How to build a summary table of glm's parameters and AICcWt
1
I have a dataset that I am using to build generalised linear models. The response variable is binary (absence/presence) and the explanatory variables are categorical.
CODE
library(tidyverse)
library(AICcmodavg)
# Data
set.seed(123)
t <- tibble(ID = 1:100,
A = as.factor(sample(c(0, 1), 100, T)),
B = as.factor(sample(c("black", "white"), 100, T)),
C = as.factor(sample(c("pos", "neg", "either"), 100, T)))
# Candidate set of models - Binomial family because response variable
# is binary (0 for absent & 1 for present)
# Global model is A ~ B_black + C_either
m1 <- glm(A ~ 1, binomial, t)
m2 <- glm(A ~ B, binomial, t)
m3 <- glm(A ~ C, binomial, t)
m4 <- glm(A ~ B + C, binomial, t)
# List with all models
ms <- list(null = m1, m_B = m2, m_C = m3, m_BC = m4)
# Summary table
aic_tbl <- aictab(ms)
PROBLEM
I want to build a table like the one below that summarises the coefficients, standard errors, and Akaike weights of the models within my candidate set.
QUESTION
Can anyone suggest how to best build this table using my list of models and AIC table?
r data-manipulation glm
asked Dec 31 '18 at 11:17
DariusDarius
104115
add a comment |
1
I have a dataset that I am using to build generalised linear models. The response variable is binary (absence/presence) and the explanatory variables are categorical.
CODE
library(tidyverse)
library(AICcmodavg)
# Data
set.seed(123)
t <- tibble(ID = 1:100,
A = as.factor(sample(c(0, 1), 100, T)),
B = as.factor(sample(c("black", "white"), 100, T)),
C = as.factor(sample(c("pos", "neg", "either"), 100, T)))
# Candidate set of models - Binomial family because response variable
# is binary (0 for absent & 1 for present)
# Global model is A ~ B_black + C_either
m1 <- glm(A ~ 1, binomial, t)
m2 <- glm(A ~ B, binomial, t)
m3 <- glm(A ~ C, binomial, t)
m4 <- glm(A ~ B + C, binomial, t)
# List with all models
ms <- list(null = m1, m_B = m2, m_C = m3, m_BC = m4)
# Summary table
aic_tbl <- aictab(ms)
PROBLEM
I want to build a table like the one below that summarises the coefficients, standard errors, and Akaike weights of the models within my candidate set.
QUESTION
Can anyone suggest how to best build this table using my list of models and AIC table?
r data-manipulation glm
asked Dec 31 '18 at 11:17
DariusDarius
104115
1
try theMuMInpackage?
– Ben Bolker
Dec 31 '18 at 11:41
2
By the way, it is not advisable to use a name such astfor an object as it is a built-in R function and effectively overwrites it. If any function is going to call it later, it will try to use your dataframe first, instead of the correct call to transpose.
– coffeinjunky
Dec 31 '18 at 14:50
1
msis a built-in function inlubridate.Might want to change the name too.
– NelsonGon
Dec 31 '18 at 15:11
add a comment |
1
1
1
I have a dataset that I am using to build generalised linear models. The response variable is binary (absence/presence) and the explanatory variables are categorical.
CODE
library(tidyverse)
library(AICcmodavg)
# Data
set.seed(123)
t <- tibble(ID = 1:100,
A = as.factor(sample(c(0, 1), 100, T)),
B = as.factor(sample(c("black", "white"), 100, T)),
C = as.factor(sample(c("pos", "neg", "either"), 100, T)))
# Candidate set of models - Binomial family because response variable
# is binary (0 for absent & 1 for present)
# Global model is A ~ B_black + C_either
m1 <- glm(A ~ 1, binomial, t)
m2 <- glm(A ~ B, binomial, t)
m3 <- glm(A ~ C, binomial, t)
m4 <- glm(A ~ B + C, binomial, t)
# List with all models
ms <- list(null = m1, m_B = m2, m_C = m3, m_BC = m4)
# Summary table
aic_tbl <- aictab(ms)
PROBLEM
I want to build a table like the one below that summarises the coefficients, standard errors, and Akaike weights of the models within my candidate set.
QUESTION
Can anyone suggest how to best build this table using my list of models and AIC table?
r data-manipulation glm
asked Dec 31 '18 at 11:17
DariusDarius
104115
I have a dataset that I am using to build generalised linear models. The response variable is binary (absence/presence) and the explanatory variables are categorical.
