predict()가 내 lmer 모델에 대해 'newdata'작업을 허용하지 않는 이유는 무엇입니까?

Question

나는 newData에 제공함으로써) (예측, 또는 predictInterval() 또는 bootMer()를 사용하려고 할 때, 나는 다음과 같은 오류 얻을 (그것은 내가 가진 모델을 구축 동일한 데이터의 경우에도) :

Error in [.data.frame(fr, vars) : undefined columns selected 

은

tidyverse 패키지를 사용하여 tibble과 관련이 있다고 생각했지만 data.frame (as.data.frame 사용)으로 변환해도 문제가 해결되지 않습니다.

또한 공백을 포함하지 않고 데이터에서 NA를 없애기 위해 열 이름을 변경해 보았습니다.

library(tidyverse) 
library(lme4) 
library(lattice) 
library(merTools) 

df = read_csv(file = "~/Documents/Experiments/Myles Study/mylesdata.csv") 

names(df)[names(df) == "h3"] = "hr3" 

spms = df %>% 
    dplyr::select(
    -c(
     mets1, mets2, mets3, mets4, mets5, mets6 
     , rpe1, rpe2, rpe3, rpe4, rpe5, rpe6 
     , hr1, hr2, hr3, hr4, hr5, hr6 
     , sl1, sl2, sl3, sl4, sl5, sl6 
    ) 
) %>% 
    gather(speed_lev, spm, spm1, spm2, spm3, spm4, spm5, spm6) 
spms$speed_lev = factor(spms$speed_lev) 
levels(spms$speed_lev) = c("1.5", "2", "2.5", "3.5", "4.0", "4.5") 


mets = df %>% 
    dplyr::select(
    -c(
     spm1, spm2, spm3, spm4, spm5, spm6 
     , rpe1, rpe2, rpe3, rpe4, rpe5, rpe6 
     , hr1, hr2, hr3, hr4, hr5, hr6 
     , sl1, sl2, sl3, sl4, sl5, sl6 
    ) 
) %>% 
    gather(speed_lev, mets, mets1, mets2, mets3, mets4, mets5, mets6) 
mets$speed_lev = factor(mets$speed_lev) 
levels(mets$speed_lev) = c("1.5", "2", "2.5", "3.5", "4.0", "4.5") 


rpe = df %>% 
    dplyr::select(
    -c(
     spm1, spm2, spm3, spm4, spm5, spm6 
     , mets1, mets2, mets3, mets4, mets5, mets6 
     , hr1, hr2, hr3, hr4, hr5, hr6 
     , sl1, sl2, sl3, sl4, sl5, sl6 
    ) 
) %>% 
    gather(speed_lev, rpe, rpe1, rpe2, rpe3, rpe4, rpe5, rpe6) 
rpe$speed_lev = factor(rpe$speed_lev) 
levels(rpe$speed_lev) = c("1.5", "2", "2.5", "3.5", "4.0", "4.5") 


hr = df %>% 
    dplyr::select(
    -c(
     spm1, spm2, spm3, spm4, spm5, spm6 
     , mets1, mets2, mets3, mets4, mets5, mets6 
     , rpe1, rpe2, rpe3, rpe4, rpe5, rpe6 
     , sl1, sl2, sl3, sl4, sl5, sl6 
    ) 
) %>% 
    gather(speed_lev, hr, hr1, hr2, hr3, hr4, hr5, hr6) 
hr$speed_lev = factor(hr$speed_lev) 
levels(hr$speed_lev) = c("1.5", "2", "2.5", "3.5", "4.0", "4.5") 


sl = df %>% 
    dplyr::select(
    -c(
     spm1, spm2, spm3, spm4, spm5, spm6 
     , mets1, mets2, mets3, mets4, mets5, mets6 
     , rpe1, rpe2, rpe3, rpe4, rpe5, rpe6 
     , hr1, hr2, hr3, hr4, hr5, hr6 
    ) 
) %>% 
    gather(speed_lev, sl, sl1, sl2, sl3, sl4, sl5, sl6) 
sl$speed_lev = factor(sl$speed_lev) 
levels(sl$speed_lev) = c("1.5", "2", "2.5", "3.5", "4.0", "4.5") 


dat = left_join(spms, mets) 
dat = left_join(dat, rpe) 
dat = left_join(dat, hr) 
dat = left_join(dat, sl) 
names(dat)[names(dat) == "speed_lev"] = "speed (m/h)" 
dat$`speed (m/h)` = as.numeric(as.character(dat$`speed (m/h)`)) 

dat$mets_sq = dat$mets^2 
dat$mets_cubed = dat$mets^3 

m3 = lmer(spm ~ `leg length (cm)` + mets_cubed + mets_sq + mets + (1 | subject), data = dat) 
summary(m3) 

