# What is a 4 parameter logistic curve?

## What is a 4 parameter logistic curve?

Four parameter logistic (4PL) curve is a regression model often used to analyze bioassays such as ELISA. They follow a sigmoidal, or “s”, shaped curve. This type of curve is particularly useful for characterizing bioassays because bioassays are often only linear across a specific range of concentration magnitudes.

How many types of parameters are there?

Out Parameters. Default Parameters or Optional Arguments (C# 4.0 and above) Dynamic parameter (dynamic keyword). Value parameter or Passing Value Types by Value (normal C# method param are value parameter)

### What is HillSlope IC50?

The Hill slope describes the slope of the sigmoidal curve between these two plateaus. The EC50 (or IC50) refer to a concentration of agonist (or antagonist) required to increase (or reduce) the measured response to half – or 50% – of its maximal value.

What are the parameters of a four parameter logistic regression?

As the name implies, it has 4 parameters that need to be estimated in order to “fit the curve”. The model fits data that makes a sort of S shaped curve. The equation for the model is: Of course x = the independent variable and y = the dependent variable just as in the linear model above. The 4 estimated parameters consist of the following:

## When to use a 4 parameter logistic or 5PL?

The 4 Parameter Logistic or 4PL is sufficient if you know that your sigmoidal curve is symmetrical around the inflection point. If not, then the 5PL would be a better choice.

How to solve for X in 4 parameter model?

The 4 parameter model is: y = (a-d)/[1+(x/c)^b]+d. You can solve for x: x = c ((-a + y)/(d – y))^(1/b) x is log concentrations, y is OD signal read from your instrument.

### How to fit a 4 parameter Elisa curve?

You can fit an ELISA curve using free software called R. The 4 parameter model is: y = (a-d)/[1+(x/c)^b]+d. You can solve for x: x = c ((-a + y)/(d – y))^(1/b) x is log concentrations, y is OD signal read from your instrument. Get your data into a dataframe and take the log of the concentration (R code in red):