohol_Consumption, "Excessive Consumption")None or Low Consumption -0.02942 0.24892 -0.118 0.9075 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Zero or negative residual df; p-values not defined (Dispersion parameter for quasibinomial family taken to be 1.00033) Number of Fisher Scoring iterations: 4 > confint(model4, df= degf(nhc)) 2.5 % 97.5 % (Intercept) NaN NaN relevel(Folate, "Excessive")Normal NaN NaN relevel(Gender, "Female")Male NaN NaN relevel(Age, "Elderly")Late Age NaN NaN relevel(Age, "Elderly")Middle Age NaN NaN relevel(Age, "Elderly")Young Adult NaN NaN relevel(Ethnicity, "Asian")Black NaN NaN relevel(Ethnicity, "Asian")Mexican American NaN NaN relevel(Ethnicity, "Asian")Other NaN NaN relevel(Ethnicity, "Asian")Other Hispanic NaN NaN relevel(Ethnicity, "Asian")White NaN NaN relevel(BMI, "Normal Weight")Obesity Class I NaN NaN relevel(BMI, "Normal Weight")Obesity Class II NaN NaN relevel(BMI, "Normal Weight")Obesity Class III NaN NaN relevel(BMI, "Normal Weight")Overweight NaN NaN relevel(BMI, "Normal Weight")Underweight NaN NaN relevel(Serum_Zinc, "Deficient")Normal NaN NaN relevel(Alcohol_Consumption, "Excessive Consumption")Moderate Consumption NaN NaN relevel(Alcohol_Consumption, "Excessive Consumption")None or Low Consumption NaN NaN > summ(model4,confint=TRUE, n.sd=1, digits= 6) MODEL INFO: Observations: 558 Dependent Variable: HSCRP Type: Analysis of complex survey design Family: quasibinomial Link function: logit MODEL FIT: Pseudo-R² (Cragg-Uhler) = 0.119937 Pseudo-R² (McFadden) = 0.069658 AIC = NA ------------------------------------------------------------------------------------ Est. 2.5% 97.5% t val. p -------------------------------- ----------- ----------- ----------- ----------- --- (Intercept) 0.697729 -0.715765 2.111223 0.967478 relevel(Folate, 0.325629 -0.134112 0.785370 1.388217 "Excessive")Normal relevel(Gender, 0.119070 -0.316627 0.554767 0.535631 "Female")Male relevel(Age, "Elderly")Late -0.792223 -1.484915 -0.099531 -2.241585 Age relevel(Age, -0.328290 -1.107883 0.451304 -0.825348 "Elderly")Middle Age relevel(Age, "Elderly")Young -0.323422 -1.039377 0.392534 -0.885383 Adult relevel(Ethnicity, -0.184167 -1.269278 0.900944 -0.332649 "Asian")Black relevel(Ethnicity, -0.818299 -1.803883 0.167284 -1.627297 "Asian")Mexican American relevel(Ethnicity, -0.822012 -2.414639 0.770614 -1.011609 "Asian")Other relevel(Ethnicity, -0.114635 -0.968173 0.738904 -0.263233 "Asian")Other Hispanic relevel(Ethnicity, 0.249458 -0.587552 1.086468 0.584137 "Asian")White relevel(BMI, "Normal 0.503171 -0.442926 1.449268 1.042385 Weight")Obesity Class I relevel(BMI, "Normal 0.074368 -0.764380 0.913116 0.173781 Weight")Obesity Class II relevel(BMI, "Normal -0.353995 -1.438606 0.730617 -0.639692 Weight")Obesity Class III relevel(BMI, "Normal -0.236891 -0.927758 0.453977 -0.672050 Weight")Overweight relevel(BMI, "Normal -0.448107 -1.030455 0.134241 -1.508160 Weight")Underweight relevel(Serum_Zinc, 0.371577 -0.124455 0.867609 1.468208 "Deficient")Normal relevel(Alcohol_Consumption, -0.272448 -0.822977 0.278081 -0.969955 "Excessive Consumption")Moderate Consumption relevel(Alcohol_Consumption, -0.029419 -0.517289 0.458451 -0.118188 "Excessive Consumption")None or Low Consumption ------------------------------------------------------------------------------------ Estimated dispersion parameter = 1.00033 > > > ### Equation > > model5 <- svyglm(HSCRP ~ factor(Folate) + factor(Gender) + factor(Ethnicity) + factor(Serum_Zinc)* + factor(Alcohol_Consumption), design= nhc,rescale = TRUE, family = quasibinomial()) > ### Summs > summary (model5, df= degf(nhc)) Call: svyglm(formula = HSCRP ~ factor(Folate) + factor(Gender) + factor(Ethnicity) + factor(Serum_Zinc) * factor(Alcohol_Consumption), design = nhc, family = quasibinomial(), rescale = TRUE) Survey design: svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.4928 0.6851 0.719 0.4830 factor(Folate)Normal 0.3477 0.2485 1.399 0.1820 factor(Gender)Male 0.2221 0.2368 0.938 0.3631 factor(Ethnicity)Black -0.1575 0.5165 -0.305 0.7646 factor(Ethnicity)Mexican American -0.8509 0.5179 -1.643 0.1212 factor(Ethnicity)Other -0.7942 0.7551 -1.052 0.3095 factor(Ethnicity)Other Hispanic -0.1381 0.4730 -0.292 0.7744 factor(Ethnicity)White 0.3410 0.4310 0.791 0.4412 factor(Serum_Zinc)Normal -0.1646 0.5726 -0.288 0.7777 factor(Alcohol_Consumption)Moderate Consumption -0.8996 0.4373 -2.057 0.0575 . factor(Alcohol_Consumption)None or Low Consumption -0.4981 0.5519 -0.903 0.3810 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate Consumption 0.9081 0.7157 1.269 0.2238 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None or Low Consumption 0.6325 0.6744 0.938 0.3632 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for quasibinomial family taken to be 0.9964218) Number of Fisher Scoring iterations: 4 > confint(model5, df= degf(nhc)) 2.5 % 97.5 % (Intercept) -1.6875162 2.6731172 factor(Folate)Normal -0.4430913 1.1385642 factor(Gender)Male -0.5315193 0.9757947 factor(Ethnicity)Black -1.8012340 1.4861718 factor(Ethnicity)Mexican American -2.4991124 0.7972242 factor(Ethnicity)Other -3.1972250 1.6088238 factor(Ethnicity)Other Hispanic -1.6434968 1.3673746 factor(Ethnicity)White -1.0306541 1.7126254 factor(Serum_Zinc)Normal -1.9870135 1.6577457 factor(Alcohol_Consumption)Moderate Consumption -2.2911448 0.4919738 factor(Alcohol_Consumption)None or Low Consumption -2.2545333 1.2582603 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate Consumption -1.3696117 3.1857984 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None or Low Consumption -1.5137161 2.7786603 > summ(model5,confint=TRUE, n.sd=1, digits= 6) MODEL INFO: Observations: 558 Dependent Variable: HSCRP Type: Analysis of complex survey design Family: quasibinomial Link function: logit MODEL FIT: Pseudo-R² (Cragg-Uhler) = 0.087860 Pseudo-R² (McFadden) = 0.050391 AIC = NA --------------------------------------------------------------------------------------------------------------------------- Est. 2.5% 97.5% t val. p ---------------------------------------------------------------- ----------- ----------- ----------- ----------- ---------- (Intercept) 0.492801 -0.849985 1.835586 0.719304 0.523923 factor(Folate)Normal 0.347736 -0.139308 0.834781 1.399360 0.256180 factor(Gender)Male 0.222138 -0.242015 0.686290 0.938015 0.417435 factor(Ethnicity)Black -0.157531 -1.169834 0.854771 -0.305003 0.780300 factor(Ethnicity)Mexican -0.850944 -1.865997 0.164109 -1.643087 0.198909 American factor(Ethnicity)Other -0.794201 -2.274144 0.685743 -1.051800 0.370133 factor(Ethnicity)Other -0.138061 -1.065209 0.789087 -0.291857 0.789415 Hispanic factor(Ethnicity)White 0.340986 -0.503762 1.185733 0.791147 0.486637 factor(Serum_Zinc)Normal -0.164634 -1.286978 0.957710 -0.287502 0.792444 factor(Alcohol_Consumption)Moderate -0.899585 -1.756601 -0.042570 -2.057320 0.131834 Consumption factor(Alcohol_Consumption)None -0.498136 -1.579844 0.583571 -0.902582 0.433276 or Low Consumption factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate 0.908093 -0.494670 2.310857 1.268803 0.294015 Consumption factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None 