Scratch Notes
josef-pkt edited this page Jun 2, 2012
·
5 revisions
>>> import os
>>> import json
>>> import statsmodels.stats.tests
>>> cur_dir = statsmodels.stats.tests.__path__[0]
>>> fp = open(os.path.join(cur_dir, "results", "influence_lsdiag_R.json"))
>>> lsdiag = json.load(fp)
>>> fp.close()
>>> lsdiag.keys()
[u'cooks', u'std.res', u'dfits', u'std.err', u'std.dev', u'correlation', u'cov.unscaled',
u'stud.res', u'hat', u'cov.scaled']
used in statsmodels\stats\tests\test_diagnostic.py
example : export results from R using json
library(lmtest)
library(sandwich)
library(rjson)
options(digits=20)
mkhtest <- function(ht, name, distr="f") {
cat(name); cat(" = dict(");
cat("statistic="); cat(ht$statistic); cat(", ");
cat("pvalue="); cat(ht$p.value); cat(", ");
cat("parameters=("); cat(ht$parameter, sep=","); cat(",), ");
cat("distr='"); cat(distr); cat("'");
cat(")");
cat("\n\n")
}
d = read.csv("path_to\\statsmodels\\datasets\\macrodata\\macrodata.csv", header=TRUE)
names(d)
dinv <- diff(d$realinvs)
dgdp = diff(d$realgdp)
lint = d$realint[1:202]
tbilrate = d$tbilrate[1:202]
ginv <- 400 * diff(log(d$realinv))
ggdp <- 400 * diff(log(d$realgdp))
d2 = data.frame(ginv, ggdp, lint, tbilrate)
fm_level <- lm(realinv ~ realgdp + realint, data = d)
fm <- lm(ginv ~ ggdp + lint, data=d2)
nw = NeweyWest(fm, lag = 4, prewhite = FALSE, verbose=TRUE)
nw
nw2 = NeweyWest(fm, prewhite = FALSE, verbose=TRUE)
nw2
rt = raintest(fm)
mkhtest(rt, 'raintest', 'f')
jt = jtest(fm, lm(ginv ~ ggdp + tbilrate))
toJSON(jt)