README.md
In leyaozh/lm.hw4: Linear Regression
Overview
lm.hw4 is a package to fit and analyze linear regression model,
including following functions.
lm_fit() estimates the parameters of the linear regression models.lm_summary() summarizes the properties of the fitted model.lm_anova() computes analysis of variance (or deviance) tables for
fitted model objects.
Installation
install.packages("devtools")
library(devtools)
install_github("leyaozh/lm.hw4", build_vignettes = TRUE, force = TRUE)
Usage
library(lm.hw4)
# use dataset mtcats as an example
attach(mtcars)
x = cbind(hp, wt, hp * wt)
y = mpg
z = lm_fit(x, y, add.intercept = TRUE, method = "qr")
z
#> $add.intercept
#> [1] TRUE
#>
#> $coefficients
#> (Intercept) x1 x2 x3
#> 49.80842343 -0.12010209 -8.21662430 0.02784815
#>
#> $residuals
#> [1] -2.0954741 -0.7813755 -2.7848771 1.3707560 1.4100448 -0.7854161
#> [7] -1.1074453 2.7411309 1.9500834 0.6462128 -0.7537872 1.2500604
#> [13] 1.0607148 -0.8790873 -1.6217868 -1.4949003 2.1977932 4.5513369
#> [19] -2.2319540 3.6541302 -3.0631732 -2.0744146 -2.7177637 -1.7311118
#> [25] 3.2640401 -2.2390044 -0.7187056 1.8336953 0.4396692 0.1701019
#> [31] 1.4541328 -0.9136259
#>
#> $effects
#> (Intercept) x1 x2 x3
#> -113.64973741 -26.04559222 -15.89423036 8.07999113 1.51781070
#>
#> -1.02141990 -0.68576630 2.55781415 1.91580583 0.50988326
#>
#> -0.89011674 1.12119100 1.07222009 -0.88822537 -1.91166212
#>
#> -1.69803908 2.21679308 5.31175485 -0.87005072 4.75998225
#>
#> -2.54554657 -2.12810729 -2.72459486 -1.33991566 3.19524989
#>
#> -1.22976136 0.06542025 3.04184062 0.97202255 0.56994833
#>
#> 2.35227885 -0.62692830
#>
#> $rank
#> [1] 4
#>
#> $fitted.values
#> [1] 23.09547 21.78138 25.58488 20.02924 17.28996 18.88542 15.40745 21.65887
#> [9] 20.84992 18.55379 18.55379 15.14994 16.23929 16.07909 12.02179 11.89490
#> [17] 12.50221 27.84866 32.63195 30.24587 24.56317 17.57441 17.91776 15.03111
#> [25] 15.93596 29.53900 26.71871 28.56630 15.36033 19.52990 13.54587 22.31363
#>
#> $qr
#> $qr
#> hp wt
#> [1,] -5.6568542 -829.78980772 -1.819951e+01 -2.911820e+03
#> [2,] 0.1767767 381.74189579 3.588740e+00 1.769947e+03
#> [3,] 0.1767767 0.12620114 4.098743e+00 6.385918e+02
#> [4,] 0.1767767 0.08166844 -9.210001e-02 2.901446e+02
#> [5,] 0.1767767 -0.08860368 5.328237e-04 1.269484e-01
#> [6,] 0.1767767 0.09476629 -1.632227e-01 1.711451e-01
#> [7,] 0.1767767 -0.27197365 1.276918e-01 4.434795e-02
#> [8,] 0.1767767 0.20740784 -1.949441e-01 1.154693e-01
#> [9,] 0.1767767 0.12096200 -1.102864e-01 1.167319e-01
#> [10,] 0.1767767 0.04761401 -1.174894e-01 1.573710e-01
#> [11,] 0.1767767 0.04761401 -1.174894e-01 1.573710e-01
#> [12,] 0.1767767 -0.10170154 -1.418246e-01 6.333227e-02
#> [13,] 0.1767767 -0.10170154 -5.887229e-02 9.608607e-02
#> [14,] 0.1767767 -0.10170154 -7.107116e-02 9.126934e-02
#> [15,] 0.1767767 -0.16719081 -3.729763e-01 -2.209256e-01
#> [16,] 0.1767767 -0.19338652 -3.927318e-01 -3.269106e-01
#> [17,] 0.1767767 -0.23268009 -3.394127e-01 -4.170315e-01
#> [18,] 0.1767767 0.19692956 5.567198e-02 -1.770314e-01
#> [19,] 0.1767767 0.23360355 1.666235e-01 -4.303952e-01
