Install Packages

#install.packages(“data.table”) #install.packages(“psych”) #install.packages(“dplyr”) #install.packages(“dataframeexplorer”) #install.packages(“tidyverse”) #install.packages(“MASS”) #install.packages(“ggplot2”) #install.packages(“DT”) #install.packages(“skimr”) #install.packages(“caret”) #install.packages(“lessR”) #install.packages(“fst”) #install.packages(“ggplot”) #install.packages(“plotly”) #install.packages(“caTools”) #install.packages(‘car’) #install.packages(“relaimpo”) #install.packages(“survival”) #install.packages(“survey”) #install.packages(“Matrix”) #install.packages(“gvlma”) #install.packages(“knitr”) #install.packages(“kableExtra”) #install.packages(“shiny”) # Load library

library(data.table)
library(psych)
library(table1)
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library(explore)
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library(ggplot2)
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library(GGally)
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## Attaching package: 'GGally'
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library(gridExtra)
library(dataframeexplorer)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.1.8
## ✔ purrr     1.0.1     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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## ✖ lubridate::month()   masks data.table::month()
## ✖ lubridate::quarter() masks data.table::quarter()
## ✖ lubridate::second()  masks data.table::second()
## ✖ purrr::transpose()   masks data.table::transpose()
## ✖ lubridate::wday()    masks data.table::wday()
## ✖ lubridate::week()    masks data.table::week()
## ✖ lubridate::yday()    masks data.table::yday()
## ✖ lubridate::year()    masks data.table::year()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(readxl)
library(MASS)
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library(ggplot2)
library(explore)
library(tidyverse)
library(broom)
require(ggplot2)
library(dplyr)
library(plotly)
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library(rlang)
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##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
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library(car)
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library(broom)
library(relaimpo)
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## Loading required package: mitools
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## If you are a non-US user, a version with the interesting additional metric pmvd is available
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## from Ulrike Groempings web site at prof.beuth-hochschule.de/groemping.
library(survival)
library(survey)
library(Matrix)
library(psych)
library(gvlma)
library(caTools)
library(nlme)
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library(knitr)
library(kableExtra)
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##     group_rows

Load data

setwd("/Users/macos/Documents/Research/Journal/Data/R/mprice")
mprice=read_xlsx("datapricestock.xlsx")

Describe data

dim(mprice)
## [1] 627  12
names(mprice)
##  [1] "ma"        "id"        "quarterly" "mp"        "gdp"       "atc"      
##  [7] "buy"       "sell"      "vol"       "cpi"       "roa"       "roe"
glimpse(mprice) 
## Rows: 627
## Columns: 12
## $ ma        <chr> "SHS", "SHS", "SHS", "SHS", "SHS", "SHS", "SHS", "SHS", "SHS…
## $ id        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ quarterly <chr> "2014q1", "2014q2", "2014q3", "2014q4", "2015q1", "2015q2", …
## $ mp        <dbl> 3.270213, 3.373464, 3.379003, 3.373525, 3.305351, 3.238046, …
## $ gdp       <dbl> 0.0506, 0.0534, 0.0607, 0.0696, 0.0612, 0.0647, 0.0687, 0.07…
## $ atc       <dbl> 1.7338403, 1.3680556, 1.7738913, 1.3949382, 1.0561798, 0.979…
## $ buy       <dbl> 0.0000000000, 0.0000000000, 0.0000000000, 0.0500776203, 0.00…
## $ sell      <dbl> 0.0707550503, 0.1576944264, 0.0000000000, 0.0000000000, 0.00…
## $ vol       <dbl> 1.2927438, 1.6177976, 1.8726927, 1.4197807, 0.5520500, 0.579…
## $ cpi       <dbl> 0.0026666667, 0.0019333333, 0.0028333333, -0.0013333333, -0.…
## $ roa       <dbl> 0.020941185, 0.001974712, 0.011330372, -0.006545927, 0.00375…
## $ roe       <dbl> 0.084376270, 0.007726305, 0.068684805, -0.022879567, 0.01305…
str(mprice)
## tibble [627 × 12] (S3: tbl_df/tbl/data.frame)
##  $ ma       : chr [1:627] "SHS" "SHS" "SHS" "SHS" ...
##  $ id       : num [1:627] 1 1 1 1 1 1 1 1 1 1 ...
##  $ quarterly: chr [1:627] "2014q1" "2014q2" "2014q3" "2014q4" ...
##  $ mp       : num [1:627] 3.27 3.37 3.38 3.37 3.31 ...
##  $ gdp      : num [1:627] 0.0506 0.0534 0.0607 0.0696 0.0612 0.0647 0.0687 0.0701 0.0548 0.0578 ...
##  $ atc      : num [1:627] 1.73 1.37 1.77 1.39 1.06 ...
##  $ buy      : num [1:627] 0 0 0 0.0501 0 ...
##  $ sell     : num [1:627] 0.0708 0.1577 0 0 0 ...
##  $ vol      : num [1:627] 1.293 1.618 1.873 1.42 0.552 ...
##  $ cpi      : num [1:627] 0.002667 0.001933 0.002833 -0.001333 -0.000333 ...
##  $ roa      : num [1:627] 0.02094 0.00197 0.01133 -0.00655 0.00376 ...
##  $ roe      : num [1:627] 0.08438 0.00773 0.06868 -0.02288 0.01306 ...
mprice %>% head()
## # A tibble: 6 × 12
##   ma       id quarterly    mp    gdp   atc    buy   sell   vol      cpi      roa
##   <chr> <dbl> <chr>     <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>    <dbl>    <dbl>
## 1 SHS       1 2014q1     3.27 0.0506 1.73  0      0.0708 1.29   2.67e-3  0.0209 
## 2 SHS       1 2014q2     3.37 0.0534 1.37  0      0.158  1.62   1.93e-3  0.00197
## 3 SHS       1 2014q3     3.38 0.0607 1.77  0      0      1.87   2.83e-3  0.0113 
## 4 SHS       1 2014q4     3.37 0.0696 1.39  0.0501 0      1.42  -1.33e-3 -0.00655
## 5 SHS       1 2015q1     3.31 0.0612 1.06  0      0      0.552 -3.33e-4  0.00376
## 6 SHS       1 2015q2     3.24 0.0647 0.980 0      0      0.580  2.17e-3  0.00529
## # … with 1 more variable: roe <dbl>
attach(mprice)
head(mprice)
## # A tibble: 6 × 12
##   ma       id quarterly    mp    gdp   atc    buy   sell   vol      cpi      roa
##   <chr> <dbl> <chr>     <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>    <dbl>    <dbl>
## 1 SHS       1 2014q1     3.27 0.0506 1.73  0      0.0708 1.29   2.67e-3  0.0209 
## 2 SHS       1 2014q2     3.37 0.0534 1.37  0      0.158  1.62   1.93e-3  0.00197
## 3 SHS       1 2014q3     3.38 0.0607 1.77  0      0      1.87   2.83e-3  0.0113 
## 4 SHS       1 2014q4     3.37 0.0696 1.39  0.0501 0      1.42  -1.33e-3 -0.00655
## 5 SHS       1 2015q1     3.31 0.0612 1.06  0      0      0.552 -3.33e-4  0.00376
## 6 SHS       1 2015q2     3.24 0.0647 0.980 0      0      0.580  2.17e-3  0.00529
## # … with 1 more variable: roe <dbl>
tail(mprice)
## # A tibble: 6 × 12
##   ma       id quarterly    mp     gdp   atc   buy    sell    vol     cpi     roa
##   <chr> <dbl> <chr>     <dbl>   <dbl> <dbl> <dbl>   <dbl>  <dbl>   <dbl>   <dbl>
## 1 <NA>     19 2020q4     3.88  0.0448 0.830     0 0       0.0253 6.00e-4 0.00789
## 2 <NA>     19 2021q1     3.95  0.0472 0.938     0 0       0.0557 4.37e-3 0.0116 
## 3 <NA>     19 2021q2     4.00  0.0673 1.12      0 4.67e-4 0.109  1.03e-3 0.0168 
## 4 <NA>     19 2021q3     4.12 -0.0602 1.30      0 0       0.124  8.33e-4 0.0146 
## 5 <NA>     19 2021q4     4.16  0.0522 1.83      0 0       0.319  8.33e-4 0.00751
## 6 <NA>     19 2022q1     4.12  0.0503 1.35      0 0       0.0998 6.30e-3 0.0119 
## # … with 1 more variable: roe <dbl>
mprice %>% head() %>% data.table()
##     ma id quarterly       mp    gdp       atc        buy       sell      vol
## 1: SHS  1    2014q1 3.270213 0.0506 1.7338403 0.00000000 0.07075505 1.292744
## 2: SHS  1    2014q2 3.373464 0.0534 1.3680556 0.00000000 0.15769443 1.617798
## 3: SHS  1    2014q3 3.379003 0.0607 1.7738913 0.00000000 0.00000000 1.872693
## 4: SHS  1    2014q4 3.373525 0.0696 1.3949382 0.05007762 0.00000000 1.419781
## 5: SHS  1    2015q1 3.305351 0.0612 1.0561798 0.00000000 0.00000000 0.552050
## 6: SHS  1    2015q2 3.238046 0.0647 0.9799675 0.00000000 0.00000000 0.579740
##              cpi          roa          roe
## 1:  0.0026666667  0.020941185  0.084376270
## 2:  0.0019333333  0.001974712  0.007726305
## 3:  0.0028333333  0.011330372  0.068684805
## 4: -0.0013333333 -0.006545927 -0.022879567
## 5: -0.0003333333  0.003756064  0.013058053
## 6:  0.0021666667  0.005294538  0.017874397

