How to fix error in tidy for building table with flextable-CodePudding

I've created the followinf list models

models_list_1 <- data_long %>%
  group_by(signals) %>%
  do(fit = lmerTest::lmer(value ~ COND*SES   (1 |ID), data = .)) %>% 
  pull(fit) %>% 
  lapply(., function(x) summary(x))

And for example the statistics reported into the first object are the following ones:

> models_list_1[[1]]
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: value ~ COND * SES   (1 | ID)
   Data: .

REML criterion at convergence: 1172.7

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.43364 -0.60624  0.01405  0.54498  2.38710 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 10.87    3.297   
 Residual              8.06    2.839   
Number of obs: 228, groups:  ID, 27

Fixed effects:
                 Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)        4.0610     0.8451  64.5397   4.805 9.60e-06 ***
CONDNEG-NOC       -0.6577     0.7874 192.5862  -0.835   0.4046    
CONDNEU-NOC       -4.0998     0.7874 192.5862  -5.207 4.91e-07 ***
SESR              -0.7276     0.7988 193.0113  -0.911   0.3635    
SESV              -1.5098     0.7988 193.0113  -1.890   0.0602 .  
CONDNEG-NOC:SESR  -0.8070     1.1246 192.5862  -0.718   0.4739    
CONDNEU-NOC:SESR   1.0970     1.1246 192.5862   0.975   0.3306    
CONDNEG-NOC:SESV   1.2112     1.1246 192.5862   1.077   0.2828    
CONDNEU-NOC:SESV   2.3398     1.1246 192.5862   2.081   0.0388 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                 (Intr) CONDNEG-NOC CONDNEU-NOC SESR   SESV   CONDNEG-NOC:SESR
CONDNEG-NOC      -0.466                                                       
CONDNEU-NOC      -0.466  0.500                                                
SESR             -0.462  0.493       0.493                                    
SESV             -0.462  0.493       0.493       0.488                        
CONDNEG-NOC:SESR  0.326 -0.700      -0.350      -0.704 -0.345                 
CONDNEU-NOC:SESR  0.326 -0.350      -0.700      -0.704 -0.345  0.500          
CONDNEG-NOC:SESV  0.326 -0.700      -0.350      -0.345 -0.704  0.490          
CONDNEU-NOC:SESV  0.326 -0.350      -0.700      -0.345 -0.704  0.245          
                 CONDNEU-NOC:SESR CONDNEG-NOC:SESV
CONDNEG-NOC                                       
CONDNEU-NOC                                       
SESR                                              
SESV                                              
CONDNEG-NOC:SESR                                  
CONDNEU-NOC:SESR                                  
CONDNEG-NOC:SESV  0.245                           
CONDNEU-NOC:SESV  0.490            0.500

If I'm interested to report iterativey each **model coefficients (i.e. models_lists[[1]]$coefficients)**contained in each of the model into the list in a table that could be readible into a word/pdf doc created via RMarkdown, which package I shoulòd use? How could I set command lines?

I'm keeping on trying with this code, but I have no succes at all:

models_list_1 %>% 
    map(~.x %>% map( ~broom::tidy(.x) %>% flextable::flextable()))

Since I get this error back

Error: No tidy method for objects of class character

Here the dataset

> dput(head(data_long,300))
structure(list(ID = c("01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "02", "02", "02", "02", "02", "02", "02", "02", "02", "02", 
"02", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04", "04", "04", "04", "04", 
"04", "04", "04", "04", "04", "04", "04"), GR = c("RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP"), SES = c("L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", "L", 
"L", "L", "L", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", 
"R", "R", "R", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V"), COND = c("NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", "NEU-NOC", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", "NEG-CTR", 
"NEG-CTR", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", "NEG-NOC", 
"NEG-NOC", "NEG-NOC", "NEU-NOC"), signals = c("P3(400-450).FCz", 
"P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
"LPPearly(500-700).Cz", "LPPearly(500-700).Pz", "LPP1(500-1000).FCz", 
"LPP1(500-1000).Cz", "LPP1(500-1000).Pz", "LPP2(1000-1500).FCz", 
"LPP2(1000-1500).Cz", "LPP2(1000-1500).Pz", "LPP2(1000-1500).POz", 
"P3(400-450).FCz", "P3(400-450).Cz", "P3(400-450).Pz", "LPPearly(500-700).FCz", 
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1.65917850515029, 5.03749946898385)), row.names = c(NA, -300L
), class = c("tbl_df", "tbl", "data.frame"))
>

CodePudding user response：

There is a function in the gtsummary package that will build and summarize regression models stratified by the signals column. The resulting tables can either be merged to have a wide summary of the results, or stacked to have a long summary table. The example below shows the first few rows of stacked results.

data_long %>%
  tbl_strata(
    strata = signals, 
    ~ lmerTest::lmer(value ~ COND*SES   (1 |ID), data = .x) %>%
      tbl_regression(),
    .combine_with = "tbl_stack"
  ) %>%
  as_flex_table()