library(SASxport)
## Warning: package 'SASxport' was built under R version 3.6.3
library(tidyr)
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.6.3
library(mice)
## Warning: package 'mice' was built under R version 3.6.3
library(mi)
## Warning: package 'mi' was built under R version 3.6.2
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.6.3
setwd("C:/Users/User/Desktop/nyulearningmaterial/2020Fall/consult/data")
diet <- read.xport("DR1IFF_J.xpt")
mental<-read.xport("DPQ_J.xpt")
selfreport<- read.xport("DBQ_J.xpt")
demo <- read.xport("DEMO_J.xpt")
health <- read.xport("HSQ_J.xpt")
activity <- read.xport("PAQ_J.xpt")
heidata <- read.csv("RES.csv",header = T)
#length(unique(diet$SEQN))
#aggregate observations
diet_agg <- diet %>%
group_by(SEQN) %>%
summarise(sum_kcal = sum(DR1IKCAL), sum_sugar = sum(DR1ISUGR), sum_tfat = sum(DR1ITFAT),sum_chl = sum(DR1ICHOL))
## `summarise()` ungrouping output (override with `.groups` argument)
#Merge DPQ_J mental questions
mental <- mental[,1:10]
#take out people who have complete the screener
mental <- na.omit(mental)
#remove refused and don't know
mental <- mental[rowSums(mental[,2:10]<7)==9,]
#calculate their PHQ depression score
mental$dp_score <- rowSums(mental[,2:10])
mental$dp_score_1 <- rowSums(mental[,c(2:5,7:10)])
#check the # of people for differet depress level
sum(mental$dp_score > 4)
## [1] 1296
sum(mental$dp_score > 9)
## [1] 459
sum(mental$dp_score > 14)
## [1] 167
sum(mental$dp_score > 19)
## [1] 43
#dummy coding the level: minimal(0-4) = 0, mild(5-9) = 1, moderate(10-14) = 2, moderately severe(15-19) = 3, severe(20-27) = 4
mental <- mental %>% mutate(dp_level = case_when(dp_score >=0 & dp_score <= 4 ~ '0',
dp_score >=5 & dp_score <= 9 ~ '1',
dp_score >=10 & dp_score <= 14 ~ '2',
dp_score >=15 & dp_score <= 19 ~ '3',
dp_score >=20 & dp_score <= 27 ~ '4'))
full_data <- merge(diet_agg,mental,by="SEQN")
full_data <- full_data[!is.na(full_data$dp_level),]
#Merge Diet Behavior (Only perception)
full_data <- merge(full_data,selfreport[,c("SEQN","DBQ700")],by="SEQN")
#Merge demo
demo <- demo[,c("SEQN","RIDAGEYR","RIDRETH3","DMDEDUC2","DMDMARTL","RIAGENDR","INDFMIN2")]
full_data <- merge(full_data,demo,by="SEQN")
#Merge health
#full_data <- merge(full_data,health,by="SEQN")
#Merge HEI
full_data <- merge(full_data,heidata[,c("SEQN","HEI2015_TOTAL_SCORE")],by="SEQN")
#Change variable names
colnames(full_data)[colnames(full_data)=="HEI2015_TOTAL_SCORE"] <-"hei"
colnames(full_data)[colnames(full_data)=="DBQ700"] <-"perception"
colnames(full_data)[colnames(full_data)=="RIDAGEYR"] <-"age"
colnames(full_data)[colnames(full_data)=="RIDRETH3"] <-"race"
colnames(full_data)[colnames(full_data)=="DMDEDUC2"] <-"edu"
colnames(full_data)[colnames(full_data)=="DMDMARTL"] <-"marital" #Cate
colnames(full_data)[colnames(full_data)=="RIAGENDR"] <-"gender"
colnames(full_data)[colnames(full_data)=="INDFMIN2"] <-"income_fam"
#Remove refused and don't knows
full_data<-full_data[!full_data$perception == 9,]
full_data<-full_data[!full_data$income_fam == 77,]
full_data<-full_data[!full_data$income_fam == 99,]
full_data<-full_data[!full_data$marital == 77,]
