ui.ols {ui} | R Documentation |
This function allows you to derive uncertainty intervals for OLS regression when there is missing data in the continuous outcome. The uncertainty intervals can be used as a sensitivity analysis to ignorability (missing at random). Note that rho=0 render the same results as a complete case analysis.
ui.ols(out.formula, mis.formula = NULL, data, rho = c(-0.3, 0.3),
alpha = 0.05, gridn = 101)
out.formula |
Formula for outcome regression. |
mis.formula |
Formula for missingness mechanism. If NULL the same covariates as in the outcome regression will be used. |
data |
data.frame containing the variables in the formula. |
rho |
The limits of rho for which the uncertainty interval should be constructed. |
alpha |
Default 0.05 corresponding to a confidence level of 95 for CI and UI. |
gridn |
The number of distinct points within the interval |
In order to visualize the results, you can use plot.uiols
,
or profile.uiols
.
A list containing:
call |
The matched call |
ci |
Confidence intervals for different values of |
ui |
Uncertainty intervals |
coef |
Estimated coefficients (outcome regression) for different values of |
out.model |
Outcome regression model when rho=0. |
mis.model |
Regression model for missingness mechanism (selection). |
rho |
The range of |
gridrho |
The values of |
sigma |
Consistant estimate of sigma |
se |
Standard error for different values of |
ciols |
Confidence intervals from a complete case analysis |
ident.bound |
Bounds for the coefficient estimates. |
Minna Genbäck
Genbäck, M., Stanghellini, E., de Luna, X. (2015). Uncertainty Intervals for Regression Parameters with Non-ignorable Missingness in the Outcome. Statistical Papers, 56(3), 829-847.
library(MASS)
n<-500
delta<-c(0.5,0.3,0.1)
beta<-c(0.8,-0.2,0.3)
X<-cbind(rep(1,n),rnorm(n),rbinom(n,1,0.5))
x<-X[,-1]
rho=0.4
error<-mvrnorm(n,c(0,0),matrix(c(1,rho*2,rho*2,4),2))
zstar<-X%*%delta+error[,1]
z<-as.numeric(zstar>0)
y<-X%*%beta+error[,2]
y[z==0]<-NA
data<-data.frame(y,x,z)
ui<-ui.ols(y~X1+X2,data=data,rho=c(-0.5,0.5))
ui
plot(ui)