Publications by Ying Huang
Regression spline
Data Preparation #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 %>...
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Longitudinal Data Analysis
In this example, we will use Generalized Estimating Equations to do some longitudinal modeling of data from the ECLS-K 2011. Specifically, we will model changes in a student’s standardized math score as a continuous outcome and self rated health as a binomial outcome, from fall kindergarten to spring, 1st grade. Introduction to GEE’s Up unti...
37621 sym R (22368 sym/86 pcs) 3 img 1 tbl
Multi-level Models
A Fundamental problem of names Before we begin our discussion of multi-level data, we must first clarify some semantics. The models we are going to be describing and using in this and the following lesson go by a variety of different names, depending on the disciplinary tradition you may come from. To clarify, these models all follow common forms...
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GLMM(s) using the INLA Approximation
Data Manipulations # Get US county socio-economic variables from Area resource file 2019-2020 arf2020<-import("ahrf2020.sas7bdat") arf2020<-arf2020%>% mutate(cofips=as.factor(f00004), coname=f00010, state = f00011, medhouvl=f1461314, pctcrpop= round(100*(f1492010/f0453010),2), medhinc= f1434514, ...
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Calculating Indices of Segregation
Data Manipulations # Get other US county Hispanic/Ethnicity Race variables from the 5 year (2015-2019) ASC data source. us <- unique(fips_codes$state)[1:51] Racetable <- map_df(us, function(x) {get_acs(geography = "tract", year=2019, geometry = F, output="wide", ...
9026 sym R (1938 sym/5 pcs) 1 img
Spatial Generalized Linear Models
Data Manipulations # Get US county socio-economic variables from Area resource file 2019-2020 arf2020<-import("ahrf2020.sas7bdat") arf2020<-arf2020%>% mutate(cofips=as.factor(f00004), coname=f00010, state = f00011, medhouvl=f1461314, pctcrpop= round(100*(f1492010/f0453010),2), medhinc= f1434514, ...
12068 sym R (5288 sym/9 pcs) 1 img
Document Spatial Regression
Data Manipulations # Get county socio-economic variables from Area resource file 2019-2020 arf2020<-import("ahrf2020.sas7bdat") arf2020<-arf2020%>% filter(f00011=="48") %>% mutate(cofips=as.factor(f00004), coname=f00010, state = f00011, medhouvl=f1461314, pctcrpop= round(100*(f1492010/f0453010),2), ...
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Spatial Exploratory analysis
# Download ACS data for the year 2015 for Los Angeles County, California census tracts in R using the tidycensus library LA_county <- get_acs(geography = 'tract',variables = c(totPop15 = "B01001_001", hispanic ="B03003_003", afrAm = "B02001_00...
16541 sym R (11187 sym/30 pcs) 8 img
Spatial Regimes
Data Manipulations # Get US county socio-economic variables from Area resource file 2019-2020 arf2020<-import("ahrf2020.sas7bdat") arf2020<-arf2020%>% mutate(cofips=as.factor(f00004), coname=f00010, state = f00011, medhouvl=f1461314, pctcrpop= round(100*(f1492010/f0453010),2), medhinc= f1434514, ...
19386 sym R (2606 sym/1 pcs) 1 img 4 tbl
GIS Home work 1
Samson A. Olowolaju samson.olowolaju@utsa.edu R version 4.1.2 Screen Shot QGIS version 3.16 Screen Shot LS0tDQp0aXRsZTogIkdJUyBIb21ld29yayAxIg0KYXV0aG9yOiAiU2Ftc29uIEEgT2xvd29sYWp1LCBNUEgiDQpkYXRlOiAiYHIgZm9ybWF0KFN5cy50aW1lKCksICclZCAlQiwgJVknKWAiDQpvdXRwdXQ6DQogICBodG1sX2RvY3VtZW50Og0KICAgIGRmX3ByaW50OiBwYWdlZA0KICAgIGZpZ19oZWlnaHQ6IDcNCiAg...
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