#> [20,] 0.1767767 0.19954913 1.424540e-01 -2.902271e-01
#> [21,] 0.1767767 0.11572286 6.137739e-02 -1.240395e-02
#> [22,] 0.1767767 -0.02311440 -7.572679e-02 1.421396e-01
#> [23,] 0.1767767 -0.02311440 -5.498872e-02 1.415393e-01
#> [24,] 0.1767767 -0.27197365 6.181793e-02 -4.214949e-02
#> [25,] 0.1767767 -0.08860368 -9.827797e-02 9.491211e-02
#> [26,] 0.1767767 0.19692956 1.203260e-01 -2.556231e-01
#> [27,] 0.1767767 0.13144028 1.270521e-01 -9.743888e-02
#> [28,] 0.1767767 0.07380972 3.299583e-01 -9.612300e-02
#> [29,] 0.1767767 -0.32174550 2.684062e-01 1.790572e-01
#> [30,] 0.1767767 -0.08860368 1.639976e-01 1.799467e-01
#> [31,] 0.1767767 -0.50773504 3.319609e-01 -4.863129e-02
#> [32,] 0.1767767 0.08428801 1.176044e-02 6.763697e-02
#>
#> $rank
#> [1] 4
#>
#> $qraux
#> [1] 1.176777 1.081668 1.087675 1.132369
#>
#> $pivot
#> [1] 1 2 3 4
#>
#> attr(,"class")
#> [1] "qr"
#>
#> $df.residual
#> [1] 28
#>
#> $method
#> [1] "qr"
z.summary = lm_summary(z, correlation = TRUE)
z.summary
#> $residuals
#> 1 2 3 4 5 6 7
#> -2.0954741 -0.7813755 -2.7848771 1.3707560 1.4100448 -0.7854161 -1.1074453
#> 8 9 10 11 12 13 14
#> 2.7411309 1.9500834 0.6462128 -0.7537872 1.2500604 1.0607148 -0.8790873
#> 15 16 17 18 19 20 21
#> -1.6217868 -1.4949003 2.1977932 4.5513369 -2.2319540 3.6541302 -3.0631732
#> 22 23 24 25 26 27 28
#> -2.0744146 -2.7177637 -1.7311118 3.2640401 -2.2390044 -0.7187056 1.8336953
#> 29 30 31 32
#> 0.4396692 0.1701019 1.4541328 -0.9136259
#>
#> $coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 49.80842343 3.60515580 13.815887 5.005761e-14
#> x1 -0.12010209 0.02469835 -4.862758 4.036243e-05
#> x2 -8.21662430 1.26970814 -6.471270 5.199287e-07
#> x3 0.02784815 0.00741958 3.753332 8.108307e-04
#>
#> $aliased
#> (Intercept) x1 x2 x3
#> FALSE FALSE FALSE FALSE
#>
#> $sigma
#> [1] 2.152752
#>
#> $df
#> [1] 4 28 4
#>
#> $r.squared
#> [1] 0.8847637
#>
#> $adj.r.squared
#> [1] 0.872417
#>
#> $fstatistic
#> value numdf dendf
#> 71.65967 3.00000 28.00000
#>
#> $cov.unscaled
#> (Intercept) x1 x2 x3
#> (Intercept) 2.804531074 -0.0169996165 -0.945539647 5.366548e-03
#> x1 -0.016999616 0.0001316279 0.005310586 -3.767720e-05
#> x2 -0.945539647 0.0053105864 0.347872412 -1.850732e-03
#> x3 0.005366548 -0.0000376772 -0.001850732 1.187875e-05
#>
#> $correlation
#> (Intercept) x1 x2 x3
#> (Intercept) 1.0000000 -0.8847794 -0.9572814 0.9297784
#> x1 -0.8847794 1.0000000 0.7847991 -0.9528382
#> x2 -0.9572814 0.7847991 1.0000000 -0.9104332
#> x3 0.9297784 -0.9528382 -0.9104332 1.0000000
z.anova = lm_anova(z)
z.anova
#> $Df
#> [1] 3 28
#>
#> $`Sum Sq`
#> [1] 996.2857 129.7615
#>
#> $`Mean Sq`
#> [1] 332.095230 4.634339
#>
#> $`F value`
#> [1] 71.65967
#>
#> $`Pr(>F)`
#> [1] 2.981394e-13
detach(mtcars)
leyaozh/lm.hw4 documentation built on Dec. 3, 2019, 7:18 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Overview
lm.hw4 is a package to fit and analyze linear regression model, including following functions.
lm_fit()estimates the parameters of the linear regression models.lm_summary()summarizes the properties of the fitted model.lm_anova()computes analysis of variance (or deviance) tables for fitted model objects.
