從循環函數內的 lmerTest 模型中提取 p 值

Question

我有許多線性混合模型，我已經安裝了lmerTest庫，因此函數的summary()會為我提供固定效果的 p 值。

我寫了一個循環函數，提取性別的固定效應：時間和性別：時間：感興趣的解釋變量。

現在還嘗試提取性別的 p 值：時間固定效應（第 1 步）以及性別：時間：解釋變量（第 2 步）。

通常我可以用這段代碼提取 p 值：

coef(summary(model))[,5]["genderfemale:time"]

但是在循環函數內部它不起作用並給出錯誤：“coef(summary(model))[, 5] 中的錯誤：下標越界”

見代碼

library(lmerTest)

# Create a list of models with interaction terms to loop over
models <- list(
  mixed_age_interaction,
  mixed_tnfi_year_interaction,
  mixed_crp_interaction
)

# Create a list of explanatory variables to loop over
explanatoryVariables <- list(
  "age_at_diagnosis",
  "bio_drug_start_year",
  "crp"
)

loop_function <- function(models, explanatoryVariables) {
  # Create an empty data frame to store the results
  coef_df <- data.frame(adj_coef_gender_sex = numeric(), coef_interaction_term = numeric(), explanatory_variable = character(), adj_coef_pvalue = numeric())
  
  # Loop over the models and explanatory variables
  for (i in seq_along(models)) {
    model <- models[[i]]
    explanatoryVariable <- explanatoryVariables[[i]]
    
    # Extract the adjusted coefficients for the gender*time interaction
    adj_coef <- fixef(model)["genderfemale:time"]
    
    # Extract the fixed effect of the interaction term
    interaction_coef <- fixef(model)[paste0("genderfemale:time:", explanatoryVariable)]
    
    # Extract the p-value for the adjusted coefficient for gender*time
    adj_coef_pvalue <- coef(summary(model))[,5]["genderfemale:time"]
    
    # Add a row to the data frame with the results for this model
    coef_df <- bind_rows(coef_df, data.frame(adj_coef_gender_sex = adj_coef, coef_interaction_term = interaction_coef, explanatory_variable = explanatoryVariable, adj_coef_pvalue = adj_coef_pvalue))
  }
  return(coef_df)
}

# Loop over the models and extract the fixed effects
coef_df <- loop_function(models, explanatoryVariables)
coef_df

我的問題是如何從 gender:time 和 gender:time:explanatory 變量的模型中提取 p 值並將它們添加到最終的 data.frame coef_df 中？

還添加了其中一個模型的摘要以供參考

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: basdai ~ 1 + gender + time + age_at_diagnosis + gender * time +  
    time * age_at_diagnosis + gender * age_at_diagnosis + gender *  
    time * age_at_diagnosis + (1 | ID) + (1 | country)
   Data: dat

      AIC       BIC    logLik  deviance  df.resid 
 254340.9  254431.8 -127159.5  254318.9     28557 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.3170 -0.6463 -0.0233  0.6092  4.3180 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 154.62   12.434  
 country  (Intercept)  32.44    5.695  
 Residual             316.74   17.797  
Number of obs: 28568, groups:  ID, 11207; country, 13

Fixed effects:
                                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                         4.669e+01  1.792e+00  2.082e+01  26.048  < 2e-16 ***
genderfemale                        2.368e+00  1.308e+00  1.999e+04   1.810   0.0703 .  
time                               -1.451e+01  4.220e-01  2.164e+04 -34.382  < 2e-16 ***
age_at_diagnosis                    9.907e-02  2.220e-02  1.963e+04   4.463 8.12e-06 ***
genderfemale:time                   1.431e-01  7.391e-01  2.262e+04   0.194   0.8464    
time:age_at_diagnosis               8.188e-02  1.172e-02  2.185e+04   6.986 2.90e-12 ***
genderfemale:age_at_diagnosis       8.547e-02  3.453e-02  2.006e+04   2.476   0.0133 *  
genderfemale:time:age_at_diagnosis  4.852e-03  1.967e-02  2.274e+04   0.247   0.8052    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) gndrfm time   ag_t_d gndrf: tm:g__ gnd:__
genderfemal -0.280                                          
time        -0.241  0.331                                   
age_t_dgnss -0.434  0.587  0.511                            
gendrfml:tm  0.139 -0.519 -0.570 -0.293                     
tm:g_t_dgns  0.228 -0.313 -0.951 -0.533  0.543              
gndrfml:g__  0.276 -0.953 -0.329 -0.639  0.495  0.343       
gndrfml::__ -0.137  0.491  0.567  0.319 -0.954 -0.596 -0.516

Answer 1

{ lmerTest } 的內部函數get_coefmat可能很方便：

如果fm是一個示例 {lmer} 模型...

library("lmerTest")

fm <- lmer(Informed.liking ~ Gender + Information * Product +
             (1 | Consumer) + (1 | Consumer:Product),
           data=ham
           )

...您可以獲得包括 p 值在內的系數作為數據框，如下所示（請注意三個冒號以公開內部函數）：

df_coeff <- lmerTest:::get_coefmat(fm) |>
  as.data.frame()

輸出：

## > df_coeff
##                         Estimate Std. Error       df    t value     Pr(>|t|)
## (Intercept)            5.8490289  0.2842897 322.3364 20.5741844 1.173089e-60
## Gender2               -0.2442835  0.2605644  79.0000 -0.9375169 3.513501e-01
## Information2           0.1604938  0.2029095 320.0000  0.7909626 4.295517e-01
## Product2              -0.8271605  0.3453291 339.5123 -2.3952818 1.714885e-02
## Product3               0.1481481  0.3453291 339.5123  0.4290057 6.681912e-01
## ...

編輯

這是一個片段，它將返回您提取的系數，例如，模型m1和m2作為組合數據框：

library(dplyr)
library(tidyr)
library(purrr)
library(tibble)

list('m1', 'm2') |> ## observe the quotes
  map_dfr( ~ list(
            model = .x,
            coeff = lmerTest:::get_coefmat(get(.x)) |>
              as.data.frame() |>
              rownames_to_column()
          )
          ) |> 
  as_tibble() |>
  unnest_wider(coeff)

輸出：

## + # A tibble: 18 x 7
##    model rowname               Estimate `Std. Error`    df `t value` `Pr(>|t|)`
##    <chr> <chr>                    <dbl>        <dbl> <dbl>     <dbl>      <dbl>
##  1 m1    (Intercept)              5.85         0.284 322.     20.6     1.17e-60
##  2 m1    Gender2                 -0.244        0.261  79.0    -0.938   3.51e- 1
##  ...
##  4 m1    Product2                -0.827        0.345 340.     -2.40    1.71e- 2
##  ...
##  8 m1    Information2:Product3    0.272        0.287 320.      0.946   3.45e- 1
##  ...
## 10 m2    (Intercept)              5.85         0.284 322.     20.6     1.17e-60
## 11 m2    Gender2                 -0.244        0.261  79.0    -0.938   3.51e- 1
## ...