full_data<-full_data[!full_data$marital == 99,]
full_data<-full_data[!full_data$edu == 7 ,]
full_data<-full_data[!full_data$edu == 9 ,]
full_data <- full_data[grepl("^NA", rownames(full_data))==F,]
#Missing pattern
mdf<-missing_data.frame(full_data)
#Get the summary and number of NAs
summary(mdf)
## SEQN sum_kcal sum_sugar sum_tfat
## Min. : 93705 Min. : 25 Min. : 0.60 Min. : 0.00
## 1st Qu.: 95882 1st Qu.: 1420 1st Qu.: 55.52 1st Qu.: 51.35
## Median : 98238 Median : 1951 Median : 90.18 Median : 77.04
## Mean : 98266 Mean : 2120 Mean :106.59 Mean : 85.55
## 3rd Qu.:100623 3rd Qu.: 2619 3rd Qu.:136.65 3rd Qu.:108.33
## Max. :102956 Max. :11710 Max. :931.16 Max. :536.10
## sum_chl DPQ010 DPQ020 DPQ030
## Min. : 0.0 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 129.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 237.0 Median :0.0000 Median :0.0000 Median :0.0000
## Mean : 307.6 Mean :0.3785 Mean :0.3419 Mean :0.6399
## 3rd Qu.: 422.0 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :2403.0 Max. :3.0000 Max. :3.0000 Max. :3.0000
## DPQ040 DPQ050 DPQ060 DPQ070
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.7481 Mean :0.3859 Mean :0.2416 Mean :0.253
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :3.0000 Max. :3.0000 Max. :3.0000 Max. :3.000
## DPQ080 DPQ090 dp_score dp_score_1
## Min. :0.0000 Min. :0.00000 Min. : 0.0 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.: 0.0 1st Qu.: 0.000
## Median :0.0000 Median :0.00000 Median : 2.0 Median : 1.000
## Mean :0.1577 Mean :0.05281 Mean : 3.2 Mean : 2.814
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.: 5.0 3rd Qu.: 4.000
## Max. :3.0000 Max. :3.00000 Max. :25.0 Max. :23.000
## dp_level perception age race edu
## 0:3121 Min. :1.000 Min. :20.00 Min. :1.000 Min. :1.000
## 1: 702 1st Qu.:2.000 1st Qu.:36.00 1st Qu.:3.000 1st Qu.:3.000
## 2: 233 Median :3.000 Median :53.00 Median :3.000 Median :4.000
## 3: 95 Mean :3.065 Mean :51.06 Mean :3.477 Mean :3.581
## 4: 34 3rd Qu.:4.000 3rd Qu.:65.00 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :80.00 Max. :7.000 Max. :5.000
## marital gender income_fam hei
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. :12.71
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 5.000 1st Qu.:39.89
## Median :1.000 Median :2.000 Median : 8.000 Median :49.21
## Mean :2.588 Mean :1.512 Mean : 8.684 Mean :50.10
## 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:14.000 3rd Qu.:59.84
## Max. :6.000 Max. :2.000 Max. :15.000 Max. :92.10
#image(mdf, grayscale=TRUE)
#because there is no NA for plot NA mode
#Density plots
library(ggplot2)
#DP_27 v.s. perception
ggplot(full_data, aes(x = factor(perception), y = factor(dp_score))) +
geom_bin2d() +
stat_bin2d(geom="text", aes(label=..count..))+
scale_fill_gradient2("Count",high = "#f97561",mid = "#f2e783", low = "#a2d07e",
midpoint = 300, space = "Lab",na.value = "grey50", guide = "colourbar",
aesthetics = "fill")
#DP_level_27 v.s. perception
ggplot(full_data, aes(x = factor(perception), y = factor(dp_level))) +
geom_bin2d() +
stat_bin2d(geom="text", aes(label=..count..))+