요약은 다음과 같습니다 :

predict(m3, dat) 

predictInterval(
    m3 
    , dat 
    , level = 0.95 
    , n.sims = 1000 
    , stat = "median" 
    , type="linear.prediction" 
    , include.resid.var = TRUE 
) 

을 그리고 오류를 얻을 :

Error in `[.data.frame`(fr, vars) : undefined columns selected 

Error in `[.data.frame`(fr, vars) : undefined columns selected 
In addition: Warning message: 
In predictInterval(m3, dat, level = 0.95, n.sims = 1000, stat = "median", : 
    newdata is tbl_df or tbl object from dplyr package and has been 
       coerced to a data.frame 

을

Linear mixed model fit by REML ['lmerMod'] 
Formula: spm ~ `leg length (cm)` + mets_cubed + mets_sq + mets + (1 |  subject) 
    Data: dat 

REML criterion at convergence: 1562.6 

Scaled residuals: 
    Min  1Q Median  3Q  Max 
-2.8094 -0.5213 0.1120 0.5638 3.0933 

Random effects: 
Groups Name  Variance Std.Dev. 
subject (Intercept) 49.41 7.029 
Residual    26.90 5.186 
Number of obs: 238, groups: subject, 43 

Fixed effects: 
        Estimate Std. Error t value 
(Intercept)  41.30017 18.13968 2.277 
`leg length (cm)` -0.58471 0.16535 -3.536 
mets_cubed   0.44777 0.09343 4.793 
mets_sq   -8.67702 1.37655 -6.303 
mets    60.66036 6.36756 9.526 

Correlation of Fixed Effects: 
      (Intr) `l(c)` mts_cb mts_sq 
`lglng(cm)` -0.863      
mets_cubed -0.434 -0.045    
mets_sq  0.449 0.045 -0.994  
mets  -0.459 -0.045 0.974 -0.993 

그럼 내가 예측을하려고 여기에

은 예입니다

세션 정보 :

sessionInfo() 

R version 3.3.2 (2016-10-31) 
Platform: x86_64-apple-darwin13.4.0 (64-bit) 
Running under: macOS Sierra 10.12.3 

locale: 
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8 

attached base packages: 
[1] stats  graphics grDevices utils  datasets methods base  

other attached packages: 
[1] merTools_0.3.0 arm_1.9-3  MASS_7.3-45  lattice_0.20-34 lme4_1.1-12  Matrix_1.2-7.1 dplyr_0.5.0  
[8] purrr_0.2.2  readr_1.0.0  tidyr_0.6.0  tibble_1.2  ggplot2_2.1.0 tidyverse_1.0.0 

loaded via a namespace (and not attached): 
[1] Rcpp_0.12.6  nloptr_1.0.4  plyr_1.8.4  base64enc_0.1-3 tools_3.3.2  digest_0.6.10 
[7] jsonlite_1.1  evaluate_0.10 blme_1.0-4  nlme_3.1-128  gtable_0.2.0  psych_1.6.9  
[13] shiny_1.0.0  DBI_0.5   yaml_2.1.14  parallel_3.3.2 mvtnorm_1.0-5 coda_0.18-1  
[19] stringr_1.1.0 knitr_1.15  htmlwidgets_0.8 grid_3.3.2  DT_0.2   R6_2.1.3   
[25] rmarkdown_1.1 foreign_0.8-67 minqa_1.2.4  reshape2_1.4.1 magrittr_1.5  scales_0.4.0  
[31] htmltools_0.3.5 splines_3.3.2 assertthat_0.1 abind_1.4-5  mnormt_1.5-5  xtable_1.8-2  
[37] mime_0.5   colorspace_1.2-6 httpuv_1.3.3  labeling_0.3  stringi_1.1.1 lazyeval_0.2.0 
[43] munsell_0.4.3 broom_0.4.1 

Answer 1

롤랜드의 의심은 정확했다. tidyverse 패키지 (생태계)는 관심있는 predict() 함수를 마스킹했습니다.