Plot data

fig1 <- plot_ly(mprice, x=~id, y=~year, z=~mp)
add_lines(fig1, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig2 <- plot_ly(mprice, x=~id, y=~year, z=~gdp)
add_lines(fig2, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig3 <- plot_ly(mprice, x=~id, y=~year, z=~atc)
add_lines(fig3, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig4 <- plot_ly(mprice, x=~id, y=~year, z=~buy)
add_lines(fig4, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig5 <- plot_ly(mprice, x=~id, y=~year, z=~sell)
add_lines(fig5, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig6 <- plot_ly(mprice, x=~id, y=~year, z=~vol)
add_lines(fig6, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig7 <- plot_ly(mprice, x=~id, y=~year, z=~cpi)
add_lines(fig7, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig8 <- plot_ly(mprice, x=~id, y=~year, z=~roa)
add_lines(fig8, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
fig9 <- plot_ly(mprice, x=~id, y=~year, z=~roe)
add_lines(fig9, linetype=~id)
## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot corr between two variables

ggplot(mprice, aes(x=mp, y=gdp))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=atc))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=buy))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=sell))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=vol))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=cpi))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=roa))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(mprice, aes(x=mp, y=roe))+geom_point()+geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

boxplot(mp~gdp,col="blue")

boxplot(mp~atc,col="green")

boxplot(mp~buy,col="red")

boxplot(mp~sell,col="yellow")

boxplot(mp~vol,col="navy")

boxplot(mp~cpi,col="purple")

boxplot(mp~roa,col="orange")

boxplot(mp~roe,col="pink")

Visualization

table1(~mp+gdp+atc+buy+sell+vol+cpi+roa+roe, data=mprice)
Overall
(N=627)
mp
Mean (SD) 3.74 (0.350)
Median [Min, Max] 3.70 [2.88, 4.63]
gdp
Mean (SD) 0.0558 (0.0254)
Median [Min, Max] 0.0647 [-0.0602, 0.0765]
atc
Mean (SD) 1.27 (0.619)
Median [Min, Max] 1.07 [0.105, 4.13]
buy
Mean (SD) 0.0152 (0.0584)
Median [Min, Max] 0 [0, 0.539]
sell
Mean (SD) 0.0153 (0.0564)
Median [Min, Max] 0 [0, 0.851]
vol
Mean (SD) 0.442 (0.629)
Median [Min, Max] 0.191 [0.000236, 4.48]
cpi
Mean (SD) 0.00221 (0.00250)
Median [Min, Max] 0.00237 [-0.00303, 0.00983]
roa
Mean (SD) 0.0123 (0.0266)
Median [Min, Max] 0.00871 [-0.164, 0.265]
roe
Mean (SD) 0.0217 (0.0389)
Median [Min, Max] 0.0152 [-0.181, 0.303] 
p1=ggplot(data=mprice, aes(x=mp, col=vol))+geom_histogram()
p2=ggplot(data=mprice, aes(x=mp, col=vol))+geom_histogram(col="white", fill="blue")
p3=ggplot(data=mprice, aes(x=mp, col=vol))+geom_histogram(aes(y=..density..), col="white", fill="blue")+geom_density(col="red")
grid.arrange(p1, p2, p3, ncol=3)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: The following aesthetics were dropped during statistical transformation: colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Regression MPRICE