scale_fill_gradient2("Count",high = "#f97561",mid = "#f2e783", low = "#a2d07e",
midpoint = 750, space = "Lab",na.value = "grey50", guide = "colourbar",
aesthetics = "fill")
#DP_24 v.s. perception
ggplot(full_data, aes(x = factor(perception), y = factor(dp_score))) +
geom_bin2d() +
stat_bin2d(geom="text", aes(label=..count..))+
scale_fill_gradient2("Count",high = "#f97561",mid = "#f2e783", low = "#a2d07e",
midpoint = 300, space = "Lab",na.value = "grey50", guide = "colourbar",
aesthetics = "fill")
model.full1 <- lm(hei~perception, data = full_data) #Health diet ~ perception
model.full2 <- lm(hei~dp_score, data = full_data) #Health diet ~ dp_score_27
model.full2.24 <- lm(hei~dp_score_1, data = full_data) #Health diet ~ dp_score_24
model.full3 <- lm(hei~dp_score+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam, data = full_data) #Health diet ~ full(dp_score_27)
model.full3.1 <- lm(hei~factor(dp_level)+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam, data = full_data) #Health diet ~ full(dp_score_27_level)
model.full4 <- lm(hei~factor(dp_level)+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+factor(dp_level)*perception, data = full_data) #Health diet ~ full+ interaction(dp_score_27_level)
model.full4.1 <- lm(hei~dp_score+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+dp_score*perception, data = full_data) #Health diet ~ full+ interaction(dp_score_27)
model.full3.24 <- lm(hei~dp_score_1+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam, data = full_data)#Health diet ~ full(dp_score_24)
model.full4.24 <- lm(hei~dp_score_1+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+dp_score_1*perception, data = full_data)#Health diet ~ full+ interaction(dp_score_24)
full_data$newdp_level <- ifelse(full_data$dp_score_1*27/24 > 11.25,1,0)
model.full3.24.a <- lm(hei~dp_score_1+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+newdp_level, data = full_data)#Health diet ~ full(dp_score_24)
model.full4.24.a <- lm(hei~dp_score_1+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+dp_score_1*perception+newdp_level, data = full_data)#Health diet ~ full+ interaction(dp_score_24) + new level
model.full4.24.b <- lm(hei~dp_score_1+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+dp_score_1*perception+newdp_level*perception, data = full_data)#Health diet ~ full+ interaction(dp_newscore_24&depscore)
model.full4.24.c <- lm(hei~dp_score_1+perception+age+factor(race)+factor(edu)+factor(marital)+factor(gender)+income_fam+newdp_level*factor(perception), data = full_data) #Health diet ~ full+ interaction(dp_newscore_24)
#summary(model.full1)
#summary(model.full2)
#summary(model.full3)
#summary(model.full3.1)
#summary(model.full4)
#summary(model.full3.24)
library(jtools)
## Warning: package 'jtools' was built under R version 3.6.3
library(huxtable)
## Warning: package 'huxtable' was built under R version 3.6.3
##
## Attaching package: 'huxtable'
## The following object is masked from 'package:ggplot2':
##
## theme_grey
## The following object is masked from 'package:dplyr':
##
## add_rownames
## The following objects are masked from 'package:SASxport':
##
## label, label<-
export_summs(model.full1,model.full2,model.full2.24,scale = F,digits=2,model.names = c("~perception","~dp_score","~dp_score1"))
