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Vallo Varik
32drugs
Commits
d09ac955
Commit
d09ac955
authored
Jun 07, 2022
by
Vallo Varik
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fea2cf0e
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README.Rmd
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d09ac955
...
...
@@ -135,15 +135,16 @@ Good, filtering for `Time_h` between 9.5 and 10.5 h gives a desired result.
intervals?
10. There is one more thing. An intricacy. We have fitted and plotted (i.e.
think about
it
) the `x`, the concentration, in logarithmic scale, but the
think about) the `x`, the concentration, in logarithmic scale, but the
IC~50~ is in linear scale. Mostly, it does not matter much. You can see
above, however, that the lower confidence interval is 5x lower than IC~95~
and the upper limit is less than 2x higher. One side is 5 away, the other
and the upper limit is less than 2x higher. One side is 5
x
away, the other
less than 2x. To fix that, one could estimate IC~50~ in log scale
(substitute IC~50~ in the 4-parameter logistic regression with
log(IC~50~)). One might have to take some time to think about it what that means. Luckily, `drm` makes all this easy. You fit the model exactly as
you did before, but this time, set `fct` to `LL2.4()`. Finally, when
calculating MIC, the confidence interval should be set to "fls"
(`interval
= "fls"`).
calculating MIC, the confidence interval should be set to "fls"
(`interval
= "fls"`).
Now the MIC (IC~95~) should be the same (33 µM), but the confidence interval are symmetric, about 2x lower and 2x higher.
Now the MIC (IC~95~) should be the same you got with `LL.4` (33 µM), but
the confidence intervals are symmetric, about 2x lower and 2x higher.
README.md
View file @
d09ac955
...
...
@@ -142,18 +142,19 @@ result.
confidence intervals?
4.
There is one more thing. An intricacy. We have fitted and plotted
(i.e. think about it) the
`x`
, the concentration, in logarithmic
scale, but the IC
<sub>
50
</sub>
is in linear scale. Mostly, it does
not matter much. You can see above, however, that the lower
confidence interval is 5x lower than IC
<sub>
95
</sub>
and the upper
limit is less than 2x higher. One side is 5 away, the other less
than 2x. To fix that, one could estimate IC
<sub>
50
</sub>
in log
scale (substitute IC
<sub>
50
</sub>
in the 4-parameter logistic
regression with log(IC
<sub>
50
</sub>
)). One might have to take some
time to think about it what that means. Luckily,
`drm`
makes all
this easy. You fit the model exactly as you did before, but this
time, set
`fct`
to
`LL2.4()`
. Finally, when calculating MIC, the
confidence interval should be set to “fls” (
`interval = "fls"`
).
Now the MIC (IC<sub>95</sub>) should be the same (33 µM), but the
confidence interval are symmetric, about 2x lower and 2x higher.
(i.e. think about) the
`x`
, the concentration, in logarithmic scale,
but the IC
<sub>
50
</sub>
is in linear scale. Mostly, it does not
matter much. You can see above, however, that the lower confidence
interval is 5x lower than IC
<sub>
95
</sub>
and the upper limit is
less than 2x higher. One side is 5x away, the other less than 2x. To
fix that, one could estimate IC
<sub>
50
</sub>
in log scale
(substitute IC
<sub>
50
</sub>
in the 4-parameter logistic regression
with log(IC
<sub>
50
</sub>
)). One might have to take some time to
think about it what that means. Luckily,
`drm`
makes all this easy.
You fit the model exactly as you did before, but this time, set
`fct`
to
`LL2.4()`
. Finally, when calculating MIC, the confidence
interval should be set to “fls” (
`interval = "fls"`
).
Now the MIC (IC<sub>95</sub>) should be the same you got with `LL.4`
(33 µM), but the confidence intervals are symmetric, about 2x lower
and 2x higher.
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