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How to deal with error “Error in eval(predvars, data, env) : numeric 'envir' arg not of length one” in R?
This error occurs when we do not pass the argument for independent variable as a data frame. The predict function will predict the value of the dependent variable for the provided values of the independent variable and we can also use the values of the independent variable using which the model is created.
Example
Consider the below data frame −
set.seed(1) x <-rnorm(20) y <-runif(20,5,10) df <-data.frame(x,y) df
Output
x y 1 -0.62645381 9.104731 2 0.18364332 8.235301 3 -0.83562861 8.914664 4 1.59528080 7.765182 5 0.32950777 7.648598 6 -0.82046838 8.946781 7 0.48742905 5.116656 8 0.73832471 7.386150 9 0.57578135 8.661569 10 -0.30538839 8.463658 11 1.51178117 7.388098 12 0.38984324 9.306047 13 -0.62124058 7.190486 14 -2.21469989 6.223986 15 1.12493092 5.353395 16 -0.04493361 5.497331 17 -0.01619026 6.581359 18 0.94383621 7.593171 19 0.82122120 8.310025 20 0.59390132 7.034151
Creating the linear model −
M <-lm(y~x,data=df)
Formula for prediction that results in error −
predict(M,newdata=df$x,interval="confidence") Error in eval(predvars, data, env) : numeric 'envir' arg not of length one
Formula for prediction that does not result in error −
predict(M,newdata=data.frame(df$x),interval="confidence")
Output
fit lwr upr 1 7.642084 6.814446 8.469722 2 7.536960 6.927195 8.146725 3 7.669228 6.738695 8.599762 4 7.353775 6.214584 8.492966 5 7.518031 6.900897 8.135166 6 7.667261 6.744547 8.589975 7 7.497538 6.854767 8.140310 8 7.464980 6.749018 8.180943 9 7.486073 6.821666 8.150480 10 7.600420 6.902430 8.298410 11 7.364611 6.273305 8.455917 12 7.510202 6.885355 8.135048 13 7.641408 6.816180 8.466635 14 7.848187 6.091378 9.604995 15 7.414811 6.530792 8.298831 16 7.566622 6.935903 8.197340 17 7.562892 6.936919 8.188865 18 7.438312 6.639516 8.237107 19 7.454223 6.706932 8.201514 20 7.483722 6.814287 8.153156
We can simply use the Model object as well, if we want to predict the dependent variable for the independent variable
Example
predict(M)
Output
1 2 3 4 5 6 7 8 7.642084 7.536960 7.669228 7.353775 7.518031 7.667261 7.497538 7.464980 9 10 11 12 13 14 15 16 7.486073 7.600420 7.364611 7.510202 7.641408 7.848187 7.414811 7.566622 17 18 19 20 7.562892 7.438312 7.454223 7.483722
Example
predict(M,interval="confidence")
Output
fit lwr upr 1 7.642084 6.814446 8.469722 2 7.536960 6.927195 8.146725 3 7.669228 6.738695 8.599762 4 7.353775 6.214584 8.492966 5 7.518031 6.900897 8.135166 6 7.667261 6.744547 8.589975 7 7.497538 6.854767 8.140310 8 7.464980 6.749018 8.180943 9 7.486073 6.821666 8.150480 10 7.600420 6.902430 8.298410 11 7.364611 6.273305 8.455917 12 7.510202 6.885355 8.135048 13 7.641408 6.816180 8.466635 14 7.848187 6.091378 9.604995 15 7.414811 6.530792 8.298831 16 7.566622 6.935903 8.197340 17 7.562892 6.936919 8.188865 18 7.438312 6.639516 8.237107 19 7.454223 6.706932 8.201514 20 7.483722 6.814287 8.153156
Updated on 21-Aug-2020 11:35:52
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