Displaying 2 results from an estimated 2 matches for "remarkebly".
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remarkably
2008 Dec 08
1
Multivariate kernel density estimation
I would like to estimate a 95% highest density area for a multivariate
parameter space (In the context of anova). Unfortunately I have only
experience with univariate kernel density estimation, which is remarkebly
easier :)
Using Gibbs, i have sampled from a posterior distirbution of an Anova model
with k means (mu) and 1 common residual variance (s2). The means are
independent of eachother, but conditional on the residual variance. So now I
have a data frame of say 10.000 iterations, and k+1 parameters.
I...
2010 Dec 15
1
lmList and lapply(... lm) different std. errors
...Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Residual standard error: 0.8979 on 37 degrees of freedom
Multiple R-squared: 0.1114, Adjusted R-squared: 0.08739
F-statistic: 4.639 on 1 and 37 DF, p-value: 0.03784
It should also be possible to use the lmList function, but remarkebly, I get
the same estimates, but different Std. Errors... I used the following code:
modlst <- lmList(X2 ~ X3 | VARIABLE2, TRY)
summary(modlst)
Which produces
Call:
Model: X2 ~ X3 | VARIABLE2
Data: TRY
Coefficients:
(Intercept)
Estimate Std. Error t value Pr(>|t|)...