| ~perception | ~dp_score | ~dp_score1 |
| (Intercept) | 59.97 *** | 51.43 *** | 51.35 *** |
| (0.67) | (0.27) | (0.27) |
| perception | -3.22 *** | | |
| (0.21) | | |
| dp_score | | -0.42 *** | |
| | (0.05) | |
| dp_score_1 | | | -0.44 *** |
| | | (0.06) |
| N | 4185 | 4185 | 4185 |
| R2 | 0.05 | 0.02 | 0.01 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
export_summs(model.full3,model.full3.1,model.full4,model.full4.1,model.full3.24,model.full4.24,scale = F,digits=2,model.names = c("cont.dp","categ.dp","categ.dp.inter","cont.dp.inter","cont.dp24","cont.dp24.inter"))
| cont.dp | categ.dp | categ.dp.inter | cont.dp.inter | cont.dp24 | cont.dp24.inter |
| (Intercept) | 53.79 *** | 53.57 *** | 54.43 *** | 54.84 *** | 53.82 *** | 54.70 *** |
| (1.54) | (1.54) | (1.60) | (1.63) | (1.54) | (1.63) |
| dp_score | -0.19 *** | | | -0.50 ** | | |
| (0.05) | | | (0.16) | | |
| perception | -2.19 *** | -2.24 *** | -2.49 *** | -2.50 *** | -2.21 *** | -2.47 *** |
| (0.21) | (0.21) | (0.25) | (0.26) | (0.21) | (0.26) |
| age | 0.11 *** | 0.11 *** | 0.11 *** | 0.11 *** | 0.11 *** | 0.11 *** |
| (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) |
| factor(race)2 | -0.08 | -0.13 | -0.19 | -0.13 | -0.09 | -0.12 |
| (0.90) | (0.90) | (0.90) | (0.90) | (0.90) | (0.90) |
| factor(race)3 | -4.43 *** | -4.50 *** | -4.54 *** | -4.46 *** | -4.43 *** | -4.46 *** |
| (0.70) | (0.70) | (0.70) | (0.70) | (0.70) | (0.70) |
| factor(race)4 | -3.29 *** | -3.30 *** | -3.35 *** | -3.33 *** | -3.29 *** | -3.32 *** |
| (0.75) | (0.75) | (0.75) | (0.75) | (0.75) | (0.75) |
| factor(race)6 | 2.47 ** | 2.44 ** | 2.31 ** | 2.36 ** | 2.47 ** | 2.38 ** |
| (0.86) | (0.86) | (0.87) | (0.87) | (0.86) | (0.87) |
| factor(race)7 | -3.87 *** | -3.96 *** | -4.02 *** | -3.92 *** | -3.88 *** | -3.92 *** |
| (1.08) | (1.08) | (1.08) | (1.08) | (1.08) | (1.08) |
| factor(edu)2 | -3.21 ** | -3.22 ** | -3.26 ** | -3.25 ** | -3.23 ** | -3.26 ** |
| (1.02) | (1.02) | (1.02) | (1.02) | (1.02) | (1.02) |
| factor(edu)3 | -2.23 * | -2.21 * | -2.23 * | -2.25 * | -2.24 * | -2.25 * |
| (0.94) | (0.94) | (0.94) | (0.94) | (0.94) | (0.94) |
| factor(edu)4 | -0.94 | -0.94 | -0.96 | -0.95 | -0.95 | -0.95 |
| (0.93) | (0.93) | (0.93) | (0.93) | (0.93) | (0.93) |
| factor(edu)5 | 2.36 * | 2.38 * | 2.34 * | 2.32 * | 2.37 * | 2.34 * |
| (0.99) | (0.99) | (0.99) | (0.99) | (0.99) | (0.99) |
| factor(marital)2 | 0.92 | 0.86 | 0.83 | 0.89 | 0.90 | 0.87 |
| (0.86) | (0.86) | (0.86) | (0.86) | (0.86) | (0.86) |
| factor(marital)3 | 0.61 | 0.60 | 0.61 | 0.61 | 0.61 | 0.60 |
| (0.69) | (0.69) | (0.69) | (0.69) | (0.69) | (0.69) |
| factor(marital)4 | 0.80 | 0.75 | 0.70 | 0.75 | 0.78 | 0.72 |
| (1.13) | (1.13) | (1.13) | (1.13) | (1.13) | (1.13) |
| factor(marital)5 | -1.67 ** | -1.69 ** | -1.69 ** | -1.66 * | -1.67 ** | -1.67 ** |
| (0.65) | (0.65) | (0.65) | (0.65) | (0.65) | (0.65) |
| factor(marital)6 | -1.43 | -1.48 | -1.47 | -1.43 | -1.44 | -1.44 |
| (0.77) | (0.77) | (0.77) | (0.77) | (0.77) | (0.77) |
