[R] Predicting response from fitted linear model with incomplete new sample data

[Rolf Turner]

As far as I can discern, your question makes no sense at all.

Suppose you *know* that y = 2 + 3*x1 + 4*x2.

Now what should you predict when x1 = 6 (with x2 "missing"/unknown)?

See fortune("magic").

On 19/12/13 07:18, Chris Wilkinson wrote:
> I would like to predict a new response from a fitted linear model where the
> new data is a single case with a missing value. My reading of the help on
> predict() is inconclusive on whether this is possible.
>
> Leaving out the missing value or setting it to NA both fail but differently,
> see example code below.
>
>> y <- runif(50)
>> x1 <- rnorm(50)
>> x2 <- rnorm(50)
>> dat <- data.frame(y, x1, x2)
>> mod <- lm(y~.,data=dat)
>> summary(mod)
> Call:
> lm(formula = y ~ ., data = dat)
> Residuals:
>       Min       1Q   Median       3Q      Max
> -0.50467 -0.28997  0.01457  0.27970  0.47791
> Coefficients:
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept)  0.50098    0.04577  10.945  1.6e-14 ***
> x1          -0.01762    0.04172  -0.422    0.675
> x2          -0.02753    0.04920  -0.560    0.578
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Residual standard error: 0.3177 on 47 degrees of freedom
> Multiple R-squared:  0.009301,  Adjusted R-squared:  -0.03286
> F-statistic: 0.2206 on 2 and 47 DF,  p-value: 0.8028
>
>> predict(mod, newdata=data.frame(x1=0.1, x2=0.3))   #OK as expected
>          1
> 0.4909624
>
>> predict(mod, newdata=data.frame(x1=0.1))  # x2 missing
> Error in model.frame.default(Terms, newdata, na.action = na.action, xlev =
> object$xlevels) :
>    variable lengths differ (found for 'x2')
> In addition: Warning message:
> 'newdata' had 1 row but variables found have 50 rows
>> predict(mod, newdata=data.frame(x1=0.1, x2=NA))   #x2=NA
> Error: variable 'x2' was fitted with type "numeric" but type "logical" was
> supplied