Inverse probability weights in r

Question

I'm trying to apply inverse probability weights to a regression, but lm() only uses analytic weights. This is part of a replication I'm working on where the original author is using pweight in Stata, but I'm trying to replicate it in R. The analytic weights are providing lower standard errors which is causing problems with some of my variable being significance.

I've tried looking at the survey package, but am not sure how to prepare a survey object for use with svyglm(). Is this the approach I want, or is there an easier way to apply inverse probability weights?

dput :

data <- structure(list(lexptot = c(9.1595012302023, 9.86330744180814, 
8.92372556833205, 8.58202430280175, 10.1133857229336), progvillm = c(1L, 
1L, 1L, 1L, 0L), sexhead = c(1L, 1L, 0L, 1L, 1L), agehead = c(79L, 
43L, 52L, 48L, 35L), weight = c(1.04273509979248, 1.01139605045319, 
1.01139605045319, 1.01139605045319, 0.76305216550827)), .Names = c("lexptot", 
"progvillm", "sexhead", "agehead", "weight"), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -5L))

Linear Model (using analytic weights)

prog.lm <- lm(lexptot ~ progvillm + sexhead + agehead, data = data, weight = weight)
summary(prog.lm)

Answer 1

Alright, so I figured it out and thought I would update the post incase others were trying to figure it out. It's actually pretty straightforward.

data$X <- 1:nrow(data)
des1 <- svydesign(id = ~X,  weights = ~weight, data = data)
prog.lm <- svyglm(lexptot ~ progvillm + sexhead + agehead, design=des1)
summary(prog.lm)

Standard errors are now correct.