I am hoping to present a nice table of a logistic regression measuring hypertension using stargazer which includes the coefficients, standard error, and significance (indicated by stars). When I try and plug in the specifications for stargazer, I see the following error message: "% Error: Unrecognized object type." I've included some sample data/the code I've run below. How might this be resolved? Thank you!
library(stargazer)
library(mfx)
structure(list(AGE = c(40L, 23L, 24L, 18L, 30L, 33L, 32L, 63L,
22L, 24L), IMMIGRANT = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 1), FAMSIZE = c(2L,
2L, 2L, 3L, 2L, 6L, 2L, 1L, 2L, 1L), HLTH_INS = c(1, 1, 1, 1,
1, 0, 1, 1, 1, 0), HYPERTEN = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0),
SMOKE = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1), PSU = c(2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L)), row.names = c(NA, -10L), class = "data.frame")
#The regression works without adjusting for clustered SE
logit<-logitmfx(HYPERTEN~AGE+IMMIGRANT+FAMSIZE+HLTH_INS+
SMOKE,data=sample,
atmean=TRUE,robust=T)
logit_mfx_coef <- logit$mfxest[,1]
logit_mfx_se <- logit$mfxest[,2]
stargazer(logit, type="text",title = "Predicting Probability of Hypertension",intercept.bottom=FALSE,
coef = logit_mfx_coef,
se = logit_mfx_coef, column.labels="Logit mfx",
digits=4,align=TRUE)
Pass logit$fit
as the first argument to stargazer()
.
The logitmfx()
operation returns a bunch of stuff, but stargazer()
expects a fitted model object (or a data frame) as its first argument.
stargazer(logit$fit, type="text",title = "Predicting Probability of Hypertension",intercept.bottom=FALSE,
coef = logit_mfx_coef,
se = logit_mfx_coef, column.labels="Logit mfx",
digits=4,align=TRUE)
Output:
Predicting Probability of Hypertension
=============================================
Dependent variable:
---------------------------
HYPERTEN
Logit mfx
---------------------------------------------
Constant 0.0000
(0.0000)
AGE
IMMIGRANT
FAMSIZE
HLTH_INS
SMOKE
---------------------------------------------
Observations 10
Log Likelihood -0.0000
Akaike Inf. Crit. 10.0000
=============================================
Note: *p<0.1; **p<0.05; ***p<0.01