drc:: 'vmmin' 中的优化初始值不是有限的
drc:: optim initial value in 'vmmin' is not finite
我想对 drc
R
包中的以下数据进行 log-logistic
回归。但是,我的代码抛出以下错误。
df1 <-
structure(list(Temp = c(15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 20L, 20L, 20L, 20L, 20L, 20L, 25L, 25L, 25L, 25L, 30L, 30L,
30L, 30L, 35L, 35L, 35L, 35L, 40L, 40L, 40L, 40L), Start = c(0L,
24L, 48L, 72L, 96L, 120L, 144L, 168L, 192L, 0L, 24L, 48L, 72L,
96L, 120L, 0L, 24L, 48L, 72L, 0L, 24L, 48L, 72L, 0L, 24L, 48L,
72L, 0L, 24L, 48L, 72L), End = c(24, 48, 72, 96, 120, 144, 168,
192, Inf, 24, 48, 72, 96, 120, Inf, 24, 48, 72, 96, 24, 48, 72,
Inf, 24, 48, 72, Inf, 24, 48, 72, Inf), Germinated = c(0L, 0L,
1L, 3L, 3L, 12L, 14L, 12L, 15L, 0L, 11L, 27L, 15L, 3L, 4L, 2L,
30L, 15L, 13L, 6L, 43L, 7L, 4L, 5L, 48L, 3L, 4L, 0L, 31L, 21L,
8L)), .Names = c("Temp", "Start", "End", "Germinated"), row.names = c(NA,
-31L), class = "data.frame")
library(drc)
fm1 <-
drm(
formula = Germinated ~ Start + End
, data = df1
, fct = LL.2()
, type = "event"
, control = drmc(
constr = FALSE
, errorm = TRUE
, maxIt = 1500
, method = "BFGS"
, noMessage = FALSE
, relTol = 1e-07
, rmNA = FALSE
, useD = FALSE
, trace = FALSE
, otrace = FALSE
, warnVal = -1
, dscaleThres = 1e-15
, rscaleThres = 1e-15
)
)
summary(fm1)
您需要按 Temp 对数据进行分组,因为每个 Temp 值的时间段都是重复的。 curveid = Temp
成功了:
fm1 <-drm(data = df1, curveid = Temp,
formula = Germinated ~ Start + End, fct = LL.2(), type = "event",
control = drmc(constr = FALSE, errorm = TRUE, maxIt = 1500, method = "BFGS",
noMessage = FALSE, relTol = 1e-07, rmNA = FALSE, useD = FALSE,
trace = FALSE, otrace = FALSE, warnVal = -1, dscaleThres = 1e-15, rscaleThres = 1e-15))
summary(fm1)
Model fitted: Log-logistic (ED50 as parameter) with lower limit at 0 and upper limit at 1 (2 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
b:15 -6.03055 0.78915 -7.64179 0
b:20 -4.96450 0.60740 -8.17338 0
b:25 -4.43973 0.54904 -8.08639 0
b:30 -4.80876 0.60792 -7.91025 0
b:35 -5.45991 0.69159 -7.89467 0
b:40 -5.43892 0.79772 -6.81811 0
e:15 162.33568 6.10473 26.59177 0
e:20 64.71588 3.08660 20.96674 0
e:25 48.23883 2.68278 17.98090 0
e:30 36.38415 2.04252 17.81337 0
e:35 35.07398 1.85537 18.90405 0
e:40 48.44494 2.21375 21.88366 0
我想对 drc
R
包中的以下数据进行 log-logistic
回归。但是,我的代码抛出以下错误。
df1 <-
structure(list(Temp = c(15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 20L, 20L, 20L, 20L, 20L, 20L, 25L, 25L, 25L, 25L, 30L, 30L,
30L, 30L, 35L, 35L, 35L, 35L, 40L, 40L, 40L, 40L), Start = c(0L,
24L, 48L, 72L, 96L, 120L, 144L, 168L, 192L, 0L, 24L, 48L, 72L,
96L, 120L, 0L, 24L, 48L, 72L, 0L, 24L, 48L, 72L, 0L, 24L, 48L,
72L, 0L, 24L, 48L, 72L), End = c(24, 48, 72, 96, 120, 144, 168,
192, Inf, 24, 48, 72, 96, 120, Inf, 24, 48, 72, 96, 24, 48, 72,
Inf, 24, 48, 72, Inf, 24, 48, 72, Inf), Germinated = c(0L, 0L,
1L, 3L, 3L, 12L, 14L, 12L, 15L, 0L, 11L, 27L, 15L, 3L, 4L, 2L,
30L, 15L, 13L, 6L, 43L, 7L, 4L, 5L, 48L, 3L, 4L, 0L, 31L, 21L,
8L)), .Names = c("Temp", "Start", "End", "Germinated"), row.names = c(NA,
-31L), class = "data.frame")
library(drc)
fm1 <-
drm(
formula = Germinated ~ Start + End
, data = df1
, fct = LL.2()
, type = "event"
, control = drmc(
constr = FALSE
, errorm = TRUE
, maxIt = 1500
, method = "BFGS"
, noMessage = FALSE
, relTol = 1e-07
, rmNA = FALSE
, useD = FALSE
, trace = FALSE
, otrace = FALSE
, warnVal = -1
, dscaleThres = 1e-15
, rscaleThres = 1e-15
)
)
summary(fm1)
您需要按 Temp 对数据进行分组,因为每个 Temp 值的时间段都是重复的。 curveid = Temp
成功了:
fm1 <-drm(data = df1, curveid = Temp,
formula = Germinated ~ Start + End, fct = LL.2(), type = "event",
control = drmc(constr = FALSE, errorm = TRUE, maxIt = 1500, method = "BFGS",
noMessage = FALSE, relTol = 1e-07, rmNA = FALSE, useD = FALSE,
trace = FALSE, otrace = FALSE, warnVal = -1, dscaleThres = 1e-15, rscaleThres = 1e-15))
summary(fm1)
Model fitted: Log-logistic (ED50 as parameter) with lower limit at 0 and upper limit at 1 (2 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
b:15 -6.03055 0.78915 -7.64179 0
b:20 -4.96450 0.60740 -8.17338 0
b:25 -4.43973 0.54904 -8.08639 0
b:30 -4.80876 0.60792 -7.91025 0
b:35 -5.45991 0.69159 -7.89467 0
b:40 -5.43892 0.79772 -6.81811 0
e:15 162.33568 6.10473 26.59177 0
e:20 64.71588 3.08660 20.96674 0
e:25 48.23883 2.68278 17.98090 0
e:30 36.38415 2.04252 17.81337 0
e:35 35.07398 1.85537 18.90405 0
e:40 48.44494 2.21375 21.88366 0