In this picture, lm function only has got call and coefficients, but if using '$', other attributes can show up. How did a function just show part results like this ?
CodePudding user response：
When you enter a, it just runs the print method for the "lm" class on a (since class(a) is "lm"). The print method could be anything at all. There's no requirements on how the printed output corresponds to the underlying data.
Here's the print method called to display the output for an "lm" object
stats:::print.lm
#> function (x, digits = max(3L, getOption("digits") - 3L), ...)
#> {
#> cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"),
#> "\n\n", sep = "")
#> if (length(coef(x))) {
#> cat("Coefficients:\n")
#> print.default(format(coef(x), digits = digits), print.gap = 2L,
#> quote = FALSE)
#> }
#> else cat("No coefficients\n")
#> cat("\n")
#> invisible(x)
#> }
#> <bytecode: 0x7f8607193f38>
#> <environment: namespace:stats>
Created on 2021-11-28 by the reprex package (v2.0.1)
But again it could be anything. You can define a class "my_new_class" that just prints "zebra" no matter what, for any object regardless of the underlying data.
a <- lm(mpg ~ cyl, mtcars)
a
#>
#> Call:
#> lm(formula = mpg ~ cyl, data = mtcars)
#>
#> Coefficients:
#> (Intercept) cyl
#> 37.885 -2.876
class(a)
#> [1] "lm"
print.my_new_class <- function(x) cat('zebra\n')
class(a) <- 'my_new_class'
class(a)
#> [1] "my_new_class"
a
#> zebra
Created on 2021-11-28 by the reprex package (v2.0.1)
If you want to see the full output, you could manually run a different print method. The full output is a lot though, which explains why not all of it is printed by default.
a <- lm(mpg ~ cyl, mtcars)
print.default(a)
#> $coefficients
#> (Intercept) cyl
#> 37.88458 -2.87579
#>
#> $residuals
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 0.3701643 0.3701643 -3.5814159 0.7701643
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 3.8217446 -2.5298357 -0.5782554 -1.9814159
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> -3.5814159 -1.4298357 -2.8298357 1.5217446
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 2.4217446 0.3217446 -4.4782554 -4.4782554
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> -0.1782554 6.0185841 4.0185841 7.5185841
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> -4.8814159 0.6217446 0.3217446 -1.5782554
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 4.3217446 0.9185841 -0.3814159 4.0185841
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 0.9217446 -0.9298357 0.1217446 -4.9814159
#>
#> $effects
#> (Intercept) cyl
#> -113.6497374 -28.5956807 -3.7042540 0.7095969 3.8234479 -2.5904031
#>
#> -0.5765521 -2.1042540 -3.7042540 -1.4904031 -2.8904031 1.5234479
#>
#> 2.4234479 0.3234479 -4.4765521 -4.4765521 -0.1765521 5.8957460
#>
#> 3.8957460 7.3957460 -5.0042540 0.6234479 0.3234479 -1.5765521
#>
#> 4.3234479 0.7957460 -0.5042540 3.8957460 0.9234479 -0.9904031
#>
#> 0.1234479 -5.1042540
#>
#> $rank
#> [1] 2
#>
#> $fitted.values
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 20.62984 20.62984 26.38142 20.62984
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 14.87826 20.62984 14.87826 26.38142
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 26.38142 20.62984 20.62984 14.87826
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 14.87826 14.87826 14.87826 14.87826
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 14.87826 26.38142 26.38142 26.38142
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 26.38142 14.87826 14.87826 14.87826
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 14.87826 26.38142 26.38142 26.38142
