How to make predictions even with NAs using predict()?
I want to use predict() with a polr() model to predict variable z, as per the following code. This first is the df to train the model and the subsequent test data.
df <- data.frame(x=c(1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2),
y=c(32, 67, 12, 89, 45, 78, 43, 47, 14, 67, 16, 36, 25, 23, 56, 26, 35, 79, 13, 44),
z=as.factor(c(1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 3, 2, 1, 2, 1, 2, 1, 2)))
test <- data.frame(x=c(1, 2, 1, 1, 2, 1, 2, 2, 1, 1),
y=c(34, NA, 78, NA, 89, 17, 27, 83, 23, 48),
z=c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1))
This is the polr() model:
mod <- polr(z ~ x + y, data = df, Hess = TRUE)
And this is the predict() function with its outcome:
predict(mod, newdata = test)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
My problem is that I want the model to make predictions even when there are NAs, as in the 2nd and 4th cases. I have tried the following, with the same result:
predict(mod, newdata = test, na.action = "na.exclude")
predict(mod, newdata = test, na.action = "na.pass")
predict(mod, newdata = test, na.action = "na.omit")
predict(mod, newdata = test, na.rm=T)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
How can I get the model to make predictions even when there's some missing data?
linear-regression na predict
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
add a comment |
I want to use predict() with a polr() model to predict variable z, as per the following code. This first is the df to train the model and the subsequent test data.
df <- data.frame(x=c(1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2),
y=c(32, 67, 12, 89, 45, 78, 43, 47, 14, 67, 16, 36, 25, 23, 56, 26, 35, 79, 13, 44),
z=as.factor(c(1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 3, 2, 1, 2, 1, 2, 1, 2)))
test <- data.frame(x=c(1, 2, 1, 1, 2, 1, 2, 2, 1, 1),
y=c(34, NA, 78, NA, 89, 17, 27, 83, 23, 48),
z=c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1))
This is the polr() model:
mod <- polr(z ~ x + y, data = df, Hess = TRUE)
And this is the predict() function with its outcome:
predict(mod, newdata = test)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
My problem is that I want the model to make predictions even when there are NAs, as in the 2nd and 4th cases. I have tried the following, with the same result:
predict(mod, newdata = test, na.action = "na.exclude")
predict(mod, newdata = test, na.action = "na.pass")
predict(mod, newdata = test, na.action = "na.omit")
predict(mod, newdata = test, na.rm=T)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
How can I get the model to make predictions even when there's some missing data?
linear-regression na predict
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
When I viewed a 3D scatterplot of the data from various angles, the data does not appear to be on a smooth surface.
– James Phillips
Jan 18 at 21:10
@JamesPhillips I just created this data as an example. The underlying problem is that the model can’t make a prediction when there’s an NA.
– Marco Pastor Mayo
Jan 19 at 11:56
add a comment |
I want to use predict() with a polr() model to predict variable z, as per the following code. This first is the df to train the model and the subsequent test data.
df <- data.frame(x=c(1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2),
y=c(32, 67, 12, 89, 45, 78, 43, 47, 14, 67, 16, 36, 25, 23, 56, 26, 35, 79, 13, 44),
z=as.factor(c(1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 3, 2, 1, 2, 1, 2, 1, 2)))
test <- data.frame(x=c(1, 2, 1, 1, 2, 1, 2, 2, 1, 1),
y=c(34, NA, 78, NA, 89, 17, 27, 83, 23, 48),
z=c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1))
This is the polr() model:
mod <- polr(z ~ x + y, data = df, Hess = TRUE)
And this is the predict() function with its outcome:
predict(mod, newdata = test)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
My problem is that I want the model to make predictions even when there are NAs, as in the 2nd and 4th cases. I have tried the following, with the same result:
predict(mod, newdata = test, na.action = "na.exclude")
predict(mod, newdata = test, na.action = "na.pass")
predict(mod, newdata = test, na.action = "na.omit")
predict(mod, newdata = test, na.rm=T)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
How can I get the model to make predictions even when there's some missing data?
linear-regression na predict
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
I want to use predict() with a polr() model to predict variable z, as per the following code. This first is the df to train the model and the subsequent test data.
df <- data.frame(x=c(1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2),
y=c(32, 67, 12, 89, 45, 78, 43, 47, 14, 67, 16, 36, 25, 23, 56, 26, 35, 79, 13, 44),
z=as.factor(c(1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 3, 2, 1, 2, 1, 2, 1, 2)))
test <- data.frame(x=c(1, 2, 1, 1, 2, 1, 2, 2, 1, 1),
y=c(34, NA, 78, NA, 89, 17, 27, 83, 23, 48),
z=c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1))
This is the polr() model:
mod <- polr(z ~ x + y, data = df, Hess = TRUE)
And this is the predict() function with its outcome:
predict(mod, newdata = test)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
My problem is that I want the model to make predictions even when there are NAs, as in the 2nd and 4th cases. I have tried the following, with the same result:
predict(mod, newdata = test, na.action = "na.exclude")
predict(mod, newdata = test, na.action = "na.pass")
predict(mod, newdata = test, na.action = "na.omit")
predict(mod, newdata = test, na.rm=T)
[1] 2 <NA> 2 <NA> 2 2 2 2 2 2
How can I get the model to make predictions even when there's some missing data?
linear-regression na predict
linear-regression na predict
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
asked Jan 18 at 17:38
Marco Pastor MayoMarco Pastor Mayo
766
766
When I viewed a 3D scatterplot of the data from various angles, the data does not appear to be on a smooth surface.
– James Phillips
Jan 18 at 21:10
@JamesPhillips I just created this data as an example. The underlying problem is that the model can’t make a prediction when there’s an NA.
– Marco Pastor Mayo
Jan 19 at 11:56
add a comment |
When I viewed a 3D scatterplot of the data from various angles, the data does not appear to be on a smooth surface.
– James Phillips
Jan 18 at 21:10
@JamesPhillips I just created this data as an example. The underlying problem is that the model can’t make a prediction when there’s an NA.
– Marco Pastor Mayo
Jan 19 at 11:56
When I viewed a 3D scatterplot of the data from various angles, the data does not appear to be on a smooth surface.
– James Phillips
Jan 18 at 21:10
When I viewed a 3D scatterplot of the data from various angles, the data does not appear to be on a smooth surface.
– James Phillips
Jan 18 at 21:10
@JamesPhillips I just created this data as an example. The underlying problem is that the model can’t make a prediction when there’s an NA.
– Marco Pastor Mayo
Jan 19 at 11:56
@JamesPhillips I just created this data as an example. The underlying problem is that the model can’t make a prediction when there’s an NA.
– Marco Pastor Mayo
Jan 19 at 11:56
add a comment |
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When I viewed a 3D scatterplot of the data from various angles, the data does not appear to be on a smooth surface.
– James Phillips
Jan 18 at 21:10
@JamesPhillips I just created this data as an example. The underlying problem is that the model can’t make a prediction when there’s an NA.
– Marco Pastor Mayo
Jan 19 at 11:56