Displaying 4 results from an estimated 4 matches for "bovik".
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dovik
2005 Jun 14
2
ordinary polynomial coefficients from orthogonal polynomials?
...gt; pm <- lm(billions ~ poly(decade, 3))
>
> plot(decade, billions, xlim=c(1950,2050), ylim=c(0,1000),
main="average yearly inflation-adjusted dollar cost of extreme weather
events worldwide")
> curve(predict(pm, data.frame(decade=x)), add=TRUE)
> # output: http://www.bovik.org/storms.gif
>
> summary(pm)
Call:
lm(formula = billions ~ poly(decade, 3))
Residuals:
1 2 3 4 5
0.2357 -0.9429 1.4143 -0.9429 0.2357
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.800 0.882 15.647...
2005 Jun 17
2
adjusted R^2 vs. ordinary R^2
I thought the point of adjusting the R^2 for degrees of
freedom is to allow comparisons about goodness of fit between
similar models with different numbers of data points. Someone
has suggested to me off-list that this might not be the case.
Is an ADJUSTED R^2 for a four-parameter, five-point model
reliably comparable to the adjusted R^2 of a four-parameter,
100-point model? If such values
2005 Apr 23
1
start values for nls() that don't yield singular gradients?
I'm trying to fit a Gompertz sigmoid as follows:
x <- c(15, 16, 17, 18, 19) # arbitrary example data here;
y <- c(0.1, 1.8, 2.2, 2.6, 2.9) # actual data is similar
gm <- nls(y ~ a+b*exp(-exp(-c*(x-d))), start=c(a=?, b=?, c=?, d=?))
I have been unable to properly set the starting value '?'s. All of
my guesses yield either a "singular gradient" error if they
2005 Apr 11
1
glm family=binomial logistic sigmoid curve problem
I'm trying to plot an extrapolated logistic sigmoid curve using
glm(..., family=binomial) as follows, but neither the fitted()
points or the predict()ed curve are plotting correctly:
> year <- c(2003+(6/12), 2004+(2/12), 2004+(10/12), 2005+(4/12))
> percent <- c(0.31, 0.43, 0.47, 0.50)
> plot(year, percent, xlim=c(2003, 2007), ylim=c(0, 1))
> lm <- lm(percent ~ year)