model=lm(mp~gdp+atc+buy+sell+vol+cpi+roa+roe,data=mprice)
summary(model)
## 
## Call:
## lm(formula = mp ~ gdp + atc + buy + sell + vol + cpi + roa + 
##     roe, data = mprice)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63015 -0.20487 -0.03431  0.21052  0.68087 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.40438    0.04202  81.018  < 2e-16 ***
## gdp         -1.78081    0.47149  -3.777 0.000174 ***
## atc          0.32879    0.02449  13.423  < 2e-16 ***
## buy          0.47380    0.21873   2.166 0.030683 *  
## sell        -0.60542    0.22814  -2.654 0.008165 ** 
## vol         -0.07047    0.02258  -3.121 0.001888 ** 
## cpi         10.47317    4.58463   2.284 0.022686 *  
## roa         -1.04875    0.86724  -1.209 0.227012    
## roe          1.70639    0.60961   2.799 0.005283 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2776 on 618 degrees of freedom
## Multiple R-squared:  0.3806, Adjusted R-squared:  0.3726 
## F-statistic: 47.47 on 8 and 618 DF,  p-value: < 2.2e-16

Test model

vif(model)
##      gdp      atc      buy     sell      vol      cpi      roa      roe 
## 1.167011 1.864283 1.325006 1.345647 1.636108 1.067635 4.334547 4.578768
pairs.panels(mprice) 

anova(model)
## Analysis of Variance Table
## 
## Response: mp
##            Df Sum Sq Mean Sq  F value    Pr(>F)    
## gdp         1  5.353  5.3535  69.4607 5.056e-16 ***
## atc         1 21.072 21.0719 273.4055 < 2.2e-16 ***
## buy         1  0.056  0.0563   0.7304  0.393083    
## sell        1  0.640  0.6402   8.3064  0.004088 ** 
## vol         1  0.791  0.7912  10.2656  0.001425 ** 
## cpi         1  0.332  0.3317   4.3035  0.038447 *  
## roa         1  0.421  0.4213   5.4660  0.019708 *  
## roe         1  0.604  0.6039   7.8353  0.005283 ** 
## Residuals 618 47.630  0.0771                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
res=resid(model)
hist(res)

qqnorm(res)
qqline(res)

AIC(model)
## [1] 183.2721
summary(model)
## 
## Call:
## lm(formula = mp ~ gdp + atc + buy + sell + vol + cpi + roa + 
##     roe, data = mprice)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63015 -0.20487 -0.03431  0.21052  0.68087 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.40438    0.04202  81.018  < 2e-16 ***
## gdp         -1.78081    0.47149  -3.777 0.000174 ***
## atc          0.32879    0.02449  13.423  < 2e-16 ***
## buy          0.47380    0.21873   2.166 0.030683 *  
## sell        -0.60542    0.22814  -2.654 0.008165 ** 
## vol         -0.07047    0.02258  -3.121 0.001888 ** 
## cpi         10.47317    4.58463   2.284 0.022686 *  
## roa         -1.04875    0.86724  -1.209 0.227012    
## roe          1.70639    0.60961   2.799 0.005283 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2776 on 618 degrees of freedom
## Multiple R-squared:  0.3806, Adjusted R-squared:  0.3726 
## F-statistic: 47.47 on 8 and 618 DF,  p-value: < 2.2e-16
durbinWatsonTest(model)
##  lag Autocorrelation D-W Statistic p-value
##    1       0.8097485     0.3703706       0
##  Alternative hypothesis: rho != 0
outlierTest(model)
## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##    rstudent unadjusted p-value Bonferroni p
## 66 2.472397            0.01369           NA

