R tries hard to keep you from committing scientific abuse.
As stated, your problem seems to me akin to
1. Given that a man's age can be modelled as a function
of the grayness of his hair,
2. predict a man's age from the temperature in Barcelona.
Your calibration relates 'abs' and 'conc'. Now you want
to predict 'abs' from _'hours'_ (I think). I suspect that
concentration is actually related to time and this is
the missing link that you'll have to provide.
BTW, I'm surprised that you didn't find the requirement
for 'newdata' to be a data frame on the predict.lm help
page - it's pretty clearly stated there.
Peter Ehlers
On 2012-03-27 10:24, Nederjaard wrote:
Hello,
I'm new here, but will try to be as specific and complete as possible. I'm
trying to use “lm“ to first estimate parameter values from a set of
calibration measurements, and then later to use those estimates to calculate
another set of values with “predict.lm”.
First I have a calibration dataset of absorbance values measured from
standard solutions with known concentration of Bromide:
stds
abs conc
1 -0.0021 0
2 0.1003 200
3 0.2395 500
4 0.3293 800
On this small calibration series, I perform a linear regression to find the
parameter estimates of the relationship between absorbance (abs) and
concentration (conc):
linear1<- lm(abs~conc, data=stds)
summary(linear1)
Call:
lm(formula = abs ~ conc, data = stds)
Residuals:
1 2 3 4
-0.012600 0.006467 0.020667 -0.014533
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.050e-02 1.629e-02 0.645 0.58527
conc 4.167e-04 3.378e-05 12.333 0.00651 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02048 on 2 degrees of freedom
Multiple R-squared: 0.987, Adjusted R-squared: 0.9805
F-statistic: 152.1 on 1 and 2 DF, p-value: 0.00651
Now I come with another dataset, which contains measured absorbance values
of Bromide in solution:
brom
hours abs
1 -1.0 0.0633
2 1.0 0.2686
3 5.0 0.2446
4 18.0 0.2274
5 29.0 0.2091
6 42.0 0.1961
7 53.0 0.1310
8 76.0 0.1504
9 91.0 0.1317
10 95.5 0.1169
11 101.0 0.0977
12 115.0 0.1023
13 123.5 0.0879
14 138.5 0.0724
15 147.5 0.0564
16 163.0 0.0495
17 171.0 0.0325
18 189.0 0.0182
19 211.0 0.0047
20 212.5 NA
21 815.5 -0.2112
22 816.5 -0.1896
23 817.5 -0.0783
24 818.5 0.2963
25 819.5 0.1448
26 839.5 0.0936
27 864.0 0.0560
28 888.0 0.0310
29 960.5 0.0056
30 1009.0 -0.0163
The values in column brom$abs, measured on 30 subsequent points in time need
to be calculated to Bromide concentrations, using the previously established
relationship “linear1”.
At first, I thought it could be done by:
predict.lm(linear1, brom$abs)
Error in eval(predvars, data, env) :
numeric 'envir' arg not of length one
But, R gives the above error message. Then, after some searching around on
different fora and R-communities (including this one), I learned that the
“newdata” in “predict.lm” actually needs to be coerced into a separate
dataframe. Thus:
mabs<- data.frame(Abs = brom$abs)
predict.lm(linear1, mabs)
Error in eval(expr, envir, enclos) : object 'conc' not found
Again, R gives an error...probably because I made an error, but I truly fail
to see where. I hope somebody can explain to me clearly what I'm doing wrong
and what I should do to instead.
Any help is greatly appreciated, thanks !
--
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