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- Linear Regression and group by in R 10 answers
I am running a linear regression on some variables in a data frame. I'd like to be able to subset the linear regressions by a categorical variable, run the linear regression for each categorical variable, and then store the t-stats in a data frame. I'd like to do this without a loop if possible.
Here's a sample of what I'm trying to do:
a<- c("a","a","a","a","a",
"b","b","b","b","b",
"c","c","c","c","c")
b<- c(0.1,0.2,0.3,0.2,0.3,
0.1,0.2,0.3,0.2,0.3,
0.1,0.2,0.3,0.2,0.3)
c<- c(0.2,0.1,0.3,0.2,0.4,
0.2,0.5,0.2,0.1,0.2,
0.4,0.2,0.4,0.6,0.8)
cbind(a,b,c)
I can begin by running the following linear regression and pulling the t-statistic out very easily:
summary(lm(b~c))$coefficients[2,3]
However, I'd like to be able to run the regression for when column a is a, b, or c. I'd like to then store the t-stats in a table that looks like this:
variable t-stat
a 0.9
b 2.4
c 1.1
Hope that makes sense. Please let me know if you have any suggestions!
Here's a vote for the plyr package and ddply().
plyrFunc <- function(x){
mod <- lm(b~c, data = x)
return(summary(mod)$coefficients[2,3])
}
tStats <- ddply(dF, .(a), plyrFunc)
tStats
a V1
1 a 1.6124515
2 b -0.1369306
3 c 0.6852483
Here is a solution using dplyr and tidy() from the broom package. tidy() converts various statistical model outputs (e.g. lm, glm, anova, etc.) into a tidy data frame.
library(broom)
library(dplyr)
data <- data_frame(a, b, c)
data %>%
group_by(a) %>%
do(tidy(lm(b ~ c, data = .))) %>%
select(variable = a, t_stat = statistic) %>%
slice(2)
# variable t_stat
# 1 a 1.6124515
# 2 b -0.1369306
# 3 c 0.8000000
Or extracting both, the t-statistic for the intercept and the slope term:
data %>%
group_by(a) %>%
do(tidy(lm(b ~ c, data = .))) %>%
select(variable = a, term, t_stat = statistic)
# variable term t_stat
# 1 a (Intercept) 1.2366939
# 2 a c 1.6124515
# 3 b (Intercept) 2.6325081
# 4 b c -0.1369306
# 5 c (Intercept) 1.4572335
# 6 c c 0.8000000
You can use the lmList function from the nlme package to apply lm to subsets of data:
# the data
df <- data.frame(a, b, c)
library(nlme)
res <- lmList(b ~ c | a, df, pool = FALSE)
coef(summary(res))
The output:
, , (Intercept)
Estimate Std. Error t value Pr(>|t|)
a 0.1000000 0.08086075 1.236694 0.30418942
b 0.2304348 0.08753431 2.632508 0.07815663
c 0.1461538 0.10029542 1.457233 0.24110393
, , c
Estimate Std. Error t value Pr(>|t|)
a 0.50000000 0.3100868 1.6124515 0.2052590
b -0.04347826 0.3175203 -0.1369306 0.8997586
c 0.15384615 0.1923077 0.8000000 0.4821990
If you want the t values only, you can use this command:
coef(summary(res))[, "t value", -1]
# a b c
# 1.6124515 -0.1369306 0.8000000
Use split to subset the data and do the looping by lapply
dat <- data.frame(b,c)
dat_split <- split(x = dat, f = a)
res <- sapply(dat_split, function(x){
summary(lm(b~c, data = x))$coefficients[2,3]
})
Reshape the result to your needs:
data.frame(variable = names(res), "t-stat" = res)
variable t.stat
a a 1.6124515
b b -0.1369306
c c 0.8000000
You could do this:
a<- c("a","a","a","a","a",
"b","b","b","b","b",
"c","c","c","c","c")
b<- c(0.1,0.2,0.3,0.2,0.3,
0.1,0.2,0.3,0.2,0.3,
0.1,0.2,0.3,0.2,0.3)
c<- c(0.2,0.1,0.3,0.2,0.4,
0.2,0.5,0.2,0.1,0.2,
0.4,0.2,0.4,0.6,0.8)
df <- data.frame(a,b,c)
t.stats <- t(data.frame(lapply(c('a','b','c'),
function(x) summary(lm(b~c,data=df[df$a==x,]))$coefficients[2,3])))
colnames(t.stats) <- 't-stat'
rownames(t.stats) <- c('a','b','c')
Output:
> t.stats
t-stat
a 1.6124515
b -0.1369306
c 0.8000000
Unless I am mistaken the values you give in your output are not the correct ones.
Or:
t.stats <- data.frame(t.stats)
t.stats$variable <- rownames(t.stats)
> t.stats[,c(2,1)]
variable t.stat
a a 1.6124515
b b -0.1369306
c c 0.8000000
If you want a data.frame and a separate column.
来源：https://stackoverflow.com/questions/28029922/linear-regression-and-storing-results-in-data-frame