Publications by Ying Huang
Stat_HMWk2
The codes below prepares the data for proper survey design analysis.Also, some variables were re-coded for the purpose of the research questions #Load data for analysis brfss <- readRDS("brfss_19.rds") # Cleaning the variable names for space, underscore & Uppercase Characters renam<-names(brfss) newnames<-tolower(gsub(pattern = "_",replaceme...
2193 sym R (2240 sym/5 pcs)
Predictive Modeling
Background In this project, i use the 2018 Natality data file for my predictive model. The outcome of interest is birth outcome(preterm birth). Mothers with singleton birth are classified based on whether they have preterm baby or not. Data Manupulation For Analysis natality <-read_dta ("Natality-18.dta") natality18 <- natality %>% select(a...
3023 sym R (10119 sym/29 pcs) 4 img
Missing data Homewk
#Load data for analysis#sub-setting and Re-codding variables for analysis purposes brfss <- readRDS("brfss_177.rds") # Cleaning the variable names for space, underscore & Uppercase Characters renam<-names(brfss) newnames<-tolower(gsub(pattern = "_",replacement = "",x = renam)) names(brfss)<-newnames homewk3 <- brfss %>% dplyr::sele...
1139 sym R (14009 sym/46 pcs) 4 tbl
Ordinal/Multinomial Logistic Regression
#Load data for analysis#sub-setting and Re-codding variables for analysis purposes brfss <- readRDS("brfss_177.rds") # Cleaning the variable names for space, underscore & Uppercase Characters renam<-names(brfss) newnames<-tolower(gsub(pattern = "_",replacement = "",x = renam)) names(brfss)<-newnames homewk3 <- brfss %>% select(stat...
1930 sym R (16737 sym/29 pcs) 1 tbl
Count model
This example will cover the use of R functions for fitting count data models to complex survey data and to aggregate data at the county level. Specifically, we focus on the Poisson and Negative Binomial models to individual level survey data as well as for aggregate data. For this example I am using 2016 CDC Behavioral Risk Factor Surveillance Sy...
23351 sym R (24432 sym/81 pcs) 5 img
Poisson modeling
#Load data for analysis#sub-setting and Re-codding variables for analysis purposes brfss <- readRDS("brfss_177.rds") # Cleaning the variable names for space, underscore & Uppercase Characters renam<-names(brfss) newnames<-tolower(gsub(pattern = "_",replacement = "",x = renam)) names(brfss)<-newnames homewk3 <- brfss %>% dplyr::sele...
1110 sym R (10362 sym/34 pcs) 1 img
Principal Component Analysis
This example illustrates the use of the method of Principal Components Analysis to form an index of overall health using data from the 2017 CDC Behavioral Risk Factor Surveillance System (BRFSS) SMART MSA data Link and an example of calcuating a place-based index of area deprivation. Principal Components is a mathematical technique (not a statist...
8183 sym R (11725 sym/26 pcs) 5 img
Home_work7_PCA
#Load data for analysis#sub-setting and Re-codding variables for analysis purposes brfss <- readRDS("brfss_177.rds") # Cleaning the variable names for space, underscore & Uppercase Characters renam<-names(brfss) newnames<-tolower(gsub(pattern = "_",replacement = "",x = renam)) names(brfss)<-newnames homewk3 <- brfss %>% dplyr::sele...
2332 sym R (7552 sym/23 pcs) 5 img
Missing data
This example will illustrate typical aspects of dealing with missing data. Topics will include: Mean imputation, modal imputation for categorical data, and multiple imputation of complex patterns of missing data. For this example I am using 2016 CDC Behavioral Risk Factor Surveillance System (BRFSS) SMART county data. Link Missing data Every ti...
11930 sym R (31543 sym/91 pcs) 9 img
Fixed effect model
#Load data for analysis#sub-setting and Re-codding variables for analysis purposes brfss <- readRDS("brfss_177.rds") # Cleaning the variable names for space, underscore & Uppercase Characters renam<-names(brfss) newnames<-tolower(gsub(pattern = "_",replacement = "",x = renam)) names(brfss)<-newnames #get state names and abb. sta <- rea...
9097 sym R (4549 sym/16 pcs)