# How to Fix: Error in eval(predvars, data, env) : object ‘x’ not found

One error you may encounter in R is:

```Error in eval(predvars, data, env) : object 'x' not found
```

This error occurs when you attempt to use a regression model in R to predict the response values of a new data frame, but the column names in the new data frame do not match the column names of the data frame that you used to fit the model.

This tutorial shares exactly how to fix this error.

### How to Reproduce the Error

Suppose we fit a simple linear regression model in R:

```#create data frame
data <- data.frame(x=c(1, 2, 2, 3, 5, 6, 8, 9),
y=c(7, 8, 8, 6, 9, 8, 12, 14))

#fit linear regression model to data
model <- lm(y ~ x, data=data)

#view summary of model
summary(model)

Call:
lm(formula = y ~ x, data = data)

Residuals:
Min      1Q  Median      3Q     Max
-2.1613 -0.7500  0.5000  0.9355  1.5161

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)   5.5161     0.9830   5.611  0.00137 **
x             0.7742     0.1858   4.167  0.00590 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.463 on 6 degrees of freedom
Multiple R-squared:  0.7432,	Adjusted R-squared:  0.7004
F-statistic: 17.37 on 1 and 6 DF,  p-value: 0.005896
```

Now suppose we attempt to use the predict() function to predict the response values for a new data frame:

```#define new data frame
new_data <- data.frame(x1=c(4, 5, 7, 8, 9))

#attempt to predict y values for new data frame
predict(model, newdata=new_data)

Error in eval(predvars, data, env) : object 'x' not found
```

We receive an error because the data frame that we used when fitting the model had a predictor variable named x, but in the new data frame we named the predictor variable x1.

Since these names don’t match, we receive an error.

### How to Fix the Error

The way to fix this error is to simply make sure that the predictor variable in the new data frame has the same name.

So, we’ll be sure to name the predictor variable x in the new data frame:

```#define new data frame
new_data <- data.frame(x=c(4, 5, 7, 8, 9))
```

Now we can use the predict() function to predict the response values for the new data frame:

```#predict y values for new data frame
predict(model, newdata=new_data)

1         2         3         4         5
8.612903  9.387097 10.935484 11.709677 12.483871
```

We’re able to successfully predict the y values for the new data frame without any errors since the column names matched.