Installation
install.packages("devtools")
library(devtools)
install_github("leyaozh/lm.hw4", build_vignettes = TRUE, force = TRUE)
Usage
library(lm.hw4)
# use dataset mtcats as an example
attach(mtcars)
x = cbind(hp, wt, hp * wt)
y = mpg
z = lm_fit(x, y, add.intercept = TRUE, method = "qr")
z
#> $add.intercept
#> [1] TRUE
#>
#> $coefficients
#> (Intercept) x1 x2 x3
#> 49.80842343 -0.12010209 -8.21662430 0.02784815
#>
#> $residuals
#> [1] -2.0954741 -0.7813755 -2.7848771 1.3707560 1.4100448 -0.7854161
#> [7] -1.1074453 2.7411309 1.9500834 0.6462128 -0.7537872 1.2500604
#> [13] 1.0607148 -0.8790873 -1.6217868 -1.4949003 2.1977932 4.5513369
#> [19] -2.2319540 3.6541302 -3.0631732 -2.0744146 -2.7177637 -1.7311118
#> [25] 3.2640401 -2.2390044 -0.7187056 1.8336953 0.4396692 0.1701019
#> [31] 1.4541328 -0.9136259
#>
#> $effects
#> (Intercept) x1 x2 x3
#> -113.64973741 -26.04559222 -15.89423036 8.07999113 1.51781070
#>
#> -1.02141990 -0.68576630 2.55781415 1.91580583 0.50988326
#>
#> -0.89011674 1.12119100 1.07222009 -0.88822537 -1.91166212
#>
#> -1.69803908 2.21679308 5.31175485 -0.87005072 4.75998225
#>
#> -2.54554657 -2.12810729 -2.72459486 -1.33991566 3.19524989
#>
#> -1.22976136 0.06542025 3.04184062 0.97202255 0.56994833
#>
#> 2.35227885 -0.62692830
#>
#> $rank
#> [1] 4
#>
#> $fitted.values
#> [1] 23.09547 21.78138 25.58488 20.02924 17.28996 18.88542 15.40745 21.65887
#> [9] 20.84992 18.55379 18.55379 15.14994 16.23929 16.07909 12.02179 11.89490
#> [17] 12.50221 27.84866 32.63195 30.24587 24.56317 17.57441 17.91776 15.03111
#> [25] 15.93596 29.53900 26.71871 28.56630 15.36033 19.52990 13.54587 22.31363
#>
#> $qr
#> $qr
#> hp wt
#> [1,] -5.6568542 -829.78980772 -1.819951e+01 -2.911820e+03
#> [2,] 0.1767767 381.74189579 3.588740e+00 1.769947e+03
#> [3,] 0.1767767 0.12620114 4.098743e+00 6.385918e+02
#> [4,] 0.1767767 0.08166844 -9.210001e-02 2.901446e+02
#> [5,] 0.1767767 -0.08860368 5.328237e-04 1.269484e-01
#> [6,] 0.1767767 0.09476629 -1.632227e-01 1.711451e-01
#> [7,] 0.1767767 -0.27197365 1.276918e-01 4.434795e-02
#> [8,] 0.1767767 0.20740784 -1.949441e-01 1.154693e-01
#> [9,] 0.1767767 0.12096200 -1.102864e-01 1.167319e-01
#> [10,] 0.1767767 0.04761401 -1.174894e-01 1.573710e-01
#> [11,] 0.1767767 0.04761401 -1.174894e-01 1.573710e-01
#> [12,] 0.1767767 -0.10170154 -1.418246e-01 6.333227e-02
#> [13,] 0.1767767 -0.10170154 -5.887229e-02 9.608607e-02
#> [14,] 0.1767767 -0.10170154 -7.107116e-02 9.126934e-02
#> [15,] 0.1767767 -0.16719081 -3.729763e-01 -2.209256e-01
#> [16,] 0.1767767 -0.19338652 -3.927318e-01 -3.269106e-01
#> [17,] 0.1767767 -0.23268009 -3.394127e-01 -4.170315e-01
#> [18,] 0.1767767 0.19692956 5.567198e-02 -1.770314e-01
#> [19,] 0.1767767 0.23360355 1.666235e-01 -4.303952e-01
