knitr::opts_knit$set( stop_on_error = 2L ) knitr::opts_chunk$set( fig.height = 11, fig.width = 7 ) options(cite = FALSE)
rm(list=ls(pattern="\\.out")) suppressWarnings(suppressMessages(library(Zelig))) set.seed(1234)
Attaching the sample dataset:
library(survival) data(tobin)
Estimating linear regression using tobit
:
## consider these two models: m1 <- survreg(Surv(durable, durable>0, type='left') ~ age + quant, data=tobin, dist='gaussian')
Summarize estimated paramters:
summary(m1)
Setting values for the explanatory variables to their sample averages and simulating quantity of interest.
library(smargins) m.sm <- smargins(m1, quant = seq(210, 280, 10)) summary(m.sm) library(ggplot2) ggplot(summary(m.sm), aes(x = quant, y = mean)) + geom_smooth(aes(ymin = lower_2.5, ymax = upper_97.5), stat = "identity")
Set explanatory variables to their default(mean/mode) values, with
high (80th percentile) and low (20th percentile) liquidity ratio
(quant
):
m.sm2 <- smargins(m1, quant = quantile(tobin$quant, prob = c(0.2, 0.8))) summary(m.sm2)
Estimating the first difference for the effect of high versus low
liquidity ratio on duration(\ durable
):
summary(scompare(m.sm2, "quant")) ggplot(scompare(m.sm2, "quant"), aes(x = .smargin_qi)) + geom_density(fill = "blue", alpha = 0.25)
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