modeling

Andrew W. Park & John M. Drake

library(GGally); library(magrittr); data(cars) 
cars %>% ggpairs(columns=c("speed","dist"))

plot of chunk unnamed-chunk-1

library(dplyr); cars %<>% mutate(log10speed=log10(speed))
cars %>% ggpairs(columns=c("log10speed","dist"))

plot of chunk unnamed-chunk-2

Reproducibility and subsampling

x <- tibble(rnorm(10)) %>% print

# A tibble: 10 × 1
   `rnorm(10)`
         <dbl>
1   1.23836789
2  -0.64992326
3  -0.37155651
4   1.28118928
5  -1.93241589
6  -0.14352980
7  -0.05756151
8  -0.80958714
9   0.86830968
10 -0.28274651

Reproducibility and subsampling

x %>% sample_n(5)

# A tibble: 5 × 1
  `rnorm(10)`
        <dbl>
1  1.28118928
2 -0.05756151
3  1.23836789
4 -1.93241589
5 -0.64992326

Reproducibility and subsampling

x %>% sample_n(5)

# A tibble: 5 × 1
  `rnorm(10)`
        <dbl>
1  -0.8095871
2  -0.1435298
3   1.2811893
4  -0.6499233
5  -1.9324159

Reproducibility and subsampling

set.seed(123); x %>% sample_n(5) #1st call

# A tibble: 5 × 1
  `rnorm(10)`
        <dbl>
1 -0.37155651
2 -0.80958714
3  1.28118928
4 -0.05756151
5 -0.14352980

Reproducibility and subsampling

set.seed(123); x %>% sample_n(5) #2nd call

# A tibble: 5 × 1
  `rnorm(10)`
        <dbl>
1 -0.37155651
2 -0.80958714
3  1.28118928
4 -0.05756151
5 -0.14352980

Linear modeling

library(ggplot2)
ggplot(cars)+geom_point(aes(speed,dist))+
  geom_smooth(aes(speed,dist),method="lm")

plot of chunk unnamed-chunk-8

Linear modeling

summary(lm(speed~dist,data=cars))


Call:
lm(formula = speed ~ dist, data = cars)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5293 -2.1550  0.3615  2.4377  6.4179 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  8.28391    0.87438   9.474 1.44e-12 ***
dist         0.16557    0.01749   9.464 1.49e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.156 on 48 degrees of freedom
Multiple R-squared:  0.6511,    Adjusted R-squared:  0.6438 
F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12

Lists

y<-list(3.14,"eggs",lm(speed~dist,data=cars)) %>% print

[[1]]
[1] 3.14

[[2]]
[1] "eggs"

[[3]]

Call:
lm(formula = speed ~ dist, data = cars)

Coefficients:
(Intercept)         dist  
     8.2839       0.1656

Correlation coefficients

cor.test(cars$speed,cars$dist,method="spearman")


    Spearman's rank correlation rho

data:  cars$speed and cars$dist
S = 3532.8, p-value = 8.825e-14
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.8303568

modelr

create models from data
store modeling information with data
group and nest data for analysis
un-nest for visualization