source("lm_function.R")
# Example 1: cars data
x <- cars$speed
y <- cars$dist
mylm(x, y, scale_x = FALSE, actual_vs_pred = TRUE)
## $n
## [1] 50
##
## $df
## [1] 48
##
## $coefficients
## Term Estimate StdError t_statistic p_value
## 1 Intercept -17.579095 6.7584402 -2.601058 1.231882e-02
## 2 x1 3.932409 0.4155128 9.463990 1.489836e-12
##
## $sigma
## [1] 15.37959
##
## $R2
## [1] 0.6510794
# Example 2: mtcars data
x <- mtcars[, 2:7]
y <- mtcars[, 1]
mylm(x, y, diagnostics = TRUE)
## $n
## [1] 32
##
## $df
## [1] 25
##
## $coefficients
## Term Estimate StdError t_statistic p_value
## 1 Intercept 26.164407 3.482102 7.5139685 7.224742e-08
## 2 cyl -3.274241 3.246252 -1.0086220 3.228191e-01
## 3 disp 5.293842 4.825523 1.0970505 2.830743e-01
## 4 hp -5.074171 4.388007 -1.1563727 2.584599e-01
## 5 drat 2.865280 3.210463 0.8924821 3.806451e-01
## 6 wt -16.390345 4.919675 -3.3315908 2.686742e-03
## 7 qsec 3.372274 4.339307 0.7771457 4.443648e-01
##
## $sigma
## [1] 2.557161
##
## $R2
## [1] 0.8548224
# Example 3: LifeCycleSavings data
x <- LifeCycleSavings[, -1]
y <- LifeCycleSavings[, 1]
mylm(x, y, quadratic = TRUE, diagnostics = TRUE)
## $n
## [1] 50
##
## $df
## [1] 41
##
## $coefficients
## Term Estimate StdError t_statistic p_value
## 1 Intercept 16.231681 5.942887 2.7312789 0.009260773
## 2 pop15 -15.239784 10.510958 -1.4498949 0.154694932
## 3 pop75 -10.286942 13.150508 -0.7822468 0.438564170
## 4 dpi 6.261304 11.358531 0.5512424 0.584459345
## 5 ddpi 14.798326 9.658578 1.5321433 0.133167663
## 6 pop15^2 4.466102 9.258886 0.4823584 0.632116646
## 7 pop75^2 3.217247 10.354663 0.3107051 0.757598949
## 8 dpi^2 -8.038021 10.919439 -0.7361204 0.465848387
## 9 ddpi^2 -9.058591 11.543395 -0.7847424 0.437115618
##
## $sigma
## [1] 3.855315
##
## $R2
## [1] 0.3804554
# Example 4: swiss data
x <- swiss[, -1]
y <- swiss[, 1]
mylm(x, y, quadratic = TRUE, actual_vs_pred = TRUE)
## $n
## [1] 47
##
## $df
## [1] 36
##
## $coefficients
## Term Estimate StdError t_statistic p_value
## 1 Intercept 72.158061 10.24331 7.0444111 2.870972e-08
## 2 Agriculture 3.399372 24.98592 0.1360515 8.925385e-01
## 3 Examination -43.710810 21.71564 -2.0128723 5.165333e-02
## 4 Education -20.629389 21.79882 -0.9463536 3.502774e-01
## 5 Catholic -24.758243 22.08350 -1.1211197 2.696600e-01
## 6 Infant.Mortality 33.394956 23.16950 1.4413326 1.581334e-01
## 7 Agriculture^2 -16.823619 22.72824 -0.7402077 4.639743e-01
## 8 Examination^2 40.903516 24.90245 1.6425502 1.091839e-01
## 9 Education^2 -25.172873 23.79297 -1.0579962 2.971050e-01
## 10 Catholic^2 34.248852 22.50486 1.5218422 1.367849e-01
## 11 Infant.Mortality^2 -11.232037 20.45170 -0.5491981 5.862602e-01
##
## $sigma
## [1] 7.035526
##
## $R2
## [1] 0.7517468