Error message in model comparison using anova() in R - r

Question

I am trying to compare the two models below
H1 <- lm(y ~ x1 + x2, data = df)
H2 <- lm(y ~ x1 + x2 + x3, data = df)
anova(H1, H2)
However, I get an error message:
Error: Argument 'data' must be a data frame
And when I define the data, then I get another error message:
anova(H1, H2, data = df)
Error in .subset2(x, i) : recursive indexing failed at level 2
I tried to look at the models and they show (not sure if I am looking at the correct one, but):
H1
model list[89 x 3] (S3: data.frame) A data.frame with 89 rows and 3 columns
y double[89] 3.00 3.50 4.25 5.11 1.00 ...
x1 double[89] 19 24 31 35 20 21 ...
x2 double[89] 1 1 1 1 2 1 1 ...
str(H1)
List of 12
$ coefficients : Named num [1:3] 5.42739 0.000294 -0.950346
..- attr(*, "names")= chr [1:3] "(Intercept)" "x1" "x2"
$ residuals : Named num [1:89] -1.4844 -0.9835 -0.2326 -2.5338 0.0177 ...
..- attr(*, "names")= chr [1:89] "1" "2" "3" "4" ...
$ effects : Named num [1:89] -40.783 0.796 -3.258 -2.349 0.068 ...
..- attr(*, "names")= chr [1:89] "(Intercept)" "x1" "x2" "" ...
$ rank : int 3
$ fitted.values: Named num [1:89] 4.48 4.48 4.48 3.53 4.48 ...
..- attr(*, "names")= chr [1:89] "1" "2" "3" "4" ...
$ assign : int [1:3] 0 1 2
$ qr :List of 5
..$ qr : num [1:89, 1:3] -9.434 0.106 0.106 0.106 0.106 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:89] "1" "2" "3" "4" ...
.. .. ..$ : chr [1:3] "(Intercept) "x1" "x2"
.. ..- attr(*, "assign")= int [1:3] 0 1 2
..$ qraux: num [1:3] 1.11 1.02 1.03
..$ pivot: int [1:3] 1 2 3
..$ tol : num 1e-07
..$ rank : int 3
..- attr(*, "class")= chr "qr"
$ df.residual : int 86
$ xlevels : Named list()
$ call : language lm(formula = y ~ x1 + x2, data = df)
$ terms :Classes 'terms', 'formula' language y ~ x1 + x2
.. ..- attr(*, "variables")= language list(y, x1, x2)
.. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "y" "x1" "x2"
.. .. .. ..$ : chr [1:2] "x1" "x2"
.. ..- attr(*, "term.labels")= chr [1:2] "x1" "x2"
.. ..- attr(*, "order")= int [1:2] 1 1
.. ..- attr(*, "intercept")= int 1
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. ..- attr(*, "predvars")= language list(y, x1, x2)
.. ..- attr(*, "dataClasses")= Named chr [1:3] "numeric" "numeric" "numeric"
.. .. ..- attr(*, "names")= chr [1:3] "y" "x1" "x2"
$ model :'data.frame': 89 obs. of 3 variables:
..$ y : num [1:89] 3 3.5 4.25 1 4.5 5.25 4.75 3.75 3.5 5 ...
..$ x1 : num [1:89] 25 22 19 24 18 24 18 18 21 19 ...
..$ x2 : num [1:89] 1 1 1 2 1 1 1 1 1 1 ...
..- attr(*, "terms")=Classes 'terms', 'formula' language y ~ x1 + x2
.. .. ..- attr(*, "variables")= language list(y, x1, x2)
.. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. ..$ : chr [1:3] "y" "x1" "x2"
.. .. .. .. ..$ : chr [1:2] "x1" "x2"
.. .. ..- attr(*, "term.labels")= chr [1:2] "x1" "x2"
.. .. ..- attr(*, "order")= int [1:2] 1 1
.. .. ..- attr(*, "intercept")= int 1
.. .. ..- attr(*, "response")= int 1
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. ..- attr(*, "predvars")= language list(y x1 x2)
.. .. ..- attr(*, "dataClasses")= Named chr [1:3] "numeric" "numeric" "numeric"
.. .. .. ..- attr(*, "names")= chr [1:3] "y" "x1" "x2"
- attr(*, "class")= chr "lm"
H2
model list[89 x 3] (S3: data.frame) A data.frame with 89 rows and 4 columns
y double[89] 3.00 3.50 4.25 5.11 1.00 ...
x1 double[89] 19 24 31 35 20 21 ...
x2 double[89] 1 1 1 1 2 1 1 ...
x3 double[89] 0 0 0 0 1 0 0
both have xlevels list[0]
Let me know if you need more information.
I would really appreciate it if you could help me out with this!

Answer 1

Anova calls the data function from the lm objects. Are you sure they are right?
The term data in fit should be a data frame with columns x1, x2 and y.
If x1, x2 and y are vectors you don't use the data argument.