Robust - Boostrap

calc.relimp(model, type=c("lmg","last","first","pratt"),rela=TRUE)
## Response variable: mp 
## Total response variance: 0.1228439 
## Analysis based on 627 observations 
## 
## 8 Regressors: 
## gdp atc buy sell vol cpi roa roe 
## Proportion of variance explained by model: 38.06%
## Metrics are normalized to sum to 100% (rela=TRUE). 
## 
## Relative importance metrics: 
## 
##              lmg        last        first        pratt
## gdp  0.094002342 0.061904571 1.088303e-01  0.089542131
## atc  0.636088198 0.781921324 5.224814e-01  0.881305537
## buy  0.010593236 0.020361097 6.394422e-03  0.013263670
## sell 0.010104476 0.030560068 9.911148e-05 -0.002038724
## vol  0.082418475 0.042260766 1.180406e-01 -0.091231330
## cpi  0.009598759 0.022645392 2.194589e-04  0.002326117
## roa  0.049609738 0.006345946 8.904902e-02 -0.049979078
## roe  0.107584775 0.034000836 1.548857e-01  0.156811675
## 
## Average coefficients for different model sizes: 
## 
##               1X        2Xs         3Xs         4Xs          5Xs         6Xs
## gdp  -3.63751782 -3.1702034 -2.80588711 -2.51657321 -2.280875330 -2.08417980
## atc   0.32759260  0.3274922  0.32751959  0.32761043  0.327746836  0.32795087
## buy   0.38388075  0.3585846  0.35900942  0.37312183  0.394501042  0.41965435
## sell  0.04946348 -0.1181375 -0.25471282 -0.36349593 -0.449063444 -0.51575167
## vol   0.15322851  0.1086319  0.06935854  0.03471861  0.004060501 -0.02325317
## cpi   1.66059722  4.9149399  7.11110057  8.56496476  9.499325935 10.06357699
## roa   3.14033304  1.9260998  0.96540944  0.22263332 -0.330623742 -0.71644831
## roe   2.83253727  2.6471053  2.48798672  2.34444421  2.205197702  2.05918613
##              7Xs         8Xs
## gdp  -1.91823395 -1.78081131
## atc   0.32827544  0.32879480
## buy   0.44663022  0.47379881
## sell -0.56709602 -0.60541662
## vol  -0.04787043 -0.07046727
## cpi  10.36000505 10.47316629
## roa  -0.95171266 -1.04874531
## roe   1.89607427  1.70638876
boot <- boot.relimp(model, b=1000, type=c("lmg", "last", "first", "pratt"), rank = TRUE, diff=TRUE, rela= TRUE)
booteval.relimp(boot, typesel = c("lmg"), level=0.9, bty="perc", nodiff=T)
## Response variable: mp 
## Total response variance: 0.1228439 
## Analysis based on 627 observations 
## 
## 8 Regressors: 
## gdp atc buy sell vol cpi roa roe 
## Proportion of variance explained by model: 38.06%
## Metrics are normalized to sum to 100% (rela=TRUE). 
## 
## Relative importance metrics:
## Warning in matrix(cbind(x@lmg, x@pmvd, x@last, x@first, x@betasq, x@pratt, :
## data length differs from size of matrix: [32 != 8 x 1]
##              lmg
## gdp  0.094002342
## atc  0.636088198
## buy  0.010593236
## sell 0.010104476
## vol  0.082418475
## cpi  0.009598759
## roa  0.049609738
## roe  0.107584775
## 
## Average coefficients for different model sizes: 
## 
##               1X        2Xs         3Xs         4Xs          5Xs         6Xs
## gdp  -3.63751782 -3.1702034 -2.80588711 -2.51657321 -2.280875330 -2.08417980
## atc   0.32759260  0.3274922  0.32751959  0.32761043  0.327746836  0.32795087
## buy   0.38388075  0.3585846  0.35900942  0.37312183  0.394501042  0.41965435
## sell  0.04946348 -0.1181375 -0.25471282 -0.36349593 -0.449063444 -0.51575167
## vol   0.15322851  0.1086319  0.06935854  0.03471861  0.004060501 -0.02325317
## cpi   1.66059722  4.9149399  7.11110057  8.56496476  9.499325935 10.06357699
## roa   3.14033304  1.9260998  0.96540944  0.22263332 -0.330623742 -0.71644831
## roe   2.83253727  2.6471053  2.48798672  2.34444421  2.205197702  2.05918613
##              7Xs         8Xs
## gdp  -1.91823395 -1.78081131
## atc   0.32827544  0.32879480
## buy   0.44663022  0.47379881
## sell -0.56709602 -0.60541662
## vol  -0.04787043 -0.07046727
## cpi  10.36000505 10.47316629
## roa  -0.95171266 -1.04874531
## roe   1.89607427  1.70638876
## 
##  
##  Confidence interval information ( 1000 bootstrap replicates, bty= perc ): 
## Relative Contributions with confidence intervals: 
##  
##                              Lower  Upper
##          percentage 0.9      0.9    0.9   
## gdp.lmg  0.0940     _BCDE___ 0.0479 0.1451
## atc.lmg  0.6361     A_______ 0.5594 0.6880
## buy.lmg  0.0106     _____FGH 0.0012 0.0340
## sell.lmg 0.0101     _____FGH 0.0035 0.0261
## vol.lmg  0.0824     _BCD____ 0.0647 0.1057
## cpi.lmg  0.0096     _____FGH 0.0024 0.0349
## roa.lmg  0.0496     ___DE___ 0.0309 0.0745
## roe.lmg  0.1076     _BCD____ 0.0695 0.1504
## 
## Letters indicate the ranks covered by bootstrap CIs. 
## (Rank bootstrap confidence intervals always obtained by percentile method) 
## CAUTION: Bootstrap confidence intervals can be somewhat liberal.
booteval.relimp(boot)