CODE
library(tidyverse)
library(AICcmodavg)
# Data
set.seed(123)
t <- tibble(ID = 1:100,
A = as.factor(sample(c(0, 1), 100, T)),
B = as.factor(sample(c("black", "white"), 100, T)),
C = as.factor(sample(c("pos", "neg", "either"), 100, T)))
# Candidate set of models - Binomial family because response variable
# is binary (0 for absent & 1 for present)
# Global model is A ~ B_black + C_either
m1 <- glm(A ~ 1, binomial, t)
m2 <- glm(A ~ B, binomial, t)
m3 <- glm(A ~ C, binomial, t)
m4 <- glm(A ~ B + C, binomial, t)
# List with all models
ms <- list(null = m1, m_B = m2, m_C = m3, m_BC = m4)
# Summary table
aic_tbl <- aictab(ms)
PROBLEM
I want to build a table like the one below that summarises the coefficients, standard errors, and Akaike weights of the models within my candidate set.
QUESTION
Can anyone suggest how to best build this table using my list of models and AIC table?
r data-manipulation glm
r data-manipulation glm
asked Dec 31 '18 at 11:17
DariusDarius
104115
asked Dec 31 '18 at 11:17
DariusDarius
104115
edited Jan 1 at 7:00
Darius
asked Dec 31 '18 at 11:17
DariusDarius
104115
asked Dec 31 '18 at 11:17
DariusDarius
104115
asked Dec 31 '18 at 11:17
DariusDarius
104115
104115
1
try theMuMInpackage?
– Ben Bolker
Dec 31 '18 at 11:41
2
By the way, it is not advisable to use a name such astfor an object as it is a built-in R function and effectively overwrites it. If any function is going to call it later, it will try to use your dataframe first, instead of the correct call to transpose.
– coffeinjunky
Dec 31 '18 at 14:50
1
msis a built-in function inlubridate.Might want to change the name too.
– NelsonGon
Dec 31 '18 at 15:11
add a comment |
1
try theMuMInpackage?
– Ben Bolker
Dec 31 '18 at 11:41
2
By the way, it is not advisable to use a name such astfor an object as it is a built-in R function and effectively overwrites it. If any function is going to call it later, it will try to use your dataframe first, instead of the correct call to transpose.
– coffeinjunky
Dec 31 '18 at 14:50
1
msis a built-in function inlubridate.Might want to change the name too.
– NelsonGon
Dec 31 '18 at 15:11
1
1
try the
MuMIn package?– Ben Bolker
Dec 31 '18 at 11:41
try the
MuMIn package?– Ben Bolker
Dec 31 '18 at 11:41
2
2
By the way, it is not advisable to use a name such as
t for an object as it is a built-in R function and effectively overwrites it. If any function is going to call it later, it will try to use your dataframe first, instead of the correct call to transpose.– coffeinjunky
Dec 31 '18 at 14:50
By the way, it is not advisable to use a name such as
t for an object as it is a built-in R function and effectively overwrites it. If any function is going to call it later, it will try to use your dataframe first, instead of the correct call to transpose.– coffeinjunky
Dec 31 '18 at 14:50
1
1
ms is a built-in function in lubridate.Might want to change the name too.– NelsonGon
Dec 31 '18 at 15:11
ms is a built-in function in lubridate.Might want to change the name too.– NelsonGon
Dec 31 '18 at 15:11
add a comment |
1 Answer
1
active
oldest
votes
3
Just to point it out: broom gets you half-way to where you want to get by turning the model output into a dataframe, which you can then reshape.
