[英]How to use the felm() command for a basic fixed effects model?
這對我有用。
library(lfe)
dat <- data.frame(
"race" = sample(c("white", "black", "asian"), 72, replace=TRUE),
"year" = rep(seq(2011,2016,1), each=12),
"month" = rep(c("Jan", "Feb", "Mar", "Apr",
"May", "June", "July", "Aug",
"Sept", "Oct", "Nov", "Dec"),6),
"Tdeaths" = sample(8000:1200, 72, replace=TRUE),
"temp" = sample(45:95, 72, replace=TRUE))
str(dat)
'data.frame': 72 obs. of 5 variables:
$ race : Factor w/ 3 levels "asian","black",..: 2 2 1 2 1 3 3 1 1 3 ...
$ year : num 2011 2011 2011 2011 2011 ...
$ month : Factor w/ 12 levels "Apr","Aug","Dec",..: 5 4 8 1 9 7 6 2 12 11 ...
$ Tdeaths: int 2963 2361 5609 3795 3192 7662 1849 2808 2600 5847 ...
$ temp : int 59 70 68 80 80 61 60 55 94 55 ..
summary(est <- felm(Tdeaths ~ Temp + race | year + month, data = dat))
Call:
felm(formula = Tdeaths ~ Temp + race | year + month, data = dat)
Residuals:
Min 1Q Median 3Q Max
-3637.7 -1546.5 64.9 1309.2 3363.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
Temp 16.97 19.26 0.881 0.382
raceblack 455.74 817.24 0.558 0.579
racewhite 291.64 635.87 0.459 0.648
Residual standard error: 2156 on 52 degrees of freedom
Multiple R-squared(full model): 0.2309 Adjusted R-squared: -0.05017
Multiple R-squared(proj model): 0.02333 Adjusted R-squared: -0.3335
F-statistic(full model):0.8215 on 19 and 52 DF, p-value: 0.6728
F-statistic(proj model): 0.414 on 3 and 52 DF, p-value: 0.7436
如果您遇到錯誤,我會返回 go 並檢查您的所有數據以確保其格式正確且一致。
如果需要獲取估計的固定效應使用, getfe
使用您的數據lm
應該可以正常工作。
lm(Tdeaths ~ Temp + race + factor(year) + factor(month), data = dat)
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