## Response variable: mp 
## Total response variance: 0.1228439 
## Analysis based on 627 observations 
## 
## 8 Regressors: 
## gdp atc buy sell vol cpi roa roe 
## Proportion of variance explained by model: 38.06%
## Metrics are normalized to sum to 100% (rela=TRUE). 
## 
## Relative importance metrics: 
## 
##              lmg        last        first        pratt
## gdp  0.094002342 0.061904571 1.088303e-01  0.089542131
## atc  0.636088198 0.781921324 5.224814e-01  0.881305537
## buy  0.010593236 0.020361097 6.394422e-03  0.013263670
## sell 0.010104476 0.030560068 9.911148e-05 -0.002038724
## vol  0.082418475 0.042260766 1.180406e-01 -0.091231330
## cpi  0.009598759 0.022645392 2.194589e-04  0.002326117
## roa  0.049609738 0.006345946 8.904902e-02 -0.049979078
## roe  0.107584775 0.034000836 1.548857e-01  0.156811675
## 
## Average coefficients for different model sizes: 
## 
##               1X        2Xs         3Xs         4Xs          5Xs         6Xs
## gdp  -3.63751782 -3.1702034 -2.80588711 -2.51657321 -2.280875330 -2.08417980
## atc   0.32759260  0.3274922  0.32751959  0.32761043  0.327746836  0.32795087
## buy   0.38388075  0.3585846  0.35900942  0.37312183  0.394501042  0.41965435
## sell  0.04946348 -0.1181375 -0.25471282 -0.36349593 -0.449063444 -0.51575167
## vol   0.15322851  0.1086319  0.06935854  0.03471861  0.004060501 -0.02325317
## cpi   1.66059722  4.9149399  7.11110057  8.56496476  9.499325935 10.06357699
## roa   3.14033304  1.9260998  0.96540944  0.22263332 -0.330623742 -0.71644831
## roe   2.83253727  2.6471053  2.48798672  2.34444421  2.205197702  2.05918613
##              7Xs         8Xs
## gdp  -1.91823395 -1.78081131
## atc   0.32827544  0.32879480
## buy   0.44663022  0.47379881
## sell -0.56709602 -0.60541662
## vol  -0.04787043 -0.07046727
## cpi  10.36000505 10.47316629
## roa  -0.95171266 -1.04874531
## roe   1.89607427  1.70638876
## 
##  
##  Confidence interval information ( 1000 bootstrap replicates, bty= perc ): 
## Relative Contributions with confidence intervals: 
##  
##                                Lower  Upper
##            percentage 0.95     0.95    0.95   
## gdp.lmg     0.0940    _BCDE___  0.0398  0.1572
## atc.lmg     0.6361    A_______  0.5449  0.7010
## buy.lmg     0.0106    _____FGH  0.0008  0.0404
## sell.lmg    0.0101    _____FGH  0.0029  0.0288
## vol.lmg     0.0824    _BCDE___  0.0599  0.1107
## cpi.lmg     0.0096    _____FGH  0.0019  0.0432
## roa.lmg     0.0496    ___DEF__  0.0282  0.0803
## roe.lmg     0.1076    _BCD____  0.0618  0.1598
##                                               
## gdp.last    0.0619    _BCDEFG_  0.0131  0.1324
## atc.last    0.7819    A_______  0.6482  0.8678
## buy.last    0.0204    _BCDEFGH  0.0002  0.0730
## sell.last   0.0306    _BCDEFGH  0.0013  0.0792
## vol.last    0.0423    _BCDEFGH  0.0060  0.1012
## cpi.last    0.0226    _BCDEFGH  0.0006  0.0787
## roa.last    0.0063    ___DEFGH  0.0000  0.0394
## roe.last    0.0340    _BCDEFG_  0.0018  0.0943
##                                               
## gdp.first   0.1088    _BCDE___  0.0512  0.1719
## atc.first   0.5225    A_______  0.4451  0.5968
## buy.first   0.0064    _____FGH  0.0000  0.0276
## sell.first  0.0001    _____FGH  0.0000  0.0123
## vol.first   0.1180    _BCDE___  0.0755  0.1623
## cpi.first   0.0002    _____FGH  0.0000  0.0154
## roa.first   0.0890    __CDE___  0.0461  0.1363
## roe.first   0.1549    _BCD____  0.1007  0.2046
##                                               
## gdp.pratt   0.0895    _BCD____  0.0312  0.1560
## atc.pratt   0.8813    A_______  0.7640  0.9748
## buy.pratt   0.0133    ___DEF__ -0.0007  0.0515
## sell.pratt -0.0020    ___DEFG_ -0.0192  0.0205
## vol.pratt  -0.0912    ______GH -0.1392 -0.0364
## cpi.pratt   0.0023    ___DEFG_ -0.0060  0.0327
## roa.pratt  -0.0500    ___DEFGH -0.1377  0.0389
## roe.pratt   0.1568    _BC_____  0.0312  0.3029
## 
## Letters indicate the ranks covered by bootstrap CIs. 
## (Rank bootstrap confidence intervals always obtained by percentile method) 
## CAUTION: Bootstrap confidence intervals can be somewhat liberal. 
## 
##  
##  Differences between Relative Contributions: 
##  
##                                Lower   Upper
##                difference 0.95 0.95    0.95   
## gdp-atc.lmg    -0.5421     *   -0.6428 -0.4108
## gdp-buy.lmg     0.0834     *    0.0190  0.1463
## gdp-sell.lmg    0.0839     *    0.0243  0.1452
## gdp-vol.lmg     0.0116         -0.0482  0.0835
## gdp-cpi.lmg     0.0844     *    0.0202  0.1490
## gdp-roa.lmg     0.0444         -0.0258  0.1183
## gdp-roe.lmg    -0.0136         -0.0952  0.0768
## atc-buy.lmg     0.6255     *    0.5276  0.6926
## atc-sell.lmg    0.6260     *    0.5287  0.6917
## atc-vol.lmg     0.5537     *    0.4615  0.6211
## atc-cpi.lmg     0.6265     *    0.5293  0.6948
## atc-roa.lmg     0.5865     *    0.4780  0.6615
## atc-roe.lmg     0.5285     *    0.4010  0.6213
## buy-sell.lmg    0.0005         -0.0200  0.0266
## buy-vol.lmg    -0.0718     *   -0.1016 -0.0302
## buy-cpi.lmg     0.0010         -0.0325  0.0313
## buy-roa.lmg    -0.0390     *   -0.0733 -0.0042
## buy-roe.lmg    -0.0970     *   -0.1527 -0.0445
## sell-vol.lmg   -0.0723     *   -0.1014 -0.0419