library(broom)
bind_rows(lapply(ms, tidy), .id="key")
key term estimate std.error statistic p.value
1 null (Intercept) -0.12014431182649532 0.200 -0.59963969517107030 0.549
2 m_B (Intercept) 0.00000000000000123 0.283 0.00000000000000433 1.000
3 m_B Bwhite -0.24116205496397874 0.401 -0.60071814968372905 0.548
4 m_C (Intercept) -0.47957308026188367 0.353 -1.35892869678271544 0.174
5 m_C Cneg 0.80499548069651150 0.507 1.58784953814722285 0.112
6 m_C Cpos 0.30772282333522433 0.490 0.62856402205887851 0.530
7 m_BC (Intercept) -0.36339654526926718 0.399 -0.90984856337213305 0.363
8 m_BC Bwhite -0.25083209866475475 0.408 -0.61515191157571303 0.538
9 m_BC Cneg 0.81144822536950656 0.508 1.59682131202527056 0.110
10 m_BC Cpos 0.32706970242195277 0.492 0.66527127770403538 0.506
And if you must insist of the layout of your table, I came up with the following (arguably clumsy) way of rearranging everything:
out <- bind_rows(lapply(ms, tidy), .id="mod")
t1 <- out %>% select(mod, term, estimate) %>% spread(term, estimate) %>% base::t
t2 <- out %>% select(mod, term, std.error) %>% spread(term, std.error) %>% base::t
rownames(t2) <- paste0(rownames(t2), "_std_e")
tmp <- rbind(t1, t2[-1,])
new_t <- as.data.frame(tmp[-1,])
colnames(new_t) <- tmp[1,]
new_t
Alternatively, you may want to familiarise yourself with packages that are meant to display model output for publication, e.g. texreg or stargazer come to mind:
library(texreg)
screenreg(ms)
==================================================
null m_B m_C m_BC
--------------------------------------------------
(Intercept) -0.12 0.00 -0.48 -0.36
(0.20) (0.28) (0.35) (0.40)
Bwhite -0.24 -0.25
(0.40) (0.41)
Cneg 0.80 0.81
(0.51) (0.51)
Cpos 0.31 0.33
(0.49) (0.49)
--------------------------------------------------
AIC 140.27 141.91 141.66 143.28
BIC 142.87 147.12 149.48 153.70
Log Likelihood -69.13 -68.95 -67.83 -67.64
Deviance 138.27 137.91 135.66 135.28
Num. obs. 100 100 100 100
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
add a comment |
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1 Answer
1
active
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
3
Just to point it out: broom gets you half-way to where you want to get by turning the model output into a dataframe, which you can then reshape.
library(broom)
bind_rows(lapply(ms, tidy), .id="key")
key term estimate std.error statistic p.value
1 null (Intercept) -0.12014431182649532 0.200 -0.59963969517107030 0.549
2 m_B (Intercept) 0.00000000000000123 0.283 0.00000000000000433 1.000
3 m_B Bwhite -0.24116205496397874 0.401 -0.60071814968372905 0.548
4 m_C (Intercept) -0.47957308026188367 0.353 -1.35892869678271544 0.174
5 m_C Cneg 0.80499548069651150 0.507 1.58784953814722285 0.112
6 m_C Cpos 0.30772282333522433 0.490 0.62856402205887851 0.530
7 m_BC (Intercept) -0.36339654526926718 0.399 -0.90984856337213305 0.363
8 m_BC Bwhite -0.25083209866475475 0.408 -0.61515191157571303 0.538
9 m_BC Cneg 0.81144822536950656 0.508 1.59682131202527056 0.110
10 m_BC Cpos 0.32706970242195277 0.492 0.66527127770403538 0.506
And if you must insist of the layout of your table, I came up with the following (arguably clumsy) way of rearranging everything:
out <- bind_rows(lapply(ms, tidy), .id="mod")
t1 <- out %>% select(mod, term, estimate) %>% spread(term, estimate) %>% base::t
t2 <- out %>% select(mod, term, std.error) %>% spread(term, std.error) %>% base::t
rownames(t2) <- paste0(rownames(t2), "_std_e")
tmp <- rbind(t1, t2[-1,])
new_t <- as.data.frame(tmp[-1,])
colnames(new_t) <- tmp[1,]
new_t
Alternatively, you may want to familiarise yourself with packages that are meant to display model output for publication, e.g. texreg or stargazer come to mind:
library(texreg)
screenreg(ms)
==================================================
null m_B m_C m_BC
--------------------------------------------------
(Intercept) -0.12 0.00 -0.48 -0.36
(0.20) (0.28) (0.35) (0.40)
Bwhite -0.24 -0.25
(0.40) (0.41)
Cneg 0.80 0.81
(0.51) (0.51)
Cpos 0.31 0.33
(0.49) (0.49)
--------------------------------------------------
AIC 140.27 141.91 141.66 143.28
BIC 142.87 147.12 149.48 153.70
Log Likelihood -69.13 -68.95 -67.83 -67.64
Deviance 138.27 137.91 135.66 135.28
Num. obs. 100 100 100 100
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
add a comment |
3
Just to point it out: broom gets you half-way to where you want to get by turning the model output into a dataframe, which you can then reshape.
