I don't know if you've gotten any follow up, but here are some quick reactions:
1) You make reference to the columns of y but your dput(y) does not
provide columns,
2) It's still not clear to me what all this data actually means? Do
you have multiple observations of the dependent variable
When I tried dput function, the result was this:
dput(x)
c(20, 200, 2000, 2)
dput(y)
c(0.45, 0.05, 0.5, 0.4, 0, 0.5, 0.4, 0.05, 0.4, 0.25, 0.35, 0.5,
0.05, 0.4, 0.5, 0.5, 0.5, 0.25, 0.85, 0.5, 0.5, 0.5, 0.25, 0.4,
0.25, 0.25, 0.4, 0.25, 0.5, 0.15, 0.25, 0.1, 0.25, 0.25, 0.015,
0.4, 0.5,
Hello,
I am trying to get a linear model of y ~ log(x).
* lm (y~log(x))*
However, I always get an error report:
/Error in model.frame.default(formula = y ~ log(x), drop.unused.levels =
TRUE) :
variable lengths differ (found for 'log(x)')/
*Here was my y:*
y
[1]0.4500.050
You are trying to regress ~372 observations of the dependent against
~4 observations of the independent variable. Ask yourself again if
this makes sense.
A further hint might be given by this
y = rnorm(5); x = y[1:4]
lm(y~x)
Michael
On Mon, Oct 24, 2011 at 11:13 AM, Julie
X and y must have the same number of elements, and NA values must be removed
(?na.omit)
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The variable y is made of four columns, each paired to 20, 200, 2000 or 20
000.
y - c(rdiktator20, rDiktator200, rDikt2000, rDikt2)
So I guess the problem is in the fact that I did not specify it correctly,
is it so? How can I tell R properly that one part of y matches to one part
of x?
The
Could you dput() the structure of x and y: I'm having trouble
visualizing how your data is set up.
Michael
On Mon, Oct 24, 2011 at 12:07 PM, Julie julie.novak...@gmail.com wrote:
The variable y is made of four columns, each paired to 20, 200, 2000 or 20
000.
y - c(rdiktator20, rDiktator200,
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