I have the dataframe below
and I'm trying to
library(drc)
AR231 <- drm(AR231 ~ conc, data = dosage, fct = LL.4()); summary(AR231)
but I get the error message
Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, : non-finite value supplied by optim
Error in drmOpt(opfct, opdfct1, startVecSc, optMethod, constrained, warnVal, : Convergence failed
Why?? What does this mean?
I'm hoping to build a dose-response curve using my data and receive EC50 values for each subject tested. If anybody could give me some pointers, that would be greatly appreciated.
Below I've attached a similar data frame with the expected results that I'm looking for. Not my work- Not sure what I'm doing wrong.
dosage <- read.csv("MoyingDay1.csv")
> dosage
conc X0G51 X0G63 X0G101 X0G117 X0G191 X0G214 X0G229 X0G402 X0G421 X5G51 X5G63 X5G101 X5G117 X5G191 X5G214 X5G229
1 0.0 4.1 4.3 4.1 4.2 4.1 4.0 4.6 4.3 4.5 4.0 3.9 4.0 3.9 4.2 3.8 4.2
2 0.0 4.0 4.2 4.0 4.1 4.3 4.1 4.4 4.2 4.6 3.9 4.1 4.0 4.0 4.2 3.8 4.2
3 0.0 4.1 4.3 4.1 4.2 4.1 4.1 4.4 4.2 4.6 4.1 4.1 4.0 3.8 4.0 3.9 4.2
4 12.5 2.3 2.4 2.6 3.0 2.9 3.6 4.0 2.4 2.7 2.4 1.9 3.0 3.2 2.7 3.7 3.5
5 12.5 2.4 2.3 3.0 3.0 2.7 3.6 3.9 2.9 2.8 2.4 2.4 2.8 2.9 2.6 3.7 3.5
6 12.5 2.5 2.6 2.9 2.6 2.8 3.5 3.7 2.5 2.9 2.5 2.3 2.9 3.1 2.8 3.7 3.6
7 25.0 1.4 1.4 2.1 2.5 1.9 3.1 3.2 2.1 1.4 1.5 1.6 2.4 2.3 2.5 3.5 3.1
8 25.0 1.7 1.6 2.1 2.2 1.9 3.1 3.3 2.0 2.0 1.4 1.6 2.4 2.3 2.4 3.3 3.1
9 25.0 1.8 1.6 2.4 2.4 1.6 3.2 3.4 2.1 1.6 1.4 1.6 2.4 2.3 2.5 3.4 3.0
10 50.0 0.4 0.9 1.8 1.8 1.0 2.3 2.7 0.9 1.0 0.9 0.7 1.2 1.4 1.4 2.4 2.2
11 50.0 0.4 0.9 1.7 1.4 1.1 2.3 2.6 0.4 0.9 1.1 0.9 1.4 1.6 1.5 2.2 2.3
12 50.0 0.9 0.9 2.3 1.7 0.9 2.4 2.6 0.6 0.8 0.6 1.0 1.5 1.4 1.6 2.2 2.2
13 100.0 0.2 0.1 0.3 0.3 0.2 0.5 1.4 0.4 0.5 0.7 0.2 0.3 0.3 0.3 1.1 1.1
14 100.0 0.2 0.2 0.3 0.3 0.2 0.7 1.0 0.2 0.4 0.7 0.2 0.3 0.3 0.3 0.9 0.0
15 100.0 0.2 0.2 0.3 0.3 0.2 0.7 1.2 0.4 0.6 0.6 0.2 0.4 0.3 0.3 1.1 1.0
16 200.0 0.1 0.1 0.2 0.3 0.3 0.7 0.4 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.3 0.6
17 200.0 0.1 0.2 0.3 0.3 0.2 0.7 0.7 0.2 0.2 0.3 0.2 0.4 0.3 0.3 0.4 0.6
18 200.0 0.2 0.1 0.3 0.3 0.2 0.5 0.6 0.2 0.2 0.3 0.2 0.3 0.3 0.3 0.5 0.3
X5G251 X5G402 X5G421 X10G51 X10G63 X10G101 X10G117 X10G191 X10G214 X10G229 X10G251 X10G402 X10G421
1 4.3 4.3 4.3 4.1 3.8 4.0 4.0 4.1 4.3 3.9 4.0 4.2 3.6
2 4.3 4.3 4.4 4.3 3.6 3.9 4.0 4.2 4.2 4.1 4.0 4.1 4.1
3 4.3 4.3 4.4 4.2 4.0 4.1 4.0 4.1 4.2 4.3 4.0 4.0 4.0
4 2.4 2.9 2.9 2.6 2.9 2.9 2.9 2.6 3.7 3.9 2.8 2.7 2.9
5 2.4 3.1 2.8 3.0 2.6 2.9 2.9 2.4 3.7 3.4 2.8 2.9 2.6