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 14.87826 20.62984 14.87826 26.38142
#>
#> $assign
#> [1] 0 1
#>
#> $qr
#> $qr
#> (Intercept) cyl
#> Mazda RX4 -5.6568542 -35.00178567
#> Mazda RX4 Wag 0.1767767 9.94359090
#> Datsun 710 0.1767767 0.21715832
#> Hornet 4 Drive 0.1767767 0.01602374
#> Hornet Sportabout 0.1767767 -0.18511084
#> Valiant 0.1767767 0.01602374
#> Duster 360 0.1767767 -0.18511084
#> Merc 240D 0.1767767 0.21715832
#> Merc 230 0.1767767 0.21715832
#> Merc 280 0.1767767 0.01602374
#> Merc 280C 0.1767767 0.01602374
#> Merc 450SE 0.1767767 -0.18511084
#> Merc 450SL 0.1767767 -0.18511084
#> Merc 450SLC 0.1767767 -0.18511084
#> Cadillac Fleetwood 0.1767767 -0.18511084
#> Lincoln Continental 0.1767767 -0.18511084
#> Chrysler Imperial 0.1767767 -0.18511084
#> Fiat 128 0.1767767 0.21715832
#> Honda Civic 0.1767767 0.21715832
#> Toyota Corolla 0.1767767 0.21715832
#> Toyota Corona 0.1767767 0.21715832
#> Dodge Challenger 0.1767767 -0.18511084
#> AMC Javelin 0.1767767 -0.18511084
#> Camaro Z28 0.1767767 -0.18511084
#> Pontiac Firebird 0.1767767 -0.18511084
#> Fiat X1-9 0.1767767 0.21715832
#> Porsche 914-2 0.1767767 0.21715832
#> Lotus Europa 0.1767767 0.21715832
#> Ford Pantera L 0.1767767 -0.18511084
#> Ferrari Dino 0.1767767 0.01602374
#> Maserati Bora 0.1767767 -0.18511084
#> Volvo 142E 0.1767767 0.21715832
#> attr(,"assign")
#> [1] 0 1
#>
#> $qraux
#> [1] 1.176777 1.016024
#>
#> $pivot
#> [1] 1 2
#>
#> $tol
#> [1] 1e-07
#>
#> $rank
#> [1] 2
#>
#> attr(,"class")
#> [1] "qr"
#>
#> $df.residual
#> [1] 30
#>
#> $xlevels
#> named list()
#>
#> $call
#> lm(formula = mpg ~ cyl, data = mtcars)
#>
#> $terms
#> mpg ~ cyl
#> attr(,"variables")
#> list(mpg, cyl)
#> attr(,"factors")
#> cyl
#> mpg 0
#> cyl 1
#> attr(,"term.labels")
#> [1] "cyl"
#> attr(,"order")
#> [1] 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,".Environment")
#> <environment: R_GlobalEnv>
#> attr(,"predvars")
#> list(mpg, cyl)
#> attr(,"dataClasses")
#> mpg cyl
#> "numeric" "numeric"
#>
#> $model
#> mpg cyl
#> Mazda RX4 21.0 6
#> Mazda RX4 Wag 21.0 6
#> Datsun 710 22.8 4
#> Hornet 4 Drive 21.4 6
#> Hornet Sportabout 18.7 8
#> Valiant 18.1 6
#> Duster 360 14.3 8
#> Merc 240D 24.4 4
#> Merc 230 22.8 4
#> Merc 280 19.2 6
#> Merc 280C 17.8 6
#> Merc 450SE 16.4 8
#> Merc 450SL 17.3 8
#> Merc 450SLC 15.2 8
#> Cadillac Fleetwood 10.4 8
#> Lincoln Continental 10.4 8
#> Chrysler Imperial 14.7 8
#> Fiat 128 32.4 4
#> Honda Civic 30.4 4
#> Toyota Corolla 33.9 4
#> Toyota Corona 21.5 4
#> Dodge Challenger 15.5 8
#> AMC Javelin 15.2 8
#> Camaro Z28 13.3 8
#> Pontiac Firebird 19.2 8
#> Fiat X1-9 27.3 4
#> Porsche 914-2 26.0 4
#> Lotus Europa 30.4 4
#> Ford Pantera L 15.8 8
#> Ferrari Dino 19.7 6
#> Maserati Bora 15.0 8
#> Volvo 142E 21.4 4
#>
#> attr(,"class")
#> [1] "lm"
Created on 2021-11-28 by the reprex package (v2.0.1)
CodePudding user response：
You can use summary to see more of the results of lm.
summary(a)
Example (taken from this example)
#Anscombe's Quartet Q1 Data
y=c(8.04,6.95,7.58,8.81,8.33,9.96,7.24,4.26,10.84,4.82,5.68)
x1=c(10,8,13,9,11,14,6,4,12,7,5)
#Some fake data, set the seed to be reproducible.
set.seed(15)
x2=sqrt(y) rnorm(length(y))
model=lm(y~x1 x2)
summary(model)
Output
Call:
lm(formula = y ~ x1 x2)
Residuals:
Min 1Q Median 3Q Max
-1.69194 -0.61053 -0.08073 0.60553 1.61689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8278 1.7063 0.485 0.64058
x1 0.5299 0.1104 4.802 0.00135 **
x2 0.6443 0.4017 1.604 0.14744
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.141 on 8 degrees of freedom
Multiple R-squared: 0.7477, Adjusted R-squared: 0.6846
F-statistic: 11.85 on 2 and 8 DF, p-value: 0.004054