| factor(gender)2 | 1.86 *** | 1.79 *** | 1.81 *** | 1.88 *** | 1.84 *** | 1.86 *** |
| (0.42) | (0.41) | (0.41) | (0.42) | (0.42) | (0.42) |
| income_fam | 0.02 | 0.03 | 0.03 | 0.02 | 0.02 | 0.02 |
| (0.05) | (0.05) | (0.05) | (0.05) | (0.05) | (0.05) |
| factor(dp_level)1 | | -0.04 | -1.77 | | | |
| | (0.56) | (1.86) | | | |
| factor(dp_level)2 | | -2.94 ** | -7.82 * | | | |
| | (0.90) | (3.30) | | | |
| factor(dp_level)3 | | -2.09 | -8.39 | | | |
| | (1.38) | (5.00) | | | |
| factor(dp_level)4 | | -4.46 | -14.95 | | | |
| | (2.28) | (8.09) | | | |
| factor(dp_level)1:perception | | | 0.54 | | | |
| | | (0.54) | | | |
| factor(dp_level)2:perception | | | 1.44 | | | |
| | | (0.93) | | | |
| factor(dp_level)3:perception | | | 1.77 | | | |
| | | (1.32) | | | |
| factor(dp_level)4:perception | | | 2.82 | | | |
| | | (2.05) | | | |
| dp_score:perception | | | | 0.09 * | | |
| | | | (0.05) | | |
| dp_score_1 | | | | | -0.20 *** | -0.50 ** |
| | | | | (0.06) | (0.18) |
| dp_score_1:perception | | | | | | 0.09 |
| | | | | | (0.05) |
| N | 4185 | 4185 | 4185 | 4185 | 4185 | 4185 |
| R2 | 0.14 | 0.14 | 0.15 | 0.14 | 0.14 | 0.14 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
export_summs(model.full3,model.full3.1,model.full4,model.full4.1,model.full3.24,model.full4.24,scale = F,digits=2,model.names = c("cont.dp","categ.dp","categ.dp.inter","cont.dp.inter","cont.dp24","cont.dp24.inter"))
| cont.dp | categ.dp | categ.dp.inter | cont.dp.inter | cont.dp24 | cont.dp24.inter |
| (Intercept) | 53.79 *** | 53.57 *** | 54.43 *** | 54.84 *** | 53.82 *** | 54.70 *** |
| (1.54) | (1.54) | (1.60) | (1.63) | (1.54) | (1.63) |
| dp_score | -0.19 *** | | | -0.50 ** | | |
| (0.05) | | | (0.16) | | |
| perception | -2.19 *** | -2.24 *** | -2.49 *** | -2.50 *** | -2.21 *** | -2.47 *** |
| (0.21) | (0.21) | (0.25) | (0.26) | (0.21) | (0.26) |
| age | 0.11 *** | 0.11 *** | 0.11 *** | 0.11 *** | 0.11 *** | 0.11 *** |
| (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) |
| factor(race)2 | -0.08 | -0.13 | -0.19 | -0.13 | -0.09 | -0.12 |
| (0.90) | (0.90) | (0.90) | (0.90) | (0.90) | (0.90) |
| factor(race)3 | -4.43 *** | -4.50 *** | -4.54 *** | -4.46 *** | -4.43 *** | -4.46 *** |
| (0.70) | (0.70) | (0.70) | (0.70) | (0.70) | (0.70) |
| factor(race)4 | -3.29 *** | -3.30 *** | -3.35 *** | -3.33 *** | -3.29 *** | -3.32 *** |
| (0.75) | (0.75) | (0.75) | (0.75) | (0.75) | (0.75) |
| factor(race)6 | 2.47 ** | 2.44 ** | 2.31 ** | 2.36 ** | 2.47 ** | 2.38 ** |
| (0.86) | (0.86) | (0.87) | (0.87) | (0.86) | (0.87) |
| factor(race)7 | -3.87 *** | -3.96 *** | -4.02 *** | -3.92 *** | -3.88 *** | -3.92 *** |
| (1.08) | (1.08) | (1.08) | (1.08) | (1.08) | (1.08) |
| factor(edu)2 | -3.21 ** | -3.22 ** | -3.26 ** | -3.25 ** | -3.23 ** | -3.26 ** |
| (1.02) | (1.02) | (1.02) | (1.02) | (1.02) | (1.02) |
| factor(edu)3 | -2.23 * | -2.21 * | -2.23 * | -2.25 * | -2.24 * | -2.25 * |
| (0.94) | (0.94) | (0.94) | (0.94) | (0.94) | (0.94) |
| factor(edu)4 | -0.94 | -0.94 | -0.96 | -0.95 | -0.95 | -0.95 |
| (0.93) | (0.93) | (0.93) | (0.93) | (0.93) | (0.93) |
