I am want to run a loop regression for fund1 till fund10 based on the the LIQ-factor. I want to do this regression:
lm(fund1S~ LIQ, data = Merge_Liq_Size)
but for all of the funds simultaneously.
I have attached some picture of the dataset to show you the setup. The dataset has 479 observation/rows. Can anyoune help me to sturcture a code? Sorry if this question is phrased in a wrong way.
Perhaps this is what you are looking for:
my_models <- lapply(paste0("fund", 1:10, "S ~ LIQ"), function(x) lm(as.formula(x), data = Merge_Liq_Size))
You can access each model by my_models[[1]] to my_models[[10]].
If it is lm, we can do this without lapply as well i.e. create a matrix as the dependent variable and construct the formula
lm(as.matrix(Merge_Liq_Size[paste0("fund", 1:10, "S")]) ~ LIQ, data = Merge_Liq_Size)
Using a small reproducible example
> data(mtcars)
> lm(as.matrix(mtcars[1:3]) ~ vs, data = mtcars)
Call:
lm(formula = as.matrix(mtcars[1:3]) ~ vs, data = mtcars)
Coefficients:
mpg cyl disp
(Intercept) 16.617 7.444 307.150
vs 7.940 -2.873 -174.693
Related
I JUST found out about this amazing R package, modelsummary.
It doesn't seem like it offers an ability to transpose regression outputs.
I know that you cannot do a tranposition within kable-extra, which is my go-to for ordinary table outputs in R. Since modelsummary relies on kable-extra for post-processing, I'm wondering if this is possible. Has anyone else figured it out?
Ideally I'd like to preserve the stars of my regression output.
This is available in STATA (below):
Thanks in advance!
You can flip the order of the terms in the group argument formula. See documentation here and also here for many examples.
library(modelsummary)
mod <- list(
lm(mpg ~ hp, mtcars),
lm(mpg ~ hp + drat, mtcars))
modelsummary(mod, group = model ~ term)
(Intercept)
hp
drat
Model 1
30.099
-0.068
(1.634)
(0.010)
Model 2
10.790
-0.052
4.698
(5.078)
(0.009)
(1.192)
The main problem with this strategy is that there is not (yet) an automatic way to append goodness of fit statistics. So you would probably have to rig something up by creating a data.frame and feeding it to the add_columns argument. For example:
N <- sapply(mod, function(x) get_gof(x)$nobs)
N <- data.frame(N = c(N[1], "", N[2], ""))
modelsummary(mod,
group = model ~ term,
add_columns = N,
align = "lcccc")
(Intercept)
hp
drat
N
Model 1
30.099
-0.068
32
(1.634)
(0.010)
Model 2
10.790
-0.052
4.698
32
(5.078)
(0.009)
(1.192)
If you have ideas about the best default behavior for goodness of fit statistics, please file a feature request on Github.
Situation
I am fitting a series of evolving regression models. For the purposes of this question, we can think of these models in terms of Model A, Model B, and Model C. All models share at least one same covariate.
I am also fitting these models for two separate years of data. Again, for the purposes of this question, the years will be 2000 and 2010.
In an attempt to simplify the reporting of results, I am attempting to combine the reporting of the regressions into a single table that would have some kind of the following format:
2000 2010
Model A
Coef Ex1
Model B
Coef Ex1
Coef Ex2
Model C
Coef Ex1
Coef Ex2
Coef Ex3
The idea being that someone can look quickly at Coef Ex1 across several models and years.
What Have I Tried
I have tried to achieve the above table using both R stargazer and kable packages. With stargazer I can get the fully formatted table for a single model formulation across many years (e.g., stargazer(modelA2000, modelA2010), but I cannot figure out how to stack additional model formulations on the rows.
For kable I have been able to stack horizontal models, but I have not been able to add in additional years (e.g., coefs <- bind_rows(tidy(modelA2000), tidy(modelB2000), tidy(modelC2000)); coefs %>% kable()).
Question: how can I use stargazer or kable to report evolving regression models (which share the same covariates) in the rows but also with year of cross section on the column? I think I can somehow extend the answer posted here, although I'm not sure how.
Reproducible example
# Load the data
mtcars <- mtcars
# Create example results for models A, B, and C for 2000
modelA2000 <- lm(mpg ~ cyl, data = mtcars)
modelB2000 <- lm(mpg ~ cyl + wt, data = mtcars)
modelC2000 <- lm(mpg ~ cyl + wt + disp, data = mtcars)
# Slightly modify data for second set of results
mtcars$cyl <- mtcars$cyl*runif(1)
# Fit second set of results. Same models, pretending it's a different year.
modelA2010 <- lm(mpg ~ cyl, data = mtcars)
modelB2010 <- lm(mpg ~ cyl + wt, data = mtcars)
modelC2010 <- lm(mpg ~ cyl + wt + disp, data = mtcars)
Two notes before starting:
You want a pretty "custom" table, so it is almost inevitable that some manual operations will be required.
