I have the following lm with a vector of dependent variables:
> fit<-lm(cbind(X1m, X3m, X6m, X1y, X2y, X3y, X5y, X6y, X10y,
X20y, X
I've found tidy
in the broom package useful for this:
fit <- lm(y ~ x, data.frame(x=1:10, y=rnorm(10))) # dummy example
library(broom)
tidy(fit)
# result is a data.frame:
# term estimate std.error statistic p.value
#1 (Intercept) -0.3317979 1.2034887 -0.2756967 0.7897685
#2 x -0.0663678 0.1939598 -0.3421730 0.7410364
x=1:100
y=2*x+1
fit<-lm(y~x)
Use the str(summary(fit))
you will see the structure of summary,and there is something that cannot be converted to dataframe
> str(summary(fit))
List of 11
$ call : language lm(formula = y ~ x)
$ terms :Classes 'terms', 'formula' length 3 y ~ x
.. ..- attr(*, "variables")= language list(y, x)
.. ..- attr(*, "factors")= int [1:2, 1] 0 1
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "y" "x"
.. .. .. ..$ : chr "x"
.. ..- attr(*, "term.labels")= chr "x"
.. ..- attr(*, "order")= int 1
.. ..- attr(*, "intercept")= int 1
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. ..- attr(*, "predvars")= language list(y, x)
.. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
.. .. ..- attr(*, "names")= chr [1:2] "y" "x"
$ residuals : Named num [1:100] 7.15e-14 -5.36e-13 2.16e-14 9.05e-15 2.49e-14 ...
..- attr(*, "names")= chr [1:100] "1" "2" "3" "4" ...
$ coefficients : num [1:2, 1:4] 1.00 2.00 1.12e-14 1.92e-16 8.93e+13 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:2] "(Intercept)" "x"
.. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
$ aliased : Named logi [1:2] FALSE FALSE
..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
$ sigma : num 5.56e-14
$ df : int [1:3] 2 98 2
$ r.squared : num 1
$ adj.r.squared: num 1
$ fstatistic : Named num [1:3] 1.08e+32 1.00 9.80e+01
..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
$ cov.unscaled : num [1:2, 1:2] 0.040606 -0.000606 -0.000606 0.000012
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:2] "(Intercept)" "x"
.. ..$ : chr [1:2] "(Intercept)" "x"
- attr(*, "class")= chr "summary.lm"
So use this can get the t-statistics and standard errors
write.csv(summary(fit)$coefficients,'C:/Users/sony/Desktop/a.csv')