library(broom)
bind_rows(lapply(ms, tidy), .id="key")
key term estimate std.error statistic p.value
1 null (Intercept) -0.12014431182649532 0.200 -0.59963969517107030 0.549
2 m_B (Intercept) 0.00000000000000123 0.283 0.00000000000000433 1.000
3 m_B Bwhite -0.24116205496397874 0.401 -0.60071814968372905 0.548
4 m_C (Intercept) -0.47957308026188367 0.353 -1.35892869678271544 0.174
5 m_C Cneg 0.80499548069651150 0.507 1.58784953814722285 0.112
6 m_C Cpos 0.30772282333522433 0.490 0.62856402205887851 0.530
7 m_BC (Intercept) -0.36339654526926718 0.399 -0.90984856337213305 0.363
8 m_BC Bwhite -0.25083209866475475 0.408 -0.61515191157571303 0.538
9 m_BC Cneg 0.81144822536950656 0.508 1.59682131202527056 0.110
10 m_BC Cpos 0.32706970242195277 0.492 0.66527127770403538 0.506
And if you must insist of the layout of your table, I came up with the following (arguably clumsy) way of rearranging everything:
out <- bind_rows(lapply(ms, tidy), .id="mod")
t1 <- out %>% select(mod, term, estimate) %>% spread(term, estimate) %>% base::t
t2 <- out %>% select(mod, term, std.error) %>% spread(term, std.error) %>% base::t
rownames(t2) <- paste0(rownames(t2), "_std_e")
tmp <- rbind(t1, t2[-1,])
new_t <- as.data.frame(tmp[-1,])
colnames(new_t) <- tmp[1,]
new_t
Alternatively, you may want to familiarise yourself with packages that are meant to display model output for publication, e.g. texreg or stargazer come to mind:
library(texreg)
screenreg(ms)
==================================================
null m_B m_C m_BC
--------------------------------------------------
(Intercept) -0.12 0.00 -0.48 -0.36
(0.20) (0.28) (0.35) (0.40)
Bwhite -0.24 -0.25
(0.40) (0.41)
Cneg 0.80 0.81
(0.51) (0.51)
Cpos 0.31 0.33
(0.49) (0.49)
--------------------------------------------------
AIC 140.27 141.91 141.66 143.28
BIC 142.87 147.12 149.48 153.70
Log Likelihood -69.13 -68.95 -67.83 -67.64
Deviance 138.27 137.91 135.66 135.28
Num. obs. 100 100 100 100
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
add a comment |
3
3
3
Just to point it out: broom gets you half-way to where you want to get by turning the model output into a dataframe, which you can then reshape.
library(broom)
bind_rows(lapply(ms, tidy), .id="key")
key term estimate std.error statistic p.value
1 null (Intercept) -0.12014431182649532 0.200 -0.59963969517107030 0.549
2 m_B (Intercept) 0.00000000000000123 0.283 0.00000000000000433 1.000
3 m_B Bwhite -0.24116205496397874 0.401 -0.60071814968372905 0.548
4 m_C (Intercept) -0.47957308026188367 0.353 -1.35892869678271544 0.174
5 m_C Cneg 0.80499548069651150 0.507 1.58784953814722285 0.112
6 m_C Cpos 0.30772282333522433 0.490 0.62856402205887851 0.530
7 m_BC (Intercept) -0.36339654526926718 0.399 -0.90984856337213305 0.363
8 m_BC Bwhite -0.25083209866475475 0.408 -0.61515191157571303 0.538
9 m_BC Cneg 0.81144822536950656 0.508 1.59682131202527056 0.110
10 m_BC Cpos 0.32706970242195277 0.492 0.66527127770403538 0.506
And if you must insist of the layout of your table, I came up with the following (arguably clumsy) way of rearranging everything:
out <- bind_rows(lapply(ms, tidy), .id="mod")
t1 <- out %>% select(mod, term, estimate) %>% spread(term, estimate) %>% base::t
t2 <- out %>% select(mod, term, std.error) %>% spread(term, std.error) %>% base::t
rownames(t2) <- paste0(rownames(t2), "_std_e")
tmp <- rbind(t1, t2[-1,])
new_t <- as.data.frame(tmp[-1,])
colnames(new_t) <- tmp[1,]
new_t
Alternatively, you may want to familiarise yourself with packages that are meant to display model output for publication, e.g. texreg or stargazer come to mind:
library(texreg)
screenreg(ms)
==================================================
null m_B m_C m_BC
--------------------------------------------------
(Intercept) -0.12 0.00 -0.48 -0.36
(0.20) (0.28) (0.35) (0.40)
Bwhite -0.24 -0.25
(0.40) (0.41)
Cneg 0.80 0.81
(0.51) (0.51)
Cpos 0.31 0.33
(0.49) (0.49)
--------------------------------------------------
AIC 140.27 141.91 141.66 143.28
BIC 142.87 147.12 149.48 153.70
Log Likelihood -69.13 -68.95 -67.83 -67.64
Deviance 138.27 137.91 135.66 135.28
Num. obs. 100 100 100 100
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
Just to point it out: broom gets you half-way to where you want to get by turning the model output into a dataframe, which you can then reshape.