My answer relies on the development version of modelsummary, which you can install like this:
library(remotes)
install_github("vincentarelbundock/modelsummary")
We will need 4 concepts, many of them related to the broom package:
broom::tidy a function that takes a statistical model and returns a data.frame of estimates with one row per coefficient.
broom::glance a function that takes a statistical model and returns a one-row data.frame with model characteristics (e.g., number of observations)
modelsummary_list a list with 2 elements called "tidy" and "glance", and with a class name of "modelsummary_list".
The modelsummary package allows you to draw regression tables. Under the hood, it uses broom::tidy and broom::glance to extract information from those models. Users can also supply their own information about a model by supplying a list to which we assign the class modelsummary_list, as documented here.
EDIT: The recommended way to do this in modelsummary is now to use the group argument. Scroll to the end of this post for illustrative code.
Obsolete example with useful discussion
The modelsummary_wide is a function that was initially designed to "stack" results from several models with several groups of coefficients. This is useful for things like multinomial models, but it also helps us in your case, where you have multiple models in multiple groups (here: years).
First, we load packages, tweak the data, and estimate our models:
library(modelsummary)
library(broom)
library(dplyr)
mtcars2010 <- mtcars
mtcars2010$cyl <- mtcars$cyl * runif(1)
models <- list(
"A" = list(
lm(mpg ~ cyl, data = mtcars),
lm(mpg ~ cyl, data = mtcars2010)),
"B" = list(
lm(mpg ~ cyl + wt, data = mtcars),
lm(mpg ~ cyl + wt, data = mtcars2010)),
"C" = list(
lm(mpg ~ cyl + wt + disp, data = mtcars),
lm(mpg ~ cyl + wt + disp, data = mtcars2010)))
Notice that we saved our models in three groups, in a list of list.
Then, we define a tidy_model function that accepts a list of two models (one per year), combines the information on those two models, and creates a modelsummary_list object (again, please refer to the documentation). Note that we assign the "year" information to a "group" column in the tidy object.
We apply this function to each of our three groups of models using lapply.
tidy_model <- function(model_list) {
# tidy estimates
tidy_2000 <- broom::tidy(model_list[[1]])
tidy_2010 <- broom::tidy(model_list[[2]])
# create a "group" column
tidy_2000$group <- 2000
tidy_2010$group <- 2010
ti <- bind_rows(tidy_2000, tidy_2010)
# glance estimates
gl <- data.frame("N" = stats::nobs(model_list[[1]]))
# output
out <- list(tidy = ti, glance = gl)
class(out) <- "modelsummary_list"
return(out)
}
models <- lapply(models, tidy_model)
Finally, we call the modelsummary_wide with the stacking="vertical" argument to obtain this table:
modelsummary_wide(models, stacking = "vertical")
Of course, the table can be adjusted, coefficients renamed, etc. using the other arguments of the modelsummary_wide function or with kableExtra or some other package supported by the output argument.
More modern example without detailed explanation
library("modelsummary")
library("broom")
library("quantreg")
mtcars2010 <- mtcars
mtcars2010$cyl <- mtcars$cyl * runif(1)
models <- list(
"A" = list(
"2000" = rq(mpg ~ cyl, data = mtcars),
"2010" = rq(mpg ~ cyl, data = mtcars2010)),
"B" = list(
"2000" = rq(mpg ~ cyl + wt, data = mtcars),
"2010" = rq(mpg ~ cyl + wt, data = mtcars2010)),
"C" = list(
"2000" = rq(mpg ~ cyl + wt + disp, data = mtcars),
"2010" = rq(mpg ~ cyl + wt + disp, data = mtcars2010)))
tidy_model <- function(model_list) {
# tidy estimates
tidy_2000 <- broom::tidy(model_list[[1]])
tidy_2010 <- broom::tidy(model_list[[2]])
# create a "group" column
tidy_2000$group <- "2000"
tidy_2010$group <- "2010"
ti <- bind_rows(tidy_2000, tidy_2010)
# output
out <- list(tidy = ti, glance = data.frame("nobs 2010" = length(model_list[[1]]$fitted.values)))
class(out) <- "modelsummary_list"
return(out)
}
models <- lapply(models, tidy_model)
modelsummary(models,
group = model + term ~ group,
statistic = "conf.int")
2000
2010
A
(Intercept)
36.800
36.800
[30.034, 42.403]
[30.034, 42.403]
cyl
-2.700
-67.944
[-3.465, -1.792]
[-87.204, -45.102]
B
(Intercept)
38.871
38.871
[30.972, 42.896]
[30.972, 42.896]
cyl
-1.743
-43.858
[-2.154, -0.535]
[-54.215, -13.472]
wt
-2.679
-2.679
[-5.313, -1.531]
[-5.313, -1.531]
C
(Intercept)
40.683
40.683
[31.235, 47.507]
[31.235, 47.507]
cyl
-1.993
-50.162
[-3.137, -1.322]
[-78.948, -33.258]
wt
-2.937
-2.937
[-5.443, -1.362]
[-5.443, -1.362]
disp
0.003
0.003
[-0.009, 0.035]
[-0.009, 0.035]
I would like to use stargazer adding a new regression but I'm not want add a new column, but that were in the same column. I tried to do some replicable example:
reg1 <- lm(mpg ~ wt + factor(am), data = mtcars)
reg2 <- lm(mpg ~ wt + factor(gear), data = mtcars)
stargazer::stargazer(reg1,reg2,
title="Results", align=TRUE , digits = 3,out="table2.tex",append=T,
keep = c("am","gear"))
Current output
Desired output:
It is not possible to do this with any specific parameter with stargazer, so you will have to modify your LaTeX code manually.
