model testing using AIC

[amare]

Hi dear, I may get some feedback on the following?

I was doing simple regression and planing to do Akaike information criterion ( AIC) or the Bayesian information criterion (BIC).
However, I encountered error message " Error in UseMethod("logLik") : no applicable method for 'logLik' applied to an object of class "c('tbl_df', 'tbl', 'data.frame')"

My data set and code are here:
data <- data.frame(s =c("lc", "tr", "fr", "ku","lc", "tr", "fr", "ku","lc", "lc", "fr"),
B = c("m","m","m","m","m", "m", "f","f","f","f","f"),
G = c("s","s","s","u","u", "u", "k","k","k","r","r"),
ZN =c(78,82,34,67,98,56,37,45,27,18,34),
lm =c(45,22,24,12,10,17,35,63,18,20,30),
GFR=c(120,100,90,60,100,110,100,90,95,87,96),
g1 = c(35, 2, 3, 4, 5, 6, 7, 10, 12, 41, 76),
g2 = c(20, 2, 7, 2, 8, 5, 5, 3, 7, 2, 12),
g3 = c(5, 0, 4, 5, 2, 4, 8, 9, 20, 1, 11),
g4 = c(1,3,4,5,7,3,1,5,7,3,10),
g5 = c(20,23, 27, 35, 12, 10, 17, 24, 21, 15, 16),
g6 = c(13,13,115,17,14,12,19,6,7,8,4),
g7 = c(5, 0, 4, 5, 2, 4, 8, 9, 20, 1, 11),
g8 = c(1,3,4,5,7,3,1,5,7,3,10),
g9 = c(20,23, 27, 35, 12, 10, 17, 24, 21, 15, 16),
g10 =c(13,13,115,17,14,12,19,6,7,8,4))

model1 <- data %>%
gather(Fac, value, -s:-GFR) %>%
split(.$Fac) %>%
map(~lm(GFR ~ value + ZN+ lm + B, data =.) %>%
tidy(.) %>%
select(estimate, std.error,statistic,p.value) %>%
slice(2)) %>% # Extracting regression Intercept
bind_rows(.id = "Variables")

BIC(model1)
AIC(model1)

Anyhelp is so important.

Thank you so much for your support

[toryn_stat]

The AIC and BIC functions need lm objects. However, in the above map function, you are not saving the lm object, but instead are extracting certain components. You need to break these two steps.

data <- data.frame(s =c("lc", "tr", "fr", "ku","lc", "tr", "fr", "ku","lc", "lc", "fr"),
                   B = c("m","m","m","m","m", "m", "f","f","f","f","f"),
                   G = c("s","s","s","u","u", "u", "k","k","k","r","r"),
                   ZN =c(78,82,34,67,98,56,37,45,27,18,34),
                   lm =c(45,22,24,12,10,17,35,63,18,20,30),
                   GFR=c(120,100,90,60,100,110,100,90,95,87,96),
                   g1 = c(35, 2, 3, 4, 5, 6, 7, 10, 12, 41, 76),
                   g2 = c(20, 2, 7, 2, 8, 5, 5, 3, 7, 2, 12),
                   g3 = c(5, 0, 4, 5, 2, 4, 8, 9, 20, 1, 11),
                   g4 = c(1,3,4,5,7,3,1,5,7,3,10),
                   g5 = c(20,23, 27, 35, 12, 10, 17, 24, 21, 15, 16),
                   g6 = c(13,13,115,17,14,12,19,6,7,8,4),
                   g7 = c(5, 0, 4, 5, 2, 4, 8, 9, 20, 1, 11),
                   g8 = c(1,3,4,5,7,3,1,5,7,3,10),
                   g9 = c(20,23, 27, 35, 12, 10, 17, 24, 21, 15, 16),
                   g10 =c(13,13,115,17,14,12,19,6,7,8,4))

model1 <- data %>%
  gather(Fac, value, -s:-GFR) %>%
  split(.$Fac) %>%
  map(~lm(GFR ~ value + ZN+ lm + B, data =.))

model_information <- model1 %>% 
  map(~cbind.data.frame(AIC = AIC(.x), BIC = BIC(.x))) %>% 
  bind_rows(.id = "Variables")

model_output <- model1 %>% 
  map(~tidy(.x) %>%
        select(estimate, std.error,statistic,p.value) %>%
        slice(2)) %>% 
  bind_rows(.id = "Variables")