I performed multiple regression results in R and save them in the environment as a list of ncol(data), to display one of the regression results and it's summary i use this command summary(lm_results[[1]]), which prints the following
Call:
lm(formula = fml, data = data)
Residuals:
Min 1Q Median 3Q Max
-4.1615 -0.9830 -0.3605 0.3508 4.5893
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04464 0.91212 0.049 0.961506
X2 0.34424 0.08067 4.267 0.000464 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.975 on 18 degrees of freedom
Multiple R-squared: 0.5029, Adjusted R-squared: 0.4753
F-statistic: 18.21 on 1 and 18 DF, p-value: 0.0004637
I want to print all the regression results in one command like
for(i in 1:ncol(data)) Regress[i] <- summary(lm_results[[i]])
and also to be able to extract only all R-squared or Adj R-square values of all regression models (and format them in one dataframe) . How can i do that in R?
You can try any of these approaches (I have used some simulated data):
#Option 1
lapply(listofmodels,function(x)summary(x)[8])
Output:
$model1
$model1$r.squared
[1] 0.01382265
$model2
$model2$r.squared
[1] 0.9271098
Or:
#Option 2
lapply(listofmodels,function(x)summary(x)[['r.squared']])
Output:
$model1
[1] 0.01382265
$model2
[1] 0.9271098
Some data used:
#Data
listofmodels <- list(model1=lm(iris$Sepal.Length~iris$Sepal.Width),model2=lm(iris$Petal.Width~iris$Petal.Length))
We could tidy or glance the model output with broom and extract the relevant component
library(broom)
library(purrr)
map_dfr(listofmodels, tidy)
To extract only the 'r.squared'
map_dfr(listofmodels, ~ glance(.x) %>%
select(r.squared))
Related
Is there a function in R to calculate the critical value of F-statistic and compare it to the F-statistic to determine if it is significant or not? I have to calculate thousands of linear models and at the end create a dataframe with the r squared values, p-values, f-statistic, coefficients etc. for each linear model.
> summary(mod)
Call:
lm(formula = log2umi ~ Age + Sex, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.01173 -0.01173 -0.01173 -0.01152 0.98848
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0115203 0.0018178 6.337 2.47e-10 ***
Age -0.0002679 0.0006053 -0.443 0.658
SexM 0.0002059 0.0024710 0.083 0.934
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1071 on 7579 degrees of freedom
Multiple R-squared: 2.644e-05, Adjusted R-squared: -0.0002374
F-statistic: 0.1002 on 2 and 7579 DF, p-value: 0.9047
I am aware of this question: How do I get R to spit out the critical value for F-statistic based on ANOVA?
But is there one function on its own that will compare the two values and spit out True or False?
EDIT:
I wrote this, but just out of curiosity if anyone knows a better way please let me know.
f_sig is a named vector that I will later add to the dataframe
model <- lm(log2umi~Age + Sex, df)
f_crit <- qf(1-0.05, summary(model)$fstatistic[2], summary(model)$fstatistic[3] )
f <- summary(mod)$fstatistic[1]
if (f > f_crit) {
f_sig[gen] = 0 #True
} else {
f_sig[gen] = 1 #False
}
Trying to get a summary output of the linear model created with the lm() function in R but no matter which way I set it up I get my desired y value as my x input. My desired output is the summary of the model where Winnings is the y output and averagedist is the input. This is my current output:
Call:
lm(formula = Winnings ~ averagedist, data = combineddata)
Residuals:
Min 1Q Median 3Q Max
-20.4978 -5.2992 -0.3824 6.0887 23.4764
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.882e+02 7.577e-01 380.281 < 2e-16 ***
Winnings 1.293e-06 2.023e-07 6.391 8.97e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 8.343 on 232 degrees of freedom
Multiple R-squared: 0.1497, Adjusted R-squared: 0.146
F-statistic: 40.84 on 1 and 232 DF, p-value: 8.967e-10
Have tried flipping the order and defining the variables using y = winnings, x = averagedist but always get the same output.