0.632472 -0.689295 1.954239 0.937853 0.417506 or Low Consumption --------------------------------------------------------------------------------------------------------------------------- Estimated dispersion parameter = 0.996422 > > > ### Equation > > > model6 <- svyglm(HSCRP ~ factor(Gender) + factor(Age) + factor(Serum_Zinc)* + factor(Alcohol_Consumption), design= nhc,rescale = TRUE, family = quasibinomial()) > > ### Summs > summary (model6, df= degf(nhc)) Call: svyglm(formula = HSCRP ~ factor(Gender) + factor(Age) + factor(Serum_Zinc) * factor(Alcohol_Consumption), design = nhc, family = quasibinomial(), rescale = TRUE) Survey design: svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.96529 0.61839 1.561 0.1394 factor(Gender)Male 0.23271 0.22118 1.052 0.3094 factor(Age)Late Age -0.79345 0.40851 -1.942 0.0711 . factor(Age)Middle Age -0.36204 0.42981 -0.842 0.4128 factor(Age)Young Adult -0.30931 0.32779 -0.944 0.3603 factor(Serum_Zinc)Normal -0.04221 0.64710 -0.065 0.9489 factor(Alcohol_Consumption)Moderate Consumption -0.89288 0.50491 -1.768 0.0973 . factor(Alcohol_Consumption)None or Low Consumption -0.32881 0.61547 -0.534 0.6010 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate Consumption 0.76741 0.76010 1.010 0.3287 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None or Low Consumption 0.33990 0.77518 0.438 0.6673 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for quasibinomial family taken to be 1.00079) Number of Fisher Scoring iterations: 4 > confint(model6, df= degf(nhc)) 2.5 % 97.5 % (Intercept) -0.5478664 2.4784370 factor(Gender)Male -0.3084932 0.7739175 factor(Age)Late Age -1.7930320 0.2061358 factor(Age)Middle Age -1.4137472 0.6896735 factor(Age)Young Adult -1.1113919 0.4927724 factor(Serum_Zinc)Normal -1.6256010 1.5411887 factor(Alcohol_Consumption)Moderate Consumption -2.1283563 0.3425890 factor(Alcohol_Consumption)None or Low Consumption -1.8348117 1.1771851 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate Consumption -1.0924780 2.6273024 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None or Low Consumption -1.5568926 2.2367014 > summ(model6,confint=TRUE, n.sd=1, digits= 6) MODEL INFO: Observations: 558 Dependent Variable: HSCRP Type: Analysis of complex survey design Family: quasibinomial Link function: logit MODEL FIT: Pseudo-R² (Cragg-Uhler) = 0.047129 Pseudo-R² (McFadden) = 0.026612 AIC = NA -------------------------------------------------------------------------------------------------------------------------- Est. 2.5% 97.5% t val. p ---------------------------------------------------------------- ----------- ----------- ---------- ----------- ---------- (Intercept) 0.965285 -0.246742 2.177312 1.560959 0.169555 factor(Gender)Male 0.232712 -0.200791 0.666215 1.052144 0.333250 factor(Age)Late Age -0.793448 -1.594110 0.007214 -1.942306 0.100121 factor(Age)Middle Age -0.362037 -1.204452 0.480378 -0.842316 0.431899 factor(Age)Young Adult -0.309310 -0.951773 0.333154 -0.943611 0.381796 factor(Serum_Zinc)Normal -0.042206 -1.310497 1.226085 -0.065224 0.950115 factor(Alcohol_Consumption)Moderate -0.892884 -1.882491 0.096724 -1.768398 0.127406 Consumption factor(Alcohol_Consumption)None -0.328813 -1.535110 0.877484 -0.534248 0.612375 or Low Consumption factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate 0.767412 -0.722350 2.257175 1.009624 0.351643 Consumption factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None 0.339904 -1.179420 1.859229 0.438485 0.676383 or Low Consumption -------------------------------------------------------------------------------------------------------------------------- Estimated dispersion parameter = 1.00079 > > ####### ROC and Descriptive Stats on Separate script on separate script#################################### > model7 <- svyglm(HSCRP ~ factor(Age) + factor(Serum_Zinc)* + factor(Alcohol_Consumption), design= nhc,rescale = TRUE, family = quasibinomial()) > > ### Summs > summary (model7, df= degf(nhc)) Call: svyglm(formula = HSCRP ~ factor(Age) + factor(Serum_Zinc) * factor(Alcohol_Consumption), design = nhc, family = quasibinomial(), rescale = TRUE) Survey design: svydesign(id = ~SDMVPSU, weights = ~WTSA2YR, strata = ~SDMVSTRA, nest = TRUE, survey.lonely.psu = "adjust", data = DF) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.06913 0.61152 1.748 0.101 factor(Age)Late Age -0.81035 0.40695 -1.991 0.065 . factor(Age)Middle Age -0.36248 0.42903 -0.845 0.411 factor(Age)Young Adult -0.32089 0.32023 -1.002 0.332 factor(Serum_Zinc)Normal -0.01737 0.63753 -0.027 0.979 factor(Alcohol_Consumption)Moderate Consumption -0.86948 0.50809 -1.711 0.108 factor(Alcohol_Consumption)None or Low Consumption -0.32428 0.60721 -0.534 0.601 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate Consumption 0.74043 0.75458 0.981 0.342 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None or Low Consumption 0.31628 0.76594 0.413 0.685 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for quasibinomial family taken to be 1.000175) Number of Fisher Scoring iterations: 4 > confint(model7, df= degf(nhc)) 2.5 % 97.5 % (Intercept) -0.3768861 2.5151535 factor(Age)Late Age -1.7726245 0.1519345 factor(Age)Middle Age -1.3769637 0.6520038 factor(Age)Young Adult -1.0781063 0.4363265 factor(Serum_Zinc)Normal -1.5248786 1.4901485 factor(Alcohol_Consumption)Moderate Consumption -2.0709194 0.3319530 factor(Alcohol_Consumption)None or Low Consumption -1.7600995 1.1115441 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate Consumption -1.0438682 2.5247379 factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None or Low Consumption -1.4948673 2.1274342 > summ(model7,confint=TRUE, n.sd=1, digits= 6) MODEL INFO: Observations: 558 Dependent Variable: HSCRP Type: Analysis of complex survey design Family: quasibinomial Link function: logit MODEL FIT: Pseudo-R² (Cragg-Uhler) = 0.043148 Pseudo-R² (McFadden) = 0.024328 AIC = NA --------------------------------------------------------------------------------------------------------------------------- Est. 2.5% 97.5% t val. p ---------------------------------------------------------------- ----------- ----------- ----------- ----------- ---------- (Intercept) 1.069134 -0.129427 2.267695 1.748316 0.123897 factor(Age)Late Age -0.810345 -1.607949 -0.012741 -1.991273 0.086728 factor(Age)Middle Age -0.362480 -1.203354 0.478394 -0.844892 0.426103 factor(Age)Young Adult -0.320890 -0.948523 0.306743 -1.002070 0.349683 factor(Serum_Zinc)Normal -0.017365 -1.266896 1.232166 -0.027238 0.979030 factor(Alcohol_Consumption)Moderate -0.869483 -1.865317 0.126350 -1.711286 0.130765 Consumption factor(Alcohol_Consumption)None -0.324278 -1.514386 0.865831 -0.534046 0.609842 or Low Consumption factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)Moderate 0.740435 -0.738519 2.219389 0.981252 0.359158 Consumption factor(Serum_Zinc)Normal:factor(Alcohol_Consumption)None 0.316283 -1.184923 1.817490 0.412937 0.692008 or Low Consumption --------------------------------------------------------------------------------------------------------------------------- Estimated dispersion parameter = 1.000175