CODEWITHSUNDEEP
rbroom
atiretoo
atiretoo

Reputation: 1902

Augment with newdata works with one model fails with another

I'm missing something so fundamental here -- why does augment work with one model but not another?

data(iris)
library(broom)

iris$cSepal.Length <- scale(iris$Sepal.Length, center = TRUE, scale = FALSE)
nd <- expand.grid(Sepal.Length = seq(4, 8, 0.1), Species = factor(levels(iris$Species)))
nd$cSepal.Length <- nd$Sepal.Length - mean(iris$Sepal.Length)

m0 <- lm(Sepal.Width ~ Sepal.Length * Species, data = iris)
pred.0 <- augment(m0, newdata = nd)
m1 <- lm(Sepal.Width ~ cSepal.Length * Species, data = iris)
pred.1 <- augment(m1, newdata = nd)

## Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 123, 150

sessionInfo()

## R version 3.3.1 (2016-06-21)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 10586)
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] broom_0.4.1         RevoUtilsMath_8.0.3
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.6      knitr_1.11       magrittr_1.5     mnormt_1.5-4    
##  [5] lattice_0.20-33  R6_2.1.2         stringr_1.0.0    plyr_1.8.4      
##  [9] dplyr_0.5.0      tools_3.3.1      parallel_3.3.1   grid_3.3.1      
## [13] nlme_3.1-128     psych_1.6.6      DBI_0.4-1        htmltools_0.2.6 
## [17] yaml_2.1.13      assertthat_0.1   digest_0.6.8     tibble_1.1      
## [21] reshape2_1.4.1   formatR_1.4      tidyr_0.5.1      evaluate_0.7.2  
## [25] rmarkdown_0.9.5  stringi_1.1.1    RevoUtils_10.0.1

Upvotes: 2

Views: 353

Answers (1)

MrFlick
MrFlick

Reputation: 206263

The problem here is with scale(). It is returning a matrix and not a vector. Assigning the matrix into the the data.frame is causing problems. You are using scale() to create iris$cSepal.Length but are using x-mean(x) to create nd$cSepal.Length. This creates two different data types which causes problems during the predictions of the two different models when you are using the cSepal.Length column.

It would be best if you used the same centering method each time you create the column. If you just did 

iris$cSepal.Length <- iris$Sepal.Length - mean(iris$Sepal.Length)

everything will work.

Upvotes: 1

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