ui.probit {ui} | R Documentation |
This function allows you to derive uncertainty intervals for probit regression
when there is missing data in the binary outcome. The uncertainty intervals
can be used as a sensitivity analysis to ignorability (missing at random), and
are derived by maximum likelihood. Note that rho
=0 render the same results as
a complete case analysis.
ui.probit(out.formula, mis.formula = NULL, data, rho = c(-0.3, 0.3),
progress = TRUE, max.grid = 0.1, alpha = 0.05, method = "NR")
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 |
Vector containing the values of |
progress |
If TRUE prints out process time for each maximization of the likelihood. |
max.grid |
Maximum distance between two elements in |
alpha |
Default 0.05 corresponding to a confidence level of 95 for CI and UI. |
method |
Maximization method to be passed through |
In order to visualize the results, you can use plot.uiprobit
or profile.uiprobit
.
A list containing:
coef |
Estimated coefficients (outcome regression) for different values of |
rho |
The values of |
vcov |
Covariance matrix. |
ci |
Confidence intervals for different values of |
ui |
Uncertainty intervals. |
out.model |
Outcome regression model when rho=0. |
mis.model |
Regression model for missingness mechanism (selection). |
se |
Standard errors from outcome regression. |
value |
Value of maximum likelihood for different values of |
y |
Outcome vector. |
z |
Indicator variable of observed outcome. |
X.y |
Covariate matrix for outcome regression. |
X.z |
Covariate matrix for missingness mechanism (selection regression model). |
max.info |
Information about the maximization procedure. Includes whether it |
Minna Genbäck
Genbäck, M., Ng, N., Stanghellini, E., de Luna, X. (2018). Predictors of Decline in Self-reported Health: Addressing Non-ignorable Dropout in Longitudinal Studies of Aging. European journal of ageing, 15(2), 211-220.
library(MASS)
n<-500
delta<-c(0.5,0.6,0.1,-1,1)
beta<-c(-0.3,-0.5,0,-0.4,-0.3)
X<-cbind(rep(1,n),rnorm(n),runif(n),rbinom(n,2,0.5),rbinom(n,1,0.5))
x<-X[,-1]
rho=0.4
error<-mvrnorm(n,c(0,0),matrix(c(1,rho,rho,1),2))
zstar<-X%*%delta+error[,1]
z<-as.numeric(zstar>0)
ystar<-X%*%beta+error[,2]
y<-as.integer(ystar>0)
y[z==0]<-NA
data=data.frame(y=y,x1=x[,1],x2=x[,2],x3=x[,3],x4=x[,4])
m<-ui.probit(y~x1+x2+x3+x4,data=data,rho=c(0,0.5))
m
plot(m)
profile(m)