| factor(edu)5 | 2.36 * | 2.38 * | 2.34 * | 2.32 * | 2.37 * | 2.34 * |
| (0.99) | (0.99) | (0.99) | (0.99) | (0.99) | (0.99) |
| factor(marital)2 | 0.92 | 0.86 | 0.83 | 0.89 | 0.90 | 0.87 |
| (0.86) | (0.86) | (0.86) | (0.86) | (0.86) | (0.86) |
| factor(marital)3 | 0.61 | 0.60 | 0.61 | 0.61 | 0.61 | 0.60 |
| (0.69) | (0.69) | (0.69) | (0.69) | (0.69) | (0.69) |
| factor(marital)4 | 0.80 | 0.75 | 0.70 | 0.75 | 0.78 | 0.72 |
| (1.13) | (1.13) | (1.13) | (1.13) | (1.13) | (1.13) |
| factor(marital)5 | -1.67 ** | -1.69 ** | -1.69 ** | -1.66 * | -1.67 ** | -1.67 ** |
| (0.65) | (0.65) | (0.65) | (0.65) | (0.65) | (0.65) |
| factor(marital)6 | -1.43 | -1.48 | -1.47 | -1.43 | -1.44 | -1.44 |
| (0.77) | (0.77) | (0.77) | (0.77) | (0.77) | (0.77) |
| factor(gender)2 | 1.86 *** | 1.79 *** | 1.81 *** | 1.88 *** | 1.84 *** | 1.86 *** |
| (0.42) | (0.41) | (0.41) | (0.42) | (0.42) | (0.42) |
| income_fam | 0.02 | 0.03 | 0.03 | 0.02 | 0.02 | 0.02 |
| (0.05) | (0.05) | (0.05) | (0.05) | (0.05) | (0.05) |
| factor(dp_level)1 | | -0.04 | -1.77 | | | |
| | (0.56) | (1.86) | | | |
| factor(dp_level)2 | | -2.94 ** | -7.82 * | | | |
| | (0.90) | (3.30) | | | |
| factor(dp_level)3 | | -2.09 | -8.39 | | | |
| | (1.38) | (5.00) | | | |
| factor(dp_level)4 | | -4.46 | -14.95 | | | |
| | (2.28) | (8.09) | | | |
| factor(dp_level)1:perception | | | 0.54 | | | |
| | | (0.54) | | | |
| factor(dp_level)2:perception | | | 1.44 | | | |
| | | (0.93) | | | |
| factor(dp_level)3:perception | | | 1.77 | | | |
| | | (1.32) | | | |
| factor(dp_level)4:perception | | | 2.82 | | | |
| | | (2.05) | | | |
| dp_score:perception | | | | 0.09 * | | |
| | | | (0.05) | | |
| dp_score_1 | | | | | -0.20 *** | -0.50 ** |
| | | | | (0.06) | (0.18) |
| dp_score_1:perception | | | | | | 0.09 |
| | | | | | (0.05) |
| N | 4185 | 4185 | 4185 | 4185 | 4185 | 4185 |
| R2 | 0.14 | 0.14 | 0.15 | 0.14 | 0.14 | 0.14 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
export_summs(model.full3.24.a,model.full4.24.a,model.full4.24.b ,scale = F,digits=2,model.names = c("cont.dp24(subdp)","cont.dp24.inter(subdp)","cont.dp24.inter(sub2)"))
| cont.dp24(subdp) | cont.dp24.inter(subdp) | cont.dp24.inter(sub2) |
| (Intercept) | 53.80 *** | 54.71 *** | 54.43 *** |
| (1.55) | (1.63) | (1.65) |
| dp_score_1 | -0.17 * | -0.48 * | -0.30 |
| (0.08) | (0.19) | (0.26) |
| perception | -2.21 *** | -2.49 *** | -2.40 *** |
| (0.21) | (0.26) | (0.28) |
| age | 0.11 *** | 0.11 *** | 0.11 *** |
| (0.01) | (0.01) | (0.01) |
| factor(race)2 | -0.09 | -0.13 | -0.12 |
| (0.90) | (0.90) | (0.90) |
| factor(race)3 | -4.44 *** | -4.47 *** | -4.47 *** |
| (0.70) | (0.70) | (0.70) |
| factor(race)4 | -3.29 *** | -3.32 *** | -3.31 *** |
| (0.75) | (0.75) | (0.75) |
| factor(race)6 | 2.48 ** | 2.38 ** | 2.38 ** |
| (0.86) | (0.87) | (0.87) |
| factor(race)7 | -3.88 *** | -3.93 *** | -3.95 *** |
| (1.08) | (1.08) | (1.08) |
| factor(edu)2 | -3.24 ** | -3.28 ** | -3.28 ** |
| (1.02) | (1.02) | (1.02) |
| factor(edu)3 | -2.24 * | -2.27 * | -2.26 * |
| (0.94) | (0.94) | (0.94) |
| factor(edu)4 | -0.96 | -0.97 | -0.96 |
| (0.93) | (0.93) | (0.93) |
| factor(edu)5 | 2.36 * | 2.33 * | 2.35 * |