## sell-cpi.lmg    0.0005         -0.0310  0.0225
## sell-roa.lmg   -0.0395     *   -0.0710 -0.0108
## sell-roe.lmg   -0.0975     *   -0.1487 -0.0461
## vol-cpi.lmg     0.0728     *    0.0306  0.1024
## vol-roa.lmg     0.0328         -0.0071  0.0716
## vol-roe.lmg    -0.0252         -0.0858  0.0316
## cpi-roa.lmg    -0.0400     *   -0.0712 -0.0008
## cpi-roe.lmg    -0.0980     *   -0.1493 -0.0400
## roa-roe.lmg    -0.0580     *   -0.0910 -0.0241
##                                               
## gdp-atc.last   -0.7200     *   -0.8442 -0.5379
## gdp-buy.last    0.0415         -0.0318  0.1160
## gdp-sell.last   0.0313         -0.0409  0.1109
## gdp-vol.last    0.0196         -0.0606  0.1043
## gdp-cpi.last    0.0393         -0.0276  0.1102
## gdp-roa.last    0.0556         -0.0041  0.1243
## gdp-roe.last    0.0279         -0.0517  0.1079
## atc-buy.last    0.7616     *    0.5942  0.8588
## atc-sell.last   0.7514     *    0.5997  0.8532
## atc-vol.last    0.7397     *    0.5949  0.8455
## atc-cpi.last    0.7593     *    0.5951  0.8572
## atc-roa.last    0.7756     *    0.6299  0.8640
## atc-roe.last    0.7479     *    0.5837  0.8519
## buy-sell.last  -0.0102         -0.0521  0.0329
## buy-vol.last   -0.0219         -0.0872  0.0469
## buy-cpi.last   -0.0023         -0.0647  0.0512
## buy-roa.last    0.0140         -0.0298  0.0631
## buy-roe.last   -0.0136         -0.0827  0.0453
## sell-vol.last  -0.0117         -0.0834  0.0545
## sell-cpi.last   0.0079         -0.0549  0.0680
## sell-roa.last   0.0242         -0.0234  0.0727
## sell-roe.last  -0.0034         -0.0710  0.0634
## vol-cpi.last    0.0196         -0.0514  0.0854
## vol-roa.last    0.0359         -0.0187  0.0951
## vol-roe.last    0.0083         -0.0688  0.0818
## cpi-roa.last    0.0163         -0.0263  0.0746
## cpi-roe.last   -0.0114         -0.0812  0.0576
## roa-roe.last   -0.0277         -0.0652  0.0002
##                                               
## gdp-atc.first  -0.4137     *   -0.5179 -0.3125
## gdp-buy.first   0.1024     *    0.0357  0.1659
## gdp-sell.first  0.1087     *    0.0491  0.1694
## gdp-vol.first  -0.0092         -0.0821  0.0683
## gdp-cpi.first   0.1086     *    0.0461  0.1703
## gdp-roa.first   0.0198         -0.0659  0.1087
## gdp-roe.first  -0.0461         -0.1348  0.0551
## atc-buy.first   0.5161     *    0.4382  0.5864
## atc-sell.first  0.5224     *    0.4419  0.5951
## atc-vol.first   0.4044     *    0.3177  0.4976
## atc-cpi.first   0.5223     *    0.4437  0.5933
## atc-roa.first   0.4334     *    0.3214  0.5453
## atc-roe.first   0.3676     *    0.2569  0.4794
## buy-sell.first  0.0063         -0.0060  0.0252
## buy-vol.first  -0.1116     *   -0.1588 -0.0605
## buy-cpi.first   0.0062         -0.0103  0.0253
## buy-roa.first  -0.0827     *   -0.1305 -0.0350
## buy-roe.first  -0.1485     *   -0.1984 -0.0895
## sell-vol.first -0.1179     *   -0.1603 -0.0736
## sell-cpi.first -0.0001         -0.0138  0.0103
## sell-roa.first -0.0889     *   -0.1340 -0.0442
## sell-roe.first -0.1548     *   -0.2032 -0.0986
## vol-cpi.first   0.1178     *    0.0723  0.1600
## vol-roa.first   0.0290         -0.0395  0.1008
## vol-roe.first  -0.0368         -0.1109  0.0434
## cpi-roa.first  -0.0888     *   -0.1337 -0.0418
## cpi-roe.first  -0.1547     *   -0.2028 -0.0978
## roa-roe.first  -0.0658     *   -0.0999 -0.0302
##                                               
## gdp-atc.pratt  -0.7918     *   -0.9249 -0.6241
## gdp-buy.pratt   0.0763     *    0.0012  0.1463
## gdp-sell.pratt  0.0916     *    0.0308  0.1579
## gdp-vol.pratt   0.1808     *    0.0939  0.2560
## gdp-cpi.pratt   0.0872     *    0.0173  0.1549
## gdp-roa.pratt   0.1395     *    0.0299  0.2391
## gdp-roe.pratt  -0.0673         -0.2390  0.0860
## atc-buy.pratt   0.8680     *    0.7374  0.9635
## atc-sell.pratt  0.8833     *    0.7601  0.9770
## atc-vol.pratt   0.9725     *    0.8241  1.0955
## atc-cpi.pratt   0.8790     *    0.7486  0.9735
## atc-roa.pratt   0.9313     *    0.7877  1.0272
## atc-roe.pratt   0.7245     *    0.4964  0.9017
## buy-sell.pratt  0.0153         -0.0171  0.0662
## buy-vol.pratt   0.1045     *    0.0425  0.1631
## buy-cpi.pratt   0.0109         -0.0250  0.0506
## buy-roa.pratt   0.0632         -0.0265  0.1541
## buy-roe.pratt  -0.1435     *   -0.2937 -0.0140
## sell-vol.pratt  0.0892     *    0.0332  0.1420
## sell-cpi.pratt -0.0044         -0.0378  0.0201
## sell-roa.pratt  0.0479         -0.0453  0.1427
## sell-roe.pratt -0.1589     *   -0.3030 -0.0267
## vol-cpi.pratt  -0.0936     *   -0.1449 -0.0376
## vol-roa.pratt  -0.0413         -0.1469  0.0653
## vol-roe.pratt  -0.2480     *   -0.3907 -0.1037
## cpi-roa.pratt   0.0523         -0.0340  0.1429
## cpi-roe.pratt  -0.1545     *   -0.3048 -0.0167
## roa-roe.pratt  -0.2068     *   -0.4333 -0.0054
## 
## * indicates that CI for difference does not include 0. 
## CAUTION: Bootstrap confidence intervals can be somewhat liberal.
plot(booteval.relimp(boot, sort=TRUE))

