# Hi all, I am relatively new to performing statistics with R and now I have a problem when trying to do a probit analysis for dose-response. # Underneath is the table I am using. dose total dead percentage survive <dbl> <dbl> <dbl> <dbl> <dbl> 1 0.0825 100 20 0.2 80 2 0.165 100 26.7 0.267 73.3 3 0.248 100 30 0.3 70 4 0.33 100 33.3 0.333 66.7 5 0.412 100 40 0.4 60 6 0.495 100 53.3 0.533 46.7 7 0.578 100 65 0.65 35 8 0.66 100 73.3 0.733 26.7 9 0.742 100 80 0.8 20 # First I attach the data with the function attach attach(data) # Then I create a matrix of dead and survival y=cbind(dead,survive) # However, when I try to create binomial glm (probit model), I get a specific error model.results = glm(data = data, y ~ dose,binomial(link="probit")) Error in model.frame.default(formula = y ~ dose, data = data, : variable lengths differ (found for 'dose') # Is there someone who can help me with this error/problem, any help would be appreciated. Thanks in advance!
is the column survive or survival. ?
sorry my bad, i made a typo in the question, but in my R file both in the column and the ybind function I used survive.
I cant reproduce your issue . as :
data <- data.frame( dose = c(0.0825, 0.165, 0.248, 0.33, 0.412, 0.495, 0.578, 0.66, 0.742), total = c(100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L), dead = c(20, 26.7, 30, 33.3, 40, 53.3, 65, 73.3, 80), percentage = c(0.2, 0.267, 0.3, 0.333, 0.4, 0.533, 0.65, 0.733, 0.8), survive = c(80, 73.3, 70, 66.7, 60, 46.7, 35, 26.7, 20) ) attach(data) # Then I create a matrix of dead and survival y=cbind(dead,survive) # However, when I try to create binomial glm (probit model), I get a specific error model.results = glm(data = data, y ~ dose,binomial(link="probit"))
In a fresh session, does not give a length differ error; but I'm also of the opinion that the code exhibits poor practice, and is probably worth starting over with.
The problems I see begin with the mixing of paradigms of where/how the data is loaded that glm is asked to process.
we have a data.frame passed in to the data param of glm, great but the formula involves a y that is not present there, etc. why do this ? what is the purpose/intent behind why you created 'y', are your trying to simultaneously predict two outputs from a single input ? you want to know dead and survive from a given dose ? Your dead and survive seem to be percentages which add up pairwise to total, so you would probable be best just predicting the dead % ( or surv %) from dose, and if you need the other stat its just the different of 100% - other stat;
its glm probit idea for this analysis, I'm unsure, probably you would have more rigor, if you had data of the actual trials / cases that your data is about, and use the glm weights, and have integer survival ( or death) cases depending on which way round you choose to frame the analysis.
Thanks for your reaction.
When producing the commands in a new session the problem did not occur and I think I got the right values of the LC50 and LC90 values that I wanted. Since I am just starting with R, most of the times I use examples that I find on the internet. For this specific probit analysis I used the example from this link: https://groups.google.com/g/ggplot2/c/Ssmm276zSnI?pli=1
I hope you may get more information about what I am doing by reading this link. Furthermore, to make it a little bit clearer for you I will provide you the follow up commands after making the model.results.
This are the following commands:
#create matrix of dead and survival
y = cbind(dead,survive)
#create binomial glm (probit model)
model.results = glm(data = data, y ~ dose,binomial(link="probit"))
#use function from MASS to calculate LC
I appreciate the link;
The reply on that link is particularly useful, it echoes some of my comments; Also I think it does have a key difference in the glm, which is that they have actual case counts of survival and death; and you dont; therefore you may be biasing your analysis; if there were not the same volume of total people evaluated at each dose measure.
Ah okay, but for my analysis I got my results in percentages, were I transformed this percentages into dead and survive. So the total will always be 100 and when for example 20% of the plants have died, dead will be 20 and survive 80. Do you think I can still use this method of analysis for this kind of results or should I try another?
You are introducing bias
How can I avoid this bias?
Have the counts in the way the 2nd replier on that thread does it
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