null <- glm(Status ~ Idade, family = "binomial", data = train_data)
Error in model.frame.default(formula = Status ~ Age, data = train_data, :
variable lengths differ (found for 'Age')
When I run glm I get no errors. All the variables are in a single dataset and there are no missing values. I divided de file in:
dim(train_data)
dim(test_data)
The error only occurs when i use the train_data and the test_data. When I use the whole file, I don't have errors.
How do I solve the problem?
Did you use anything like
attach(data)
If so, your response Status would be based on the full dataset.
Related
I made a Multinomial Logistic Regression model using library(nnet) in R.
I notice I, one, get an error, and two, after using the step() function, my predictor variables convert into the variable I'm attempting to predict, solely (Depression).
summary(multinom_model)$call
produces:
multinom(formula = out ~ ., data = train)
Warning message:
In sqrt(diag(vc)) : NaNs produced
BUT
mult_model <- step(multinom_model, trace = FALSE)
summary(mult_model)$call
this code produces:
multinom(formula = out ~ Depressed, data = train)
Why is this happening? Also, both models predict the same output on the test data. Does it have to do with the warning message? How do I fix that?
in my GLM model I tested for not significant variables. One of variable in Geography where values woould be country names. So test showed me that variable GeographyCountryA is significant so I want to keep it but not sure how to properly address it ? below gives an error:
Error in eval(predvars, data, env) : object 'GeographyGermany' not
found
Code:
churn_model_rl <- glm(data = train_churn, formula = Exited ~ Age+
+NumOfProducts+IsActiveMember+Gender+GeographyGermany,family = binomial(link = "logit"))
I am trying to run a logit regression and I tried two approaches:
m.logit <- glm(p4 ~ scale(log(gdp,orthodox,swb)),
data = happiness,
family = binomial("logit"))
summary(m.logit)
Throws: Error in summary(m.logit) : object 'm.logit' not found
While
m1.logit <- glm(p4 ~ gdp + orthodox + swb, family = binomial(link = "logit"), data = happiness)
Throws: Error in eval(family$initialize) : y values must be 0 <= y <= 1
I kind of understood the errors (in the former case m.logit is not found, and in the latter, I need to transform the variables I think...) but don't know how to solve it...
Any help?
I am new to R and I am trying to create a logit model. I created a train and test set for my data and when I am trying to create a logit model, I keep getting the following error message:
model <- glm(mortDefault2001$default ~.,family=binomial(link='logit'),data=train)
Error in model.frame.default(formula = mortDefault2001$default ~ .,
data = train,:variable lengths differ (found for 'creditScore')
What am I doing wrong/what can I do to fix this to run the model?
This is the code I used to create the test and train sets:
data <- subset(mortDefault2001,select=c(1,2,3,4,6))
train <- data[1:80000,]
train <- data[1:80000,]
test <- data[80001:99999,]
model <- glm(mortDefault2001$default ~.,family=binomial(link='logit'),data=train)
Error in model.frame.default(formula = mortDefault2001$default ~ ., data = train, :
variable lengths differ (found for 'creditScore')
#Logistic Regression
glm.fit <- glm(recent_cannabis_use~.,data = drug_use_train, family = binomial)
summary(glm.fit)
predict(glm.fit, with(drug_use_train, data.frame(Gender = "Male")), type = "response")
Trying to find the predicted probability for recent_canabis_use for a male.
You should use predict(glm.fit, newdata = data.frame(Gender = "Male")). Using with in this case is not warranted, since you are not accessing any of the variables in drug_use_train.
Note that this assumes your formula is, upon expansion, recent_cannabis_use ~ Gender. If you have other variable and you want to explore only the effect of Gender, you will need to set (pre-calculate or make up) all other variables to some fixed value (remember how coefficients are interpreted - change in y with one unit change of x, provided everything else stays the same). See for example this post.