6 2.5 2.9 2.8 3.3 2.8 2.9 2.9 2.9 3.6 3.6 3.0 2.9 2.7
7 1.9 2.4 1.9 1.5 2.4 2.5 2.4 2.0 3.3 3.0 1.6 1.2 1.6
8 2.0 1.9 1.9 2.0 2.2 2.5 2.5 2.2 3.3 2.9 1.6 1.4 1.6
9 1.9 2.1 1.9 2.3 2.6 2.5 2.4 1.5 3.4 3.0 1.6 1.4 1.7
10 1.0 1.0 0.9 1.1 1.2 1.4 1.6 0.8 2.3 2.2 0.7 0.9 0.9
11 0.9 1.0 0.9 1.0 1.2 1.4 1.6 0.7 2.4 2.6 1.0 0.7 0.7
12 0.9 0.9 1.1 0.9 1.4 1.6 1.6 0.9 2.5 2.3 0.9 0.9 0.9
13 0.5 0.3 0.5 0.5 0.7 0.3 0.1 0.0 0.5 0.4 0.0 0.0 0.4
14 0.4 0.3 0.5 0.6 0.5 0.3 0.3 0.0 0.4 0.4 0.0 0.0 0.6
15 0.3 0.5 0.5 0.5 0.7 0.3 0.3 0.0 0.5 0.3 0.0 0.0 0.5
16 0.0 0.0 0.5 0.1 0.4 0.1 0.3 0.0 0.5 0.4 0.0 0.0 0.2
17 0.0 0.1 0.3 0.1 0.3 0.3 0.4 0.0 0.5 0.4 0.0 0.0 0.2
18 0.1 0.1 0.2 0.1 0.3 0.4 0.2 0.0 0.6 0.5 0.0 0.0 0.2
> X0G51.m1 <- drm(X0G51 ~ conc, data = dosage, fct = LL.4()); summary(X0G51.m1)
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
b:(Intercept) 1.428520 0.176953 8.072886 0.000
c:(Intercept) -0.064166 0.129133 -0.496900 0.627
d:(Intercept) 4.055672 0.103229 39.288115 0.000
e:(Intercept) 17.429622 1.355903 12.854626 0.000
I think your main problem is probably that the NoSHAM values in your concentration column are making the whole column into a non-numeric variable.
Replicating the first two columns of your data frame:
dosage <-data.frame(
conc = c(10^(rep(c(-3:2), each = 3)), rep ("NoSHAM", 3)),
AR231 = c(0.98, 0.89, 0.96, 0.84, 0.82, 0.85, 0.32, 0.4, 0.37, 0, 0.4, rep(0,10)))
This works fine if I use as.numeric() to convert concentrations back to numeric (the "NoSHAM" values will get converted to NA, and those observations will then be automatically dropped):
fit <- drm(AR231 ~ as.numeric(conc), data = dosage, fct = LL.4()); summary(fit)
You should check and make sure that your conc variable is currently of type character, not factor: if it is factor you must use as.numeric(as.character(conc)) (rather than just as.numeric(conc)) to convert back, otherwise weird/wrong stuff will happen.
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
b:(Intercept) 0.8547343 0.2589153 3.3012 0.00525 **
c:(Intercept) 0.0073816 0.0443913 0.1663 0.87031
d:(Intercept) 0.9861405 0.0761105 12.9567 3.473e-09 ***
e:(Intercept) 0.0596658 0.0201075 2.9673 0.01019 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.09528289 (14 degrees of freedom)
(The "14 degrees of freedom" reported is consistent with the "NoSHAM" rows being dropped: the data set has 21 rows, minus 3 NoSHAM rows = 18. The df listed is the residual df, which is (18 observations - 4 model parameters) = 14.)