why the predict function does have different rows in R

0 votes
I'm using linear regression to attempt and forecast certain data. Even though there is no fault, it is strange. In order to forecast data2, which only has 1000 rows, I used a linear regression that I generated from data1, which has 4000 rows.

So, in my opinion, the variable's output should only contain 1000 rows. However, the 4000 variables never stop appearing. I have no idea what's wrong.

model = lm(train$latitude train$crashes)
Model, newdata = test, prediction1 = predict
data.frame = csv1
(Predicted = prediction1, Id = c(1:1000))
I infer from this code that it simply needs to forecast "test" data, which consists of 1000 rows. The statement reads, "Arguments suggest varied number of rows: 1000, 4337," though The train data in this instance has 4337 rows, hence the 4337.
Jun 23 in Data Analytics by avinash
• 1,840 points
31 views

1 answer to this question.

0 votes
Alter this sentence:

model = lm(train$latitude train$crashes) to

lm(crashes latitude, data=train) is the model.
The dataset name should ideally only be supplied once in data= when utilising the formula interface, as opposed to being specified for each variable as train$crashes, etc.

The same crashes latitude formula will be used on the new dataset that you have supplied when you execute predict with newdata=.

You should have been alarmed by the following warning I received when attempting to reproduce your problem:

data.frame with the following formulas: train = crashes = rnorm(1000), latitude = sample(1:1000), test = crashes = rnorm(4377), sample(1:4377)

lm(train$crashes train$latitude) is the model.
prediction1: predict (model, newdata = test)
The phrase "newdata" had 4337 rows, while the variables were only discovered to have 1000 rows.
answered Jun 24 by Sohail
• 2,960 points

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