Logistic Regression Error

[ayommanuel]

For discussions related to modeling, machine learning and deep learning. Related packages include caret, modelr, yardstick, rsample, parsnip, tensorflow, keras, cloudml, and tfestimators.

Hi, I am doing logistic regression on a class project but i am getting the error below;

AVlog_model <- glm(Satisfaction~., data=AVdata_train, family = "binomial")
Error in family$linkfun(mustart) :
Argument mu must be a nonempty numeric vector

Can anyone help out?

[nirgrahamuk]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[ayommanuel]

#### Data Modelling

round(prop.table(table(AVdata$Satisfaction))*100,digit=1)
for(i in 2:ncol(AVdata)){
  if(class(AVdata[,i])=="factor"){
    AVdata[,i] <- as.integer(AVdata[,i])
  }
}
normalize <- function(x){
  return ((x-min(x))/(max(x)-min(x)))
}
write.csv(AVdata, file = "AVdata.csv")
AVdata <- read.csv("AVdata.csv",header = T)
AVdata <- AVdata[,-1]
str(AVdata)


#### Let's Convert categorical to integer

## Gender
levels(AVdata$Gender)
AVdata$Gender<-factor(AVdata$Gender,levels=c("Female","Male"),labels=c(0,1))
AVdata$Gender <- as.integer(factor(AVdata$Gender))

## CustomerType
levels(AVdata$CustomerType)
AVdata$CustomerType<-factor(AVdata$CustomerType,levels=c("disloyal Customer","Loyal Customer"),labels=c(0,1),ordered = T)
AVdata$CustomerType <- as.integer(factor(AVdata$CustomerType))

##TypeTravel
levels(AVdata$TypeTravel)
AVdata$TypeTravel<-factor(AVdata$TypeTravel,levels=c("Business travel","Personal Travel"),labels=c(0,1))
AVdata$TypeTravel <- as.integer(factor(AVdata$TypeTravel))

##Class
levels(AVdata$Class)
AVdata$Class<-factor(AVdata$Class,levels=c("Eco","Eco Plus","Business"),labels=c(0,1,2),ordered = T)
AVdata$Class <- as.integer(factor(AVdata$Class))

AVdata_n <- as.data.frame(lapply(AVdata[2:22], normalize))
AVdata_n <- cbind(AVdata_n, Satisfaction=AVdata$Satisfaction)

set.seed(5)
partition <- createDataPartition(y = AVdata_n$Satisfaction, p=0.7, list= F)
AVdata_train <- AVdata_n[partition,]
AVdata_test <- AVdata_n[-partition,]
write.csv(AVdata_train, file = "AVdata_train.csv")
write.csv(AVdata_test, file = "AVdata_test.csv")


#### Logistic regression
AVlog_model <- glm(Satisfaction~., data=AVdata_train, family = "binomial")
summary(AVlog_model)
AVlog_preds <- predict(AVlog_model, AVdata_test[,1:22], type = "response")
head(AVlog_preds)
AVlog_class <- array(c(99))
for (i in 1:length(AVlog_preds)){
  if(AVlog_preds[i]>0.5){
    AVlog_class[i]<-"satisfied"
  }else{
    AVlog_class[i]<-"neutral or dissatisfied"
  }
}

##Creating a new dataframe containing the actual and predicted values.
AVlog_result <- data.frame(Actual = AVdata_test$Satisfaction, Prediction = AVlog_class)
mr1 <- confusionMatrix(as.factor(AVlog_class),AVdata_test$Satisfaction, positive = "satisfied")
mr1

#### Linear Discriminant Analysis
pairs(AVdata_train[,1:5], main="Predict ", pch=22, bg=c("red", "blue")[unclass(AVdata_train$Satisfaction)])
AVlda_model <- lda(Satisfaction ~ ., AVdata_train)
AVlda_model
## Let's predict the model
AVlda_preds <- predict(AVlda_model, AVdata_test)
mr2 <- confusionMatrix(AVdata_test$Satisfaction, AVlda_preds$class, positive = "satisfied")
mr2

[technocrat]

@ayommanuel

Argument mu must be a nonempty numeric vector

Confirm that AVdata_train is in the namespace and that it contains no NA values.

[nirgrahamuk]

You have provided a lot of code but no data... therefore this is not a reproducible example.
Can you reread the guide on making reprex and try again.
focus on the dput() part.