library(broom)
bind_rows(lapply(ms, tidy), .id="key")
key term estimate std.error statistic p.value
1 null (Intercept) -0.12014431182649532 0.200 -0.59963969517107030 0.549
2 m_B (Intercept) 0.00000000000000123 0.283 0.00000000000000433 1.000
3 m_B Bwhite -0.24116205496397874 0.401 -0.60071814968372905 0.548
4 m_C (Intercept) -0.47957308026188367 0.353 -1.35892869678271544 0.174
5 m_C Cneg 0.80499548069651150 0.507 1.58784953814722285 0.112
6 m_C Cpos 0.30772282333522433 0.490 0.62856402205887851 0.530
7 m_BC (Intercept) -0.36339654526926718 0.399 -0.90984856337213305 0.363
8 m_BC Bwhite -0.25083209866475475 0.408 -0.61515191157571303 0.538
9 m_BC Cneg 0.81144822536950656 0.508 1.59682131202527056 0.110
10 m_BC Cpos 0.32706970242195277 0.492 0.66527127770403538 0.506
And if you must insist of the layout of your table, I came up with the following (arguably clumsy) way of rearranging everything:
out <- bind_rows(lapply(ms, tidy), .id="mod")
t1 <- out %>% select(mod, term, estimate) %>% spread(term, estimate) %>% base::t
t2 <- out %>% select(mod, term, std.error) %>% spread(term, std.error) %>% base::t
rownames(t2) <- paste0(rownames(t2), "_std_e")
tmp <- rbind(t1, t2[-1,])
new_t <- as.data.frame(tmp[-1,])
colnames(new_t) <- tmp[1,]
new_t
Alternatively, you may want to familiarise yourself with packages that are meant to display model output for publication, e.g. texreg or stargazer come to mind:
library(texreg)
screenreg(ms)
==================================================
null m_B m_C m_BC
--------------------------------------------------
(Intercept) -0.12 0.00 -0.48 -0.36
(0.20) (0.28) (0.35) (0.40)
Bwhite -0.24 -0.25
(0.40) (0.41)
Cneg 0.80 0.81
(0.51) (0.51)
Cpos 0.31 0.33
(0.49) (0.49)
--------------------------------------------------
AIC 140.27 141.91 141.66 143.28
BIC 142.87 147.12 149.48 153.70
Log Likelihood -69.13 -68.95 -67.83 -67.64
Deviance 138.27 137.91 135.66 135.28
Num. obs. 100 100 100 100
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
edited Dec 31 '18 at 15:20
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
answered Dec 31 '18 at 14:39
coffeinjunkycoffeinjunky
7,2992042
7,2992042
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
add a comment |
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
Thanks @coffeinjunky for the two different ways of doing what I wanted. These packages for model output display are very handy
– Darius
Jan 1 at 11:29
add a comment |
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1
try the
MuMInpackage?– Ben Bolker
Dec 31 '18 at 11:41
2
By the way, it is not advisable to use a name such as
tfor an object as it is a built-in R function and effectively overwrites it. If any function is going to call it later, it will try to use your dataframe first, instead of the correct call to transpose.– coffeinjunky
Dec 31 '18 at 14:50
1
msis a built-in function inlubridate.Might want to change the name too.– NelsonGon
Dec 31 '18 at 15:11