#> [20,] 0.1767767 0.19954913 1.424540e-01 -2.902271e-01
#> [21,] 0.1767767 0.11572286 6.137739e-02 -1.240395e-02
#> [22,] 0.1767767 -0.02311440 -7.572679e-02 1.421396e-01
#> [23,] 0.1767767 -0.02311440 -5.498872e-02 1.415393e-01
#> [24,] 0.1767767 -0.27197365 6.181793e-02 -4.214949e-02
#> [25,] 0.1767767 -0.08860368 -9.827797e-02 9.491211e-02
#> [26,] 0.1767767 0.19692956 1.203260e-01 -2.556231e-01
#> [27,] 0.1767767 0.13144028 1.270521e-01 -9.743888e-02
#> [28,] 0.1767767 0.07380972 3.299583e-01 -9.612300e-02
#> [29,] 0.1767767 -0.32174550 2.684062e-01 1.790572e-01
#> [30,] 0.1767767 -0.08860368 1.639976e-01 1.799467e-01
#> [31,] 0.1767767 -0.50773504 3.319609e-01 -4.863129e-02
#> [32,] 0.1767767 0.08428801 1.176044e-02 6.763697e-02
#>
#> $rank
#> [1] 4
#>
#> $qraux
#> [1] 1.176777 1.081668 1.087675 1.132369
#>
#> $pivot
#> [1] 1 2 3 4
#>
#> attr(,"class")
#> [1] "qr"
#>
#> $df.residual
#> [1] 28
#>
#> $method
#> [1] "qr"
z.summary = lm_summary(z, correlation = TRUE)
z.summary
#> $residuals
#> 1 2 3 4 5 6 7
#> -2.0954741 -0.7813755 -2.7848771 1.3707560 1.4100448 -0.7854161 -1.1074453
#> 8 9 10 11 12 13 14
#> 2.7411309 1.9500834 0.6462128 -0.7537872 1.2500604 1.0607148 -0.8790873
#> 15 16 17 18 19 20 21
#> -1.6217868 -1.4949003 2.1977932 4.5513369 -2.2319540 3.6541302 -3.0631732
#> 22 23 24 25 26 27 28
#> -2.0744146 -2.7177637 -1.7311118 3.2640401 -2.2390044 -0.7187056 1.8336953
#> 29 30 31 32
#> 0.4396692 0.1701019 1.4541328 -0.9136259
#>
#> $coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 49.80842343 3.60515580 13.815887 5.005761e-14
#> x1 -0.12010209 0.02469835 -4.862758 4.036243e-05
#> x2 -8.21662430 1.26970814 -6.471270 5.199287e-07
#> x3 0.02784815 0.00741958 3.753332 8.108307e-04
#>
#> $aliased
#> (Intercept) x1 x2 x3
#> FALSE FALSE FALSE FALSE
#>
#> $sigma
#> [1] 2.152752
#>
#> $df
#> [1] 4 28 4
#>
#> $r.squared
#> [1] 0.8847637
#>
#> $adj.r.squared
#> [1] 0.872417
#>
#> $fstatistic
#> value numdf dendf
#> 71.65967 3.00000 28.00000
#>
#> $cov.unscaled
#> (Intercept) x1 x2 x3
#> (Intercept) 2.804531074 -0.0169996165 -0.945539647 5.366548e-03
#> x1 -0.016999616 0.0001316279 0.005310586 -3.767720e-05
#> x2 -0.945539647 0.0053105864 0.347872412 -1.850732e-03
#> x3 0.005366548 -0.0000376772 -0.001850732 1.187875e-05
#>
#> $correlation
#> (Intercept) x1 x2 x3
#> (Intercept) 1.0000000 -0.8847794 -0.9572814 0.9297784
#> x1 -0.8847794 1.0000000 0.7847991 -0.9528382
#> x2 -0.9572814 0.7847991 1.0000000 -0.9104332
#> x3 0.9297784 -0.9528382 -0.9104332 1.0000000
z.anova = lm_anova(z)
z.anova
#> $Df
#> [1] 3 28
#>
#> $`Sum Sq`
#> [1] 996.2857 129.7615
#>
#> $`Mean Sq`
#> [1] 332.095230 4.634339
#>
#> $`F value`
#> [1] 71.65967
#>
#> $`Pr(>F)`
#> [1] 2.981394e-13
detach(mtcars)
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