I have a function that asks for two parameters:
dataRead (dataframe from the user)
variableChosen (which dependent variable the user wants to utilize
in the model)
Obs: indepent variable will always be the first column
But if the user gives me for example, a dataframe called dataGiven which columns names are: "Doses", "Weight"
I want that my model name has these names in my results
My actual function correctly make the lm, but my formula names from the data frame are gone (and shows how I got the data from the function)
Results_REG<- function (dataRead, variableChosen){
fit1 <- lm(formula = dataRead[,1]~dataRead[,variableChosen])
return(fit1)
}
When I call:
test1 <- Results_REG(dataGive, "Weight")
names(teste1$model)
shows:
"dataRead[, 1]" "dataRead[, variableChosen]"
I wanted to show my dataframe columns names, like:
"Doses" "Weight"
First off, it's always difficult to help without a reproducible code example. For future posts I recommend familiarising yourself with how to provide such a minimal reproducible example.
I'm not entirely clear on what you're asking, so I assume this is about how to create a function that fits a simple linear model based on data with a single user-chosen predictor var.
Here is an example based on mtcars
results_LM <- function(data, var) {
lm(data[, 1] ~ data[, var])
}
results_LM(mtcars, "disp")
#Call:
#lm(formula = data[, 1] ~ data[, var])
#
#Coefficients:
#(Intercept) data[, var]
# 29.59985 -0.04122
You can confirm that this gives the same result as
lm(mpg ~ disp, data = mtcars)
Or perhaps you're asking how to carry through the column names for the predictor? In that case we can use as.formula to construct a formula that we use together with the data argument in lm.
results_LM <- function(data, var) {
fm <- as.formula(paste(colnames(data)[1], "~", var))
lm(fm, data = data)
}
fit <- results_LM(mtcars, "disp")
fit
#Call:
#lm(formula = fm, data = data)
#
#Coefficients:
#(Intercept) disp
# 29.59985 -0.04122
names(fit$model)
#[1] "mpg" "disp"
outcome <- 'mpg'
model <- lm(mtcars[,outcome] ~ . ,mtcars)
yields the same result as:
data(mtcars)
model <- lm( mpg ~ . ,mtcars)
but allows you to pass a variable (the column name). However, this may cause an error where mpg is included in the right hand side of the equation as well. Not sure if anyone knows how to fix that.
I am new to RStudio and I guess my question is pretty easy to solve but a lot of searching did not help me.
I am running a regression and summary(regression1) shows me all the coefficients and so on.
Now I am using coef(regression1) so it only gives me the coefficients which I want to export to a file.
write.csv(coef, file="regression1.csv) and the "Error in as.data.frame.default(x[[i]], optional = TRUE) : cannot coerce class ""function"" to a data.frame" occurs.
Would be great If you could help me. I am searching the web for a few hours now and was not successful.
Do I have to change coef somehow so it fits in a data.frame?
Thank you very much!
There's a contributed package called broom that simplifies this task, it converts model output to tidy dataframes. Here's a self-contained reproducible example:
Download and install the package:
library(devtools)
install_github("dgrtwo/broom")
library(broom)
Here's the normal base output, not very convenient:
lmfit <- lm(mpg ~ wt, mtcars)
lmfit
Call:
lm(formula = mpg ~ wt, data = mtcars)
Coefficients:
(Intercept) wt
37.285 -5.344
Here's the same model output after it's been tidied up by the broom package, much nicer and easier to work with:
tidy_lmfit <- tidy(lmfit)
tidy_lmfit
term estimate std.error statistic p.value
1 (Intercept) 37.285126 1.877627 19.857575 8.241799e-19
2 wt -5.344472 0.559101 -9.559044 1.293959e-10
And here's how you'd write that dataframe to CSV:
write.csv(tidy_lmfit, "tidy_lmfit.csv")