Predicting data via regression model and storing in a vector - r

Question

Apologies for what is probably a very basic question.
I have created a linear model for a massive meteorological dataset using multiple regression. My goal is to use that model to "predict" data during a certain period using predictors 1, 2 and 3. I will then compare those predicted data to the observed data for that period.
My approach thus far has been to create a new vector for the predicted values and loop through the vector, creating predicted values based on the extracted coefficients of the linear model. Then, I will simply subtract the predicted values from the observed values. For some reason, this approach results in the new predicted vector being NULL. Any idea how I could approach this?
A sample is below. "data" refers to the dataset containing the predictors.
coef <- coefficients(multipleRegressionModel)
predictedValues=c()
for(i in 1:length(data$timePeriod)){
predictedValues[i] = append(predictedValues, data$coef[1]+data$predictor1[i]*data$coef[2]+data$predictor2[i]*data$coef[3]+
data$predictor3[i]*data$coef[4])
}
diff=c()
diff=observedValues - predictedValues

Answer 1

It looks like you are making this more difficult than it needs to be. R has a predict() function that does all of this for you. If you had a sample data.frame like so:
set.seed(26)
mydf = data.frame (a=1:20 , b = rnorm(20),
c = 1:20 + runif(20,2,3)*runif(20, 2, 3),
d = 1:20 + rpois(20,5)*runif(1:20)*sin(1:20))
And you wanted to train on some rows, and test on the others
trainRows<-sample(1:20, 16)
mydf.train<-mydf[trainRows,]
mydf.test<-mydf[-trainRows,]
Then fit the model and predict
model<-lm(a~b+c+d, data = mydf.train)
summary(model) #gives info about your model.
mydf.test$pred<-predict(model1, newdata = mydf.test)
MSE<-mean((mydf.test$pred-mydf.test$a)^2) #calculate mean squared error
MSE
#[1] 0.06321
View the predictions with mydf.test$pred

Answer 2

Here is a simple example using a glm on the mtcars data.
Line<- #setting up the linear model function
function (train_dat, test_dat, variables, y_var, family = "gaussian")
{
fm <- as.formula(paste(y_var, " ~", paste(variables, collapse = "+"))) #formula
glm1 <- glm(fm, data = train_dat, family = family) #run the model
pred <- predict(glm1, newdata = test_dat) #predict the model
}
data(mtcars)
y_var<-'mpg'
x_vars<-setdiff(names(mtcars),y_var)
mtcars[,'linear_prediction']<-Line(mtcars,mtcars,x_vars,y_var)
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb linear_prediction
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 22.59951
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 22.11189
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 26.25064
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 21.23740
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 17.69343
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 20.38304