2.802128
I am not very confident about the results though.
A.K.
From: phale_chuong gionho
To: "r-help@r-project.org"
Cc:
Sent: Monday, June 4, 2012 10:52 AM
Subject: [R] probit analysis
Hello!
> I have a very simple set of data and I would like to analyze
> them with
Hello!
> I have a very simple set of data and I would like to analyze
> them with probit analysis.
> The data are: X Event Trial
> 1210 8 8
> 121 6 8
> 60.5 6 8
> I want to estimate the value of X that will give a 95% hit
> rate (Event/Trial) and the corresponding 95%
I am encountering a problem with the calculation of Fieller and Delta
Method confidence intervals when performing probit analysis on
simulated data; my code is included below. I am testing 5 dose
groups, with log doses (-0.2, -0.1, 0, 0.1, 0.2) and (1.8, 1.9, 2,
2.1, 2.2) so that the log(LD50) are
Hi:
The MASS package has a function dose.p() to produce a CI for ED50, ED90 or
EDp in general (0 < p < 100). It takes a model object (presumably from a
suitable logistic regression) as input. You could always take the code
already available and adapt it to your situation or you could investigate
On Nov 22, 2010, at 2:24 PM, Kenney, Colleen T CTR USA AMC wrote:
Classification: UNCLASSIFIED
Caveats: NONE
A similar question has been posted in the past but never answered. My
question is this: for probit analysis, how do you program a 95%
confidence interval for the LD50 (or LC50, ec50,
Classification: UNCLASSIFIED
Caveats: NONE
A similar question has been posted in the past but never answered. My
question is this: for probit analysis, how do you program a 95%
confidence interval for the LD50 (or LC50, ec50, etc.), including a
heterogeneity factor as written about in "Probit A
Dear all,
I have try to use mle() from package stats4 to estimate parameters of
the following model:
p = Pr(Y = 0) = C + (1 - C)F(x'\beta)
a probit model with natural response.
The log-likelihood function is:
fr <- function(c, alpha, beta) {
P <- c + (1-c) * pnorm(alpha + beta * x)
P <- pmax
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