Difference between revisions of "SOCR Simulated HELP Data Activity"
(→Data I/O, summaries, visualization) |
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attach(hemp_sim_data) | attach(hemp_sim_data) | ||
summary(hemp_sim_data) | summary(hemp_sim_data) | ||
| − | fivenum(hemp_sim_data $\$ $mcs) | + | fivenum(hemp_sim_data$\$ $mcs) |
| − | mean(hemp_sim_data $\$ $mcs, na.rm=TRUE); median(hemp_sim_data $\$ $mcs, na.rm=TRUE); range(hemp_sim_data $\$ $mcs, na.rm=TRUE); sd(hemp_sim_data $\$ $mcs, na.rm=TRUE); var(hemp_sim_data $\$ $mcs, na.rm=TRUE) | + | mean(hemp_sim_data$\$ $mcs, na.rm=TRUE); median(hemp_sim_data$\$ $mcs, na.rm=TRUE); range(hemp_sim_data$\$ $mcs, na.rm=TRUE); sd(hemp_sim_data$\$ $mcs, na.rm=TRUE); var(hemp_sim_data$\$ $mcs, na.rm=TRUE) |
| − | quantile(hemp_sim_data $\$ $mcs, seq(from=0, to=1, length=11), na.rm=TRUE) | + | quantile(hemp_sim_data$\$ $mcs, seq(from=0, to=1, length=11), na.rm=TRUE) |
| − | no_mis_hemp_sim_data_mcs <- na.omit(hemp_sim_data $\$ $mcs) | + | no_mis_hemp_sim_data_mcs <- na.omit(hemp_sim_data$\$ $mcs) |
hist(no_mis_hemp_sim_data_mcs, main="", freq=FALSE) | hist(no_mis_hemp_sim_data_mcs, main="", freq=FALSE) | ||
| Line 32: | Line 32: | ||
| − | cor_mat <- cor(cbind(hemp_sim_data $\$ $mcs, hemp_sim_data $\$ $i11, hemp_sim_data $\$ $pcs1)) | + | cor_mat <- cor(cbind(hemp_sim_data$\$ $mcs, hemp_sim_data$\$ $i11, hemp_sim_data$\$ $pcs1)) |
cor_mat | cor_mat | ||
cor_mat[c(2, 3), 2] | cor_mat[c(2, 3), 2] | ||
| − | plot(hemp_sim_data $\$ $mcs[hemp_sim_data $\$ $female==0], hemp_sim_data $\$ $cesd[hemp_sim_data $\$ $female==0], xlab="MCS", ylab="cesd", type="n", bty="n") | + | plot(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0], hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==0], xlab="MCS", ylab="cesd", type="n", bty="n") |
| − | text(hemp_sim_data $\$ $mcs[hemp_sim_data $\$ $female==0& hemp_sim_data $\$ $substance=="alcohol"], | + | text(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="alcohol"], |
| − | hemp_sim_data $\$ $cesd[hemp_sim_data $\$ $female==1& hemp_sim_data $\$ $substance=="alcohol"],"A") | + | hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==1& hemp_sim_data$\$ $substance=="alcohol"],"A") |
| − | text(hemp_sim_data $\$ $mcs[hemp_sim_data $\$ $female==0& hemp_sim_data $\$ $substance=="cocaine"], | + | text(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="cocaine"], |
| − | hemp_sim_data $\$ $cesd[hemp_sim_data $\$ $female==0& hemp_sim_data $\$ $substance=="cocaine"],"C") | + | hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="cocaine"],"C") |
| − | text(hemp_sim_data $\$ $mcs[hemp_sim_data $\$ $female==0& hemp_sim_data $\$ $substance=="heroin"], | + | text(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="heroin"], |
| − | hemp_sim_data $\$ $cesd[hemp_sim_data $\$ $female==1& hemp_sim_data $\$ $substance=="heroin"],"H") | + | hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==1& hemp_sim_data$\$ $substance=="heroin"],"H") |
| − | rug(jitter(hemp_sim_data $\$ $mcs[hemp_sim_data $\$ $female==0]), side=2) | + | rug(jitter(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0]), side=2) |
| − | rug(jitter(hemp_sim_data $\$ $mcs[hemp_sim_data $\$ $female==0]), side=3) | + | rug(jitter(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0]), side=3) |
| − | table(hemp_sim_data $\$ $homeless, hemp_sim_data $\$ $female) | + | table(hemp_sim_data$\$ $homeless, hemp_sim_data$\$ $female) |
| − | or <- (sum(hemp_sim_data $\$ $homeless==0 & hemp_sim_data $\$ $female==0 , na.rm=TRUE)* | + | or <- (sum(hemp_sim_data$\$ $homeless==0 & hemp_sim_data$\$ $female==0 , na.rm=TRUE)* |
| − | sum(hemp_sim_data $\$ $homeless==1 & hemp_sim_data $\$ $female==1 , na.rm=TRUE))/ | + | sum(hemp_sim_data$\$ $homeless==1 & hemp_sim_data$\$ $female==1 , na.rm=TRUE))/ |