Explore all data

temp=mprice[, c("mp", "gdp","atc", "buy","sell", "vol", "cpi", "roa", "roe")]
explore_all(temp)

calc.relimp(temp, type=c("lmg","last","first","pratt"),rela=TRUE)
## Response variable: mp 
## Total response variance: 0.1228439 
## Analysis based on 627 observations 
## 
## 8 Regressors: 
## gdp atc buy sell vol cpi roa roe 
## Proportion of variance explained by model: 38.06%
## Metrics are normalized to sum to 100% (rela=TRUE). 
## 
## Relative importance metrics: 
## 
##              lmg        last        first        pratt
## gdp  0.094002342 0.061904571 1.088303e-01  0.089542131
## atc  0.636088198 0.781921324 5.224814e-01  0.881305537
## buy  0.010593236 0.020361097 6.394422e-03  0.013263670
## sell 0.010104476 0.030560068 9.911148e-05 -0.002038724
## vol  0.082418475 0.042260766 1.180406e-01 -0.091231330
## cpi  0.009598759 0.022645392 2.194589e-04  0.002326117
## roa  0.049609738 0.006345946 8.904902e-02 -0.049979078
## roe  0.107584775 0.034000836 1.548857e-01  0.156811675
## 
## Average coefficients for different model sizes: 
## 
##               1X        2Xs         3Xs         4Xs          5Xs         6Xs
## gdp  -3.63751782 -3.1702034 -2.80588711 -2.51657321 -2.280875330 -2.08417980
## atc   0.32759260  0.3274922  0.32751959  0.32761043  0.327746836  0.32795087
## buy   0.38388075  0.3585846  0.35900942  0.37312183  0.394501042  0.41965435
## sell  0.04946348 -0.1181375 -0.25471282 -0.36349593 -0.449063444 -0.51575167
## vol   0.15322851  0.1086319  0.06935854  0.03471861  0.004060501 -0.02325317
## cpi   1.66059722  4.9149399  7.11110057  8.56496476  9.499325935 10.06357699
## roa   3.14033304  1.9260998  0.96540944  0.22263332 -0.330623742 -0.71644831
## roe   2.83253727  2.6471053  2.48798672  2.34444421  2.205197702  2.05918613
##              7Xs         8Xs
## gdp  -1.91823395 -1.78081131
## atc   0.32827544  0.32879480
## buy   0.44663022  0.47379881
## sell -0.56709602 -0.60541662
## vol  -0.04787043 -0.07046727
## cpi  10.36000505 10.47316629
## roa  -0.95171266 -1.04874531
## roe   1.89607427  1.70638876
boot <- boot.relimp(temp, b=1000, type=c("lmg", "last", "first", "pratt"), rank = TRUE, diff=TRUE, rela= TRUE)
booteval.relimp(boot)
## Response variable: mp 
## Total response variance: 0.1228439 
## Analysis based on 627 observations 
## 
## 8 Regressors: 
## gdp atc buy sell vol cpi roa roe 
## Proportion of variance explained by model: 38.06%
## Metrics are normalized to sum to 100% (rela=TRUE). 
## 
## Relative importance metrics: 
## 
##              lmg        last        first        pratt
## gdp  0.094002342 0.061904571 1.088303e-01  0.089542131
## atc  0.636088198 0.781921324 5.224814e-01  0.881305537
## buy  0.010593236 0.020361097 6.394422e-03  0.013263670
## sell 0.010104476 0.030560068 9.911148e-05 -0.002038724
## vol  0.082418475 0.042260766 1.180406e-01 -0.091231330
## cpi  0.009598759 0.022645392 2.194589e-04  0.002326117
## roa  0.049609738 0.006345946 8.904902e-02 -0.049979078
## roe  0.107584775 0.034000836 1.548857e-01  0.156811675
## 
## Average coefficients for different model sizes: 
## 
##               1X        2Xs         3Xs         4Xs          5Xs         6Xs
## gdp  -3.63751782 -3.1702034 -2.80588711 -2.51657321 -2.280875330 -2.08417980
## atc   0.32759260  0.3274922  0.32751959  0.32761043  0.327746836  0.32795087
## buy   0.38388075  0.3585846  0.35900942  0.37312183  0.394501042  0.41965435
## sell  0.04946348 -0.1181375 -0.25471282 -0.36349593 -0.449063444 -0.51575167
## vol   0.15322851  0.1086319  0.06935854  0.03471861  0.004060501 -0.02325317
## cpi   1.66059722  4.9149399  7.11110057  8.56496476  9.499325935 10.06357699
## roa   3.14033304  1.9260998  0.96540944  0.22263332 -0.330623742 -0.71644831
## roe   2.83253727  2.6471053  2.48798672  2.34444421  2.205197702  2.05918613
##              7Xs         8Xs
## gdp  -1.91823395 -1.78081131
## atc   0.32827544  0.32879480
## buy   0.44663022  0.47379881
## sell -0.56709602 -0.60541662
## vol  -0.04787043 -0.07046727
## cpi  10.36000505 10.47316629
## roa  -0.95171266 -1.04874531
## roe   1.89607427  1.70638876
## 
##  
##  Confidence interval information ( 1000 bootstrap replicates, bty= perc ): 
## Relative Contributions with confidence intervals: 
##  
##                                Lower  Upper
##            percentage 0.95     0.95    0.95   
## gdp.lmg     0.0940    _BCDE___  0.0405  0.1547
## atc.lmg     0.6361    A_______  0.5460  0.6993
## buy.lmg     0.0106    _____FGH  0.0011  0.0400
## sell.lmg    0.0101    _____FGH  0.0023  0.0290
## vol.lmg     0.0824    _BCDE___  0.0621  0.1110
## cpi.lmg     0.0096    _____FGH  0.0019  0.0460
## roa.lmg     0.0496    ___DEF__  0.0297  0.0792
## roe.lmg     0.1076    _BCD____  0.0653  0.1560
##                                               
## gdp.last    0.0619    _BCDEFG_  0.0141  0.1331
## atc.last    0.7819    A_______  0.6468  0.8640
## buy.last    0.0204    _BCDEFGH  0.0003  0.0686
## sell.last   0.0306    _BCDEFGH  0.0015  0.0784
## vol.last    0.0423    _BCDEFGH  0.0064  0.0997
## cpi.last    0.0226    _BCDEFGH  0.0005  0.0813
## roa.last    0.0063    ___DEFGH  0.0000  0.0389
## roe.last    0.0340    _BCDEFGH  0.0017  0.0926
##                                               
## gdp.first   0.1088    _BCDE___  0.0540  0.1672
## atc.first   0.5225    A_______  0.4490  0.5940
## buy.first   0.0064    _____FGH  0.0001  0.0289
## sell.first  0.0001    _____FGH  0.0000  0.0119
## vol.first   0.1180    _BCDE___  0.0766  0.1614
## cpi.first   0.0002    _____FGH  0.0000  0.0188
## roa.first   0.0890    __CDE___  0.0481  0.1357
## roe.first   0.1549    _BCD____  0.1046  0.2010
##                                               
## gdp.pratt   0.0895    _BC_____  0.0305  0.1580
## atc.pratt   0.8813    A_______  0.7653  0.9803
## buy.pratt   0.0133    ___DEF__ -0.0010  0.0498
## sell.pratt -0.0020    ___DEFG_ -0.0187  0.0208
## vol.pratt  -0.0912    ______GH -0.1411 -0.0372
## cpi.pratt   0.0023    ___DEFG_ -0.0055  0.0386
## roa.pratt  -0.0500    ___DEFGH -0.1286  0.0364
## roe.pratt   0.1568    _BCD____  0.0320  0.2903
## 
## Letters indicate the ranks covered by bootstrap CIs. 