Used summary(lm(Winnings ~ averagedist,combineddata)) as an alternative way to set it up and that seemed to do the trick, as opposed the two step method:
str<-lm(Winnings ~ averagedist,combineddata)
summary(str)
I have run a regression model in R using the lm function. The resulting ANOVA table gives me the F-value for each coefficient (which doesnt really make sense to me). What I would like to know is the t-stat for each coefficient and its corresponding p-value. How do I get this? Is it stored by the function or does it require additional computation?
Here is the code and output:
library(lubridate)
library(RCurl)
library(plyr)
[in] fit <- lm(btc_close ~ vix_close + gold_close + eth_close, data = all_dat)
# Other useful functions
coefficients(fit) # model coefficients
confint(fit, level=0.95) # CIs for model parameters
anova(fit) # anova table
[out]
Analysis of Variance Table
Response: btc_close
Df Sum Sq Mean Sq F value Pr(>F)
vix_close 1 20911897 20911897 280.1788 <2e-16 ***
gold_close 1 91902 91902 1.2313 0.2698
eth_close 1 42716393 42716393 572.3168 <2e-16 ***
Residuals 99 7389130 74638
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
If my statistics knowledge serves me correctly, these f-values are meaningless. Theoretically, I should receive an F-value for the model and a T-value for each coefficient.
Here is an example with comments of how you can extract just the t-values.
# Some dummy data
n <- 1e3L
df <- data.frame(x = rnorm(n), z = rnorm(n))
df$y <- with(df, 0.01 * x^2 + z/3)
# Run regression
lr1 <- lm(y ~ x + z, data = df)
# R has special summary method for class "lm"
summary(lr1)
# Call:
# lm(formula = y ~ x + z, data = df)
# Residuals:
# Min 1Q Median 3Q Max
# -0.010810 -0.009025 -0.005259 0.003617 0.096771
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 0.0100122 0.0004313 23.216 <2e-16 ***
# x 0.0008105 0.0004305 1.883 0.06 .
# z 0.3336034 0.0004244 786.036 <2e-16 ***
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Residual standard error: 0.01363 on 997 degrees of freedom
# Multiple R-squared: 0.9984, Adjusted R-squared: 0.9984
# F-statistic: 3.09e+05 on 2 and 997 DF, p-value: < 2.2e-16
# Now, if you only want the t-values
summary(lr1)[["coefficients"]][, "t value"]
# Or (better practice as explained in comments by Axeman)
coef(summary(lr1))[, "t value"]
# (Intercept) x z
# 23.216317 1.882841 786.035718
You could try this:
summary(fit)
summary(fit)$coefficients[,4] for p-values
summary(fit)$coefficients[,3] for t-values
As Benjamin has already answered, I would advise using broom::tidy() to coerce the model object to a tidy dataframe. The statistic column will contain the relevant test statistic and is easily available for plotting with ggplot2.
you can use this
summary(fit)$coefficients[,3]
To extract only t-values
I'm using R and I want to translate the results from lm() to an equation.
My model is:
Residuals:
Min 1Q Median 3Q Max
-0.048110 -0.023948 -0.000376 0.024511 0.044190
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.17691 0.00909 349.50 < 2e-16 ***
poly(QPB2_REF1, 2)1 0.64947 0.03015 21.54 2.66e-14 ***
poly(QPB2_REF1, 2)2 0.10824 0.03015 3.59 0.00209 **
B2DBSA_REF1DONSON -0.20959 0.01286 -16.30 3.17e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.03015 on 18 degrees of freedom
Multiple R-squared: 0.9763, Adjusted R-squared: 0.9724
F-statistic: 247.6 on 3 and 18 DF, p-value: 8.098e-15
Do you have any idea?
I tried to have something like
f <- function(x) {3.17691 + 0.64947*x +0.10824*x^2 -0.20959*1 + 0.03015^2}
but when I tried to set a x, the f(x) value is incorrect.