| − | (sum(hemp_sim_data $\$ $homeless==0 & hemp_sim_data $\$ $female==1 , na.rm=TRUE)* | + | (sum(hemp_sim_data$\$ $homeless==0 & hemp_sim_data$\$ $female==1 , na.rm=TRUE)* |
| − | sum(hemp_sim_data $\$ $homeless==1 & hemp_sim_data $\$ $female==0 , na.rm=TRUE)) | + | sum(hemp_sim_data$\$ $homeless==1 & hemp_sim_data$\$ $female==0 , na.rm=TRUE)) |
or | or | ||
| − | chisq_val <- chisq.test(hemp_sim_data $\$ $homeless, hemp_sim_data $\$ $female, correct=FALSE) | + | chisq_val <- chisq.test(hemp_sim_data$\$ $homeless, hemp_sim_data$\$ $female, correct=FALSE) |
chisq_val | chisq_val | ||
| − | fisher.test(hemp_sim_data $\$ $homeless, hemp_sim_data $\$ $female) | + | fisher.test(hemp_sim_data$\$ $homeless, hemp_sim_data$\$ $female) |
| − | ttres <- t.test(hemp_sim_data $\$ $age ~ hemp_sim_data $\$ $female, data=hemp_sim_data) | + | ttres <- t.test(hemp_sim_data$\$ $age ~ hemp_sim_data$\$ $female, data=hemp_sim_data) |
print(ttres) | print(ttres) | ||
| − | wilcox.test(hemp_sim_data $\$ $age ~ as.factor(hemp_sim_data $\$ $female), correct=FALSE) | + | wilcox.test(hemp_sim_data$\$ $age ~ as.factor(hemp_sim_data$\$ $female), correct=FALSE) |
| − | ksres <- ks.test(hemp_sim_data $\$ $age[hemp_sim_data $\$ $female==0], hemp_sim_data $\$ $age[hemp_sim_data $\$ $female==1], data=hemp_sim_data) | + | ksres <- ks.test(hemp_sim_data$\$ $age[hemp_sim_data$\$ $female==0], hemp_sim_data$\$ $age[hemp_sim_data$\$ $female==1], data=hemp_sim_data) |
print(ksres) | print(ksres) | ||
Revision as of 18:30, 12 September 2014
Contents
- 1 SOCR Simulated HELP Data - SOCR Activity: Simulated Health Evaluation and Linkage to Primary (HELP) Care Dataset
- 2 SOCR Simulated HELP Data
- 3 R examples
- 4 R examples
- 4.1 Data I/O, summaries, visualization
- 4.2 Derived variables and data manipulation
- 4.3 Sorting and subsetting
- 4.4 Exploratory data analysis
- 4.5 Graphing and plotting of data (scatterplot, bubble chart, multiple plots, dotplot, etc.)
- 4.6 Bivariate relationship
- 4.7 Contingency tables
- 4.8 Two-sample tests
- 4.9 Survival analysis (Kaplan–Meier plot)
- 4.10 Scatterplot with smooth fit
- 4.11 Regression with prediction intervals
- 4.12 Linear regression with interaction
- 4.13 Regression diagnostics
- 4.14 Fitting stratified regression models
- 4.15 Two-way analysis of variance (ANOVA)
- 4.16 Multiple comparisons
- 4.17 Contrasts
- 4.18 Logistic regression
- 4.19 Poisson regression
- 4.20 Zero-inflated Poisson regression
- 4.21 Negative binomial regression
- 4.22 Lasso model selection
- 4.23 Quantile regression
- 4.24 Ordinal logit regression
- 4.25 Multinomial logit regression
- 4.26 Generalized additive model
- 4.27 Data transformations
- 4.28 General linear model for correlated data
- 4.29 Random effects model
- 4.30 Generalized estimating equations (GEE) model
- 4.31 Generalized linear mixed model
- 4.32 Proportional hazards regression model
- 4.33 Bayesian Poisson regression
- 4.34 Cronbach’s $\alpha$
- 4.35 Factor analysis
- 4.36 Recursive partitioning
- 4.37 Linear discriminant analysis
- 4.38 Hierarchical clustering
- 4.39 ROC curve
- 4.40 Multiple imputation
- 4.41 Propensity score modeling
- 5 References
SOCR Simulated HELP Data - SOCR Activity: Simulated Health Evaluation and Linkage to Primary (HELP) Care Dataset
SOCR Simulated HELP Data
See the SOCR Simulated HELP Data first. These data can be copy-pasted using the mouse from the HTML table into a plain text file "help_data.csv".
R examples
These simulated HELP data can be used to demonstrate (using SOCR and R)a number of different statistical, modeling, inferential and data analytic techniques.
R examples
These simulated HELP data can be used to demonstrate (using SOCR and R)a number of different statistical, modeling, inferential and data analytic techniques.
