Apply the multiple linear regression model for the dataset rotifer in R
时间: 2023-11-22 15:54:13 浏览: 35
To apply the multiple linear regression model for the dataset "rotifer" in R, you can follow these steps:
1. Load the dataset into R using the `read.csv()` function.
```
rotifer <- read.csv("path/to/dataset.csv")
```
2. Create a linear regression model using the `lm()` function. In this case, we will use "abundance" as the response variable and "temperature", "phosphorus", and "phytoplankton" as the predictor variables.
```
model <- lm(abundance ~ temperature + phosphorus + phytoplankton, data = rotifer)
```
3. Check the summary of the model using the `summary()` function.
```
summary(model)
```
This will display the coefficients, standard errors, t-values, and p-values for each predictor variable. You can use this information to assess the significance and strength of the relationship between each predictor variable and the response variable.
4. Make predictions using the model using the `predict()` function. For example, to predict the abundance of rotifers at a temperature of 20°C, a phosphorus concentration of 0.5 mg/L, and a phytoplankton concentration of 5 µg/L, you can use the following code:
```
newdata <- data.frame(temperature = 20, phosphorus = 0.5, phytoplankton = 5)
prediction <- predict(model, newdata)
```
This will give you the predicted abundance of rotifers under these conditions.