## (Rank bootstrap confidence intervals always obtained by percentile method) 
## CAUTION: Bootstrap confidence intervals can be somewhat liberal. 
## 
##  
##  Differences between Relative Contributions: 
##  
##                                Lower   Upper
##                difference 0.95 0.95    0.95   
## gdp-atc.lmg    -0.5421     *   -0.6372 -0.4142
## gdp-buy.lmg     0.0834     *    0.0211  0.1441
## gdp-sell.lmg    0.0839     *    0.0266  0.1458
## gdp-vol.lmg     0.0116         -0.0516  0.0778
## gdp-cpi.lmg     0.0844     *    0.0193  0.1472
## gdp-roa.lmg     0.0444         -0.0209  0.1112
## gdp-roe.lmg    -0.0136         -0.0900  0.0706
## atc-buy.lmg     0.6255     *    0.5272  0.6882
## atc-sell.lmg    0.6260     *    0.5305  0.6877
## atc-vol.lmg     0.5537     *    0.4599  0.6219
## atc-cpi.lmg     0.6265     *    0.5212  0.6918
## atc-roa.lmg     0.5865     *    0.4769  0.6626
## atc-roe.lmg     0.5285     *    0.4000  0.6187
## buy-sell.lmg    0.0005         -0.0195  0.0280
## buy-vol.lmg    -0.0718     *   -0.1036 -0.0364
## buy-cpi.lmg     0.0010         -0.0338  0.0322
## buy-roa.lmg    -0.0390     *   -0.0685 -0.0042
## buy-roe.lmg    -0.0970     *   -0.1481 -0.0434
## sell-vol.lmg   -0.0723     *   -0.1017 -0.0439
## sell-cpi.lmg    0.0005         -0.0337  0.0215
## sell-roa.lmg   -0.0395     *   -0.0691 -0.0124
## sell-roe.lmg   -0.0975     *   -0.1470 -0.0506
## vol-cpi.lmg     0.0728     *    0.0324  0.1039
## vol-roa.lmg     0.0328         -0.0042  0.0683
## vol-roe.lmg    -0.0252         -0.0827  0.0284
## cpi-roa.lmg    -0.0400     *   -0.0689 -0.0010
## cpi-roe.lmg    -0.0980     *   -0.1453 -0.0445
## roa-roe.lmg    -0.0580     *   -0.0893 -0.0243
##                                               
## gdp-atc.last   -0.7200     *   -0.8390 -0.5425
## gdp-buy.last    0.0415         -0.0327  0.1155
## gdp-sell.last   0.0313         -0.0374  0.1118
## gdp-vol.last    0.0196         -0.0553  0.0980
## gdp-cpi.last    0.0393         -0.0311  0.1078
## gdp-roa.last    0.0556         -0.0025  0.1284
## gdp-roe.last    0.0279         -0.0474  0.1139
## atc-buy.last    0.7616     *    0.6039  0.8556
## atc-sell.last   0.7514     *    0.6012  0.8531
## atc-vol.last    0.7397     *    0.5895  0.8381
## atc-cpi.last    0.7593     *    0.5942  0.8556
## atc-roa.last    0.7756     *    0.6250  0.8597
## atc-roe.last    0.7479     *    0.5786  0.8509
## buy-sell.last  -0.0102         -0.0513  0.0333
## buy-vol.last   -0.0219         -0.0852  0.0447
## buy-cpi.last   -0.0023         -0.0691  0.0593
## buy-roa.last    0.0140         -0.0278  0.0652
## buy-roe.last   -0.0136         -0.0784  0.0506
## sell-vol.last  -0.0117         -0.0850  0.0546
## sell-cpi.last   0.0079         -0.0580  0.0659
## sell-roa.last   0.0242         -0.0237  0.0717
## sell-roe.last  -0.0034         -0.0748  0.0581
## vol-cpi.last    0.0196         -0.0555  0.0834
## vol-roa.last    0.0359         -0.0154  0.0928
## vol-roe.last    0.0083         -0.0695  0.0782
## cpi-roa.last    0.0163         -0.0233  0.0764
## cpi-roe.last   -0.0114         -0.0693  0.0556
## roa-roe.last   -0.0277         -0.0654  0.0010
##                                               
## gdp-atc.first  -0.4137     *   -0.5093 -0.3101
## gdp-buy.first   0.1024     *    0.0409  0.1623
## gdp-sell.first  0.1087     *    0.0517  0.1671
## gdp-vol.first  -0.0092         -0.0866  0.0660
## gdp-cpi.first   0.1086     *    0.0468  0.1659
## gdp-roa.first   0.0198         -0.0625  0.1059
## gdp-roe.first  -0.0461         -0.1321  0.0477
## atc-buy.first   0.5161     *    0.4370  0.5871
## atc-sell.first  0.5224     *    0.4468  0.5940
## atc-vol.first   0.4044     *    0.3153  0.4929
## atc-cpi.first   0.5223     *    0.4473  0.5889
## atc-roa.first   0.4334     *    0.3271  0.5300
## atc-roe.first   0.3676     *    0.2598  0.4741
## buy-sell.first  0.0063         -0.0042  0.0260
## buy-vol.first  -0.1116     *   -0.1545 -0.0640
## buy-cpi.first   0.0062         -0.0121  0.0263
## buy-roa.first  -0.0827     *   -0.1280 -0.0347
## buy-roe.first  -0.1485     *   -0.1940 -0.0918
## sell-vol.first -0.1179     *   -0.1596 -0.0732
## sell-cpi.first -0.0001         -0.0150  0.0090
## sell-roa.first -0.0889     *   -0.1353 -0.0444
## sell-roe.first -0.1548     *   -0.1991 -0.1026
## vol-cpi.first   0.1178     *    0.0680  0.1587
## vol-roa.first   0.0290         -0.0484  0.0987
## vol-roe.first  -0.0368         -0.1153  0.0382
## cpi-roa.first  -0.0888     *   -0.1330 -0.0432
## cpi-roe.first  -0.1547     *   -0.1989 -0.0995
## roa-roe.first  -0.0658     *   -0.0979 -0.0301
##                                               
## gdp-atc.pratt  -0.7918     *   -0.9282 -0.6355
## gdp-buy.pratt   0.0763     *    0.0066  0.1471
## gdp-sell.pratt  0.0916     *    0.0300  0.1583
## gdp-vol.pratt   0.1808     *    0.0978  0.2663
## gdp-cpi.pratt   0.0872     *    0.0200  0.1554
## gdp-roa.pratt   0.1395     *    0.0371  0.2291
## gdp-roe.pratt  -0.0673         -0.2183  0.0741
## atc-buy.pratt   0.8680     *    0.7358  0.9731
## atc-sell.pratt  0.8833     *    0.7644  0.9830
## atc-vol.pratt   0.9725     *    0.8143  1.1052
## atc-cpi.pratt   0.8790     *    0.7478  0.9775
## atc-roa.pratt   0.9313     *    0.7895  1.0351
## atc-roe.pratt   0.7245     *    0.4998  0.9143
## buy-sell.pratt  0.0153         -0.0145  0.0624
## buy-vol.pratt   0.1045     *    0.0482  0.1619
## buy-cpi.pratt   0.0109         -0.0262  0.0496
## buy-roa.pratt   0.0632         -0.0232  0.1479
## buy-roe.pratt  -0.1435     *   -0.2752 -0.0102
## sell-vol.pratt  0.0892     *    0.0347  0.1410
## sell-cpi.pratt -0.0044         -0.0397  0.0184
## sell-roa.pratt  0.0479         -0.0393  0.1291
## sell-roe.pratt -0.1589     *   -0.2974 -0.0272
## vol-cpi.pratt  -0.0936     *   -0.1491 -0.0423
## vol-roa.pratt  -0.0413         -0.1496  0.0560
## vol-roe.pratt  -0.2480     *   -0.3780 -0.1168
## cpi-roa.pratt   0.0523         -0.0323  0.1339
## cpi-roe.pratt  -0.1545     *   -0.2829 -0.0255
## roa-roe.pratt  -0.2068         -0.4167  0.0052
## 
## * indicates that CI for difference does not include 0. 
## CAUTION: Bootstrap confidence intervals can be somewhat liberal.
plot(booteval.relimp(boot, sort=TRUE))