Data I/O, summaries, visualization
options(digits=2) # decimal precision
options(width=80) # narrows output to stay in the grey box
hemp_sim_data <- read.csv("http://socr.umich.edu/data/SOCR_HELP_SIm_Data_2014.csv")
attach(hemp_sim_data)
summary(hemp_sim_data)
fivenum(hemp_sim_data$\$ $mcs)
mean(hemp_sim_data$\$ $mcs, na.rm=TRUE); median(hemp_sim_data$\$ $mcs, na.rm=TRUE); range(hemp_sim_data$\$ $mcs, na.rm=TRUE); sd(hemp_sim_data$\$ $mcs, na.rm=TRUE); var(hemp_sim_data$\$ $mcs, na.rm=TRUE)
quantile(hemp_sim_data$\$ $mcs, seq(from=0, to=1, length=11), na.rm=TRUE)
no_mis_hemp_sim_data_mcs <- na.omit(hemp_sim_data$\$ $mcs)
hist(no_mis_hemp_sim_data_mcs, main="", freq=FALSE)
lines(density(no_mis_hemp_sim_data_mcs), main="MCS", lty=2, lwd=2)
xvals <- seq(from=min(no_mis_hemp_sim_data_mcs), to=max(no_mis_hemp_sim_data_mcs), length=100)
lines(xvals, dnorm(xvals, mean(no_mis_hemp_sim_data_mcs), sd(no_mis_hemp_sim_data_mcs)), lwd=2)
cor_mat <- cor(cbind(hemp_sim_data$\$ $mcs, hemp_sim_data$\$ $i11, hemp_sim_data$\$ $pcs1))
cor_mat
cor_mat[c(2, 3), 2]
plot(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0], hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==0], xlab="MCS", ylab="cesd", type="n", bty="n")
text(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="alcohol"],
hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==1& hemp_sim_data$\$ $substance=="alcohol"],"A")
text(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="cocaine"],
hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="cocaine"],"C")
text(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0& hemp_sim_data$\$ $substance=="heroin"],
hemp_sim_data$\$ $cesd[hemp_sim_data$\$ $female==1& hemp_sim_data$\$ $substance=="heroin"],"H")
rug(jitter(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0]), side=2)
rug(jitter(hemp_sim_data$\$ $mcs[hemp_sim_data$\$ $female==0]), side=3)
table(hemp_sim_data$\$ $homeless, hemp_sim_data$\$ $female)
or <- (sum(hemp_sim_data$\$ $homeless==0 & hemp_sim_data$\$ $female==0 , na.rm=TRUE)*
sum(hemp_sim_data$\$ $homeless==1 & hemp_sim_data$\$ $female==1 , na.rm=TRUE))/
(sum(hemp_sim_data$\$ $homeless==0 & hemp_sim_data$\$ $female==1 , na.rm=TRUE)*
sum(hemp_sim_data$\$ $homeless==1 & hemp_sim_data$\$ $female==0 , na.rm=TRUE))
or
chisq_val <- chisq.test(hemp_sim_data$\$ $homeless, hemp_sim_data$\$ $female, correct=FALSE)
chisq_val
fisher.test(hemp_sim_data$\$ $homeless, hemp_sim_data$\$ $female)
ttres <- t.test(hemp_sim_data$\$ $age ~ hemp_sim_data$\$ $female, data=hemp_sim_data)
print(ttres)
wilcox.test(hemp_sim_data$\$ $age ~ as.factor(hemp_sim_data$\$ $female), correct=FALSE)
ksres <- ks.test(hemp_sim_data$\$ $age[hemp_sim_data$\$ $female==0], hemp_sim_data$\$ $age[hemp_sim_data$\$ $female==1], data=hemp_sim_data)
print(ksres)
Derived variables and data manipulation
Sorting and subsetting
Exploratory data analysis
Graphing and plotting of data (scatterplot, bubble chart, multiple plots, dotplot, etc.)
Bivariate relationship
Contingency tables
Two-sample tests
Survival analysis (Kaplan–Meier plot)
Scatterplot with smooth fit
Regression with prediction intervals
Linear regression with interaction
Regression diagnostics
Fitting stratified regression models
Two-way analysis of variance (ANOVA)
Multiple comparisons
Contrasts
Logistic regression
Poisson regression
Zero-inflated Poisson regression
Negative binomial regression
Lasso model selection
Quantile regression
Ordinal logit regression
Multinomial logit regression
Generalized additive model
Data transformations
Random effects model
Generalized estimating equations (GEE) model
Generalized linear mixed model
Proportional hazards regression model
Bayesian Poisson regression
Cronbach’s $\alpha$
Factor analysis
Recursive partitioning
Linear discriminant analysis
Hierarchical clustering
ROC curve
Multiple imputation
Propensity score modeling
References
- Evaluation and Linkage to Primary (HELP) Care study
- Data formats: help.csv and help.Rdata
- Study and Data specifications
- SAS and R Data Management, Statistical Analysis, and Graphics, Kleinman / Horton, 2009
- SOCR Home page: http://www.SOCR.ucla.edu
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