Your output indicates that the model includes use of the poly function which be default orthogonalizes the polynomials (includes centering the x's and other things). In your formula there is no orthogonalization done and that is the likely difference. You can refit the model using raw=TRUE in the call to poly to get the raw coefficients that can be multiplied by $x$ and $x^2$.
You may also be interested in the Function function in the rms package which automates creating functions from fitted models.
Edit
Here is an example:
library(rms)
xx <- 1:25
yy <- 5 - 1.5*xx + 0.1*xx^2 + rnorm(25)
plot(xx,yy)
fit <- ols(yy ~ pol(xx,2))
mypred <- Function(fit)
curve(mypred, add=TRUE)
mypred( c(1,25, 3, 3.5))
You need to use the rms functions for fitting (ols and pol for this example instead of lm and poly).
If you want to calculate y-hat based on the model, you can just use predict!
Example:
set.seed(123)
my_dat <- data.frame(x=1:10, e=rnorm(10))
my_dat$y <- with(my_dat, x*2 + e)
my_lm <- lm(y~x, data=my_dat)
summary(my_lm)
Result:
Call:
lm(formula = y ~ x, data = my_dat)
Residuals:
Min 1Q Median 3Q Max
-1.1348 -0.5624 -0.1393 0.3854 1.6814
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5255 0.6673 0.787 0.454
x 1.9180 0.1075 17.835 1e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9768 on 8 degrees of freedom
Multiple R-squared: 0.9755, Adjusted R-squared: 0.9724
F-statistic: 318.1 on 1 and 8 DF, p-value: 1e-07
Now, instead of making a function like 0.5255 + x * 1.9180 manually, I just call predict for my_lm:
predict(my_lm, data.frame(x=11:20))
Same result as this (not counting minor errors from rounding the slope/intercept estimates):
0.5255 + (11:20) * 1.9180
If you are looking for actually visualizing or writing out a complex equation (e.g. something that has restricted cubic spline transformations), I recommend using the rms package, fitting your model, and using the latex function to see it in latex
my_lm <- ols(y~x, data=my_dat)
latex(my_lm)
Note you will need to render the latex code so as to see your equation. There are websites and, if you are using a Mac, Mac Tex software, that will render it for you.
After running a regression, how can I select the variable name and corresponding parameter estimate?
For example, after running the following regression, I obtain:
set.seed(1)
n=1000
x=rnorm(n,0,1)
y=.6*x+rnorm(n,0,sqrt(1-.6)^2)
(reg1=summary(lm(y~x)))
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-1.2994 -0.2688 -0.0055 0.3022 1.4577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.006475 0.013162 -0.492 0.623
x 0.602573 0.012723 47.359 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4162 on 998 degrees of freedom
Multiple R-squared: 0.6921, Adjusted R-squared: 0.6918
F-statistic: 2243 on 1 and 998 DF, p-value: < 2.2e-16
I would like to be able to select the coefficient by the variable names (e.g., (Intercept) -0.006475)
I have tried the following but nothing works...
attr(reg1$coefficients,"terms")
names(reg1$coefficients)
Note: This works reg1$coefficients[1,1] but I want to be able to call it by the name rather than row / column.
The package broom tidies a lot of regression models very nicely.
require(broom)
set.seed(1)
n=1000
x=rnorm(n,0,1)
y=.6*x+rnorm(n,0,sqrt(1-.6)^2)
model = lm(y~x)
tt <- tidy(model, conf.int=TRUE)
subset(tt,term=="x")
## term estimate std.error statistic p.value conf.low conf.high
## 2 x 0.602573 0.01272349 47.35908 1.687125e-257 0.5776051 0.6275409
with(tt,tt[term=="(Intercept)","estimate"])
## [1] -0.006474794
So, your code doesn't run the way you have it. I changed it a bit:
set.seed(1)
n=1000
x=rnorm(n,0,1)
y=.6*x+rnorm(n,0,sqrt(1-.6)^2)
model = lm(y~x)
Now, I can call coef(model)["x"] or coef(model)["(Intercept)"] and get the values.
> coef(model)["x"]
x
0.602573
> coef(model)["(Intercept)"]
(Intercept)
-0.006474794