Skip to main content
Cross Validated

Return to Question

Tweeted twitter.com/StackStats/status/1166228987158380544
added 1 character in body
Source Link
Dimitris Rizopoulos
Dimitris Rizopoulos
  • 21.5k
  • 2
  • 25
  • 51

Let's say that I am trying to predict the Sepal Length in Iris data from Sepal Width, Petal Width, and Petal Length variables.

Say, we noted that Petal Length and Petal Width are grouped as described by the Species variable.

I am using the following mixed-effecteffects model for this.

libraray(lme4)

fit <- lmer(Sepal.Length ~ Sepal.Width + Petal.Length +  Petal.Width +
             (1 + Petal.Length + Petal.Width | Species), data = iris)

My question is how to explain this model concisely in plain language?

For example: In our mixed-effect model, we considered Petal Length and Petal Width to be random effect variables as they may contain variation that can be explained from the Species variable. Further, we assumed all three Sepal Width, Petal Length, and Petal Width are fixed-effect variables.

Let's say that I am trying to predict the Sepal Length in Iris data from Sepal Width, Petal Width, and Petal Length variables.

Say, we noted that Petal Length and Petal Width are grouped as described by the Species variable.

I am using the following mixed-effect model for this.

libraray(lme4)

fit <- lmer(Sepal.Length ~ Sepal.Width + Petal.Length +  Petal.Width +
             (1 + Petal.Length + Petal.Width | Species), data = iris)

My question is how to explain this model concisely in plain language?

For example: In our mixed-effect model, we considered Petal Length and Petal Width to be random effect variables as they may contain variation that can be explained from the Species variable. Further, we assumed all three Sepal Width, Petal Length, and Petal Width are fixed-effect variables.

Let's say that I am trying to predict the Sepal Length in Iris data from Sepal Width, Petal Width, and Petal Length variables.

Say, we noted that Petal Length and Petal Width are grouped as described by the Species variable.

I am using the following mixed-effects model for this.

libraray(lme4)

fit <- lmer(Sepal.Length ~ Sepal.Width + Petal.Length +  Petal.Width +
             (1 + Petal.Length + Petal.Width | Species), data = iris)

My question is how to explain this model concisely in plain language?

For example: In our mixed-effect model, we considered Petal Length and Petal Width to be random effect variables as they may contain variation that can be explained from the Species variable. Further, we assumed all three Sepal Width, Petal Length, and Petal Width are fixed-effect variables.

Source Link
kangaroo_cliff
kangaroo_cliff
  • 201
  • 2
  • 13

Explaining Fixed and Random Effects

Let's say that I am trying to predict the Sepal Length in Iris data from Sepal Width, Petal Width, and Petal Length variables.

Say, we noted that Petal Length and Petal Width are grouped as described by the Species variable.

I am using the following mixed-effect model for this.

libraray(lme4)

fit <- lmer(Sepal.Length ~ Sepal.Width + Petal.Length +  Petal.Width +
             (1 + Petal.Length + Petal.Width | Species), data = iris)

My question is how to explain this model concisely in plain language?

For example: In our mixed-effect model, we considered Petal Length and Petal Width to be random effect variables as they may contain variation that can be explained from the Species variable. Further, we assumed all three Sepal Width, Petal Length, and Petal Width are fixed-effect variables.