| (0.99) | (0.99) | (0.99) |
| factor(marital)2 | 0.89 | 0.85 | 0.85 |
| (0.86) | (0.86) | (0.86) |
| factor(marital)3 | 0.60 | 0.60 | 0.59 |
| (0.69) | (0.69) | (0.69) |
| factor(marital)4 | 0.79 | 0.73 | 0.71 |
| (1.13) | (1.13) | (1.13) |
| factor(marital)5 | -1.67 ** | -1.67 ** | -1.67 ** |
| (0.65) | (0.65) | (0.65) |
| factor(marital)6 | -1.44 | -1.44 | -1.43 |
| (0.77) | (0.77) | (0.77) |
| factor(gender)2 | 1.83 *** | 1.84 *** | 1.85 *** |
| (0.42) | (0.42) | (0.42) |
| income_fam | 0.02 | 0.02 | 0.02 |
| (0.05) | (0.05) | (0.05) |
| newdp_level | -0.53 | -0.89 | -5.15 |
| (1.32) | (1.34) | (4.70) |
| dp_score_1:perception | | 0.09 | 0.04 |
| | (0.05) | (0.08) |
| perception:newdp_level | | | 1.24 |
| | | (1.32) |
| N | 4185 | 4185 | 4185 |
| R2 | 0.14 | 0.14 | 0.14 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
summary(model.full4.24.c)
##
## Call:
## lm(formula = hei ~ dp_score_1 + perception + age + factor(race) +
## factor(edu) + factor(marital) + factor(gender) + income_fam +
## newdp_level * factor(perception), data = full_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.293 -9.245 -0.521 8.937 49.053
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.52536 1.67111 32.030 < 2e-16 ***
## dp_score_1 -0.16693 0.08160 -2.046 0.040859 *
## perception -2.08437 0.28878 -7.218 6.24e-13 ***
## age 0.10981 0.01435 7.650 2.48e-14 ***
## factor(race)2 -0.11179 0.89994 -0.124 0.901145
## factor(race)3 -4.45836 0.70452 -6.328 2.74e-10 ***
## factor(race)4 -3.27295 0.75028 -4.362 1.32e-05 ***
## factor(race)6 2.40173 0.86595 2.774 0.005570 **
## factor(race)7 -3.93278 1.07909 -3.645 0.000271 ***
## factor(edu)2 -3.34582 1.02626 -3.260 0.001122 **
## factor(edu)3 -2.32612 0.94036 -2.474 0.013414 *
## factor(edu)4 -1.06661 0.93220 -1.144 0.252612
## factor(edu)5 2.25401 0.99749 2.260 0.023892 *
## factor(marital)2 0.80929 0.86322 0.938 0.348543
## factor(marital)3 0.59329 0.69133 0.858 0.390844
## factor(marital)4 0.73914 1.13375 0.652 0.514474
## factor(marital)5 -1.73068 0.64626 -2.678 0.007435 **
## factor(marital)6 -1.43965 0.77278 -1.863 0.062539 .
## factor(gender)2 1.82689 0.41586 4.393 1.15e-05 ***
## income_fam 0.01681 0.05345 0.314 0.753227
## newdp_level -8.32333 5.15390 -1.615 0.106396
## factor(perception)2 1.20004 0.77074 1.557 0.119547
## factor(perception)3 -0.32855 0.65606 -0.501 0.616543
## factor(perception)4 0.03943 0.76727 0.051 0.959016
## factor(perception)5 NA NA NA NA
## newdp_level:factor(perception)2 4.17786 5.71027 0.732 0.464430
## newdp_level:factor(perception)3 8.44295 5.30035 1.593 0.111257
## newdp_level:factor(perception)4 7.62537 5.25733 1.450 0.147015
## newdp_level:factor(perception)5 9.93511 5.47246 1.815 0.069523 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.1 on 4157 degrees of freedom
## Multiple R-squared: 0.1454, Adjusted R-squared: 0.1399
## F-statistic: 26.2 on 27 and 4157 DF, p-value: < 2.2e-16
ggplot(full_data, aes(x=dp_score_1, y=hei, color=factor(perception))) +
geom_point(aes(alpha = 100))+
geom_smooth(method=lm, se=FALSE, fullrange=F)
## `geom_smooth()` using formula 'y ~ x'
