Last updated: 2020-05-25
Checks: 6 1
Knit directory: R-codes/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200515) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 4a2a649. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .RData
Ignored: .Rhistory
Ignored: .Rproj.user/
Unstaged changes:
Modified: analysis/regression.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/regression.Rmd) and HTML (docs/regression.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e1a5784 | KaranSShakya | 2020-05-19 | link on all pages |
| Rmd | 27ba057 | KaranSShakya | 2020-05-17 | update ggplots + data for it |
| html | 27ba057 | KaranSShakya | 2020-05-17 | update ggplots + data for it |
| Rmd | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
| html | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
| Rmd | 3ec85bd | KaranSShakya | 2020-05-16 | building html pages and links |
| html | 3ec85bd | KaranSShakya | 2020-05-16 | building html pages and links |
One of the first things to do before performing a regression is correlation. Correlation is between -1 to 1.
r <- galton_height %>%
summarize(r = cor(father, childHeight))
r r
1 0.2660385Library: stargazer, knitr, sandwitch, lmtest, AER, forecast, plm. These contain more than necessary packages required for panel regression. The first step is to make your data frame into a panel form.
panel <- pdata.frame(data, index=c("Country", "Year"))For fixed effects model (two-way - country and year)
fixed.effect <- plm(y~x1+I(x1^2)+x2+x3,
data=panel, model="within", effect = "twoway")For random effects model
random.effect <- plm(y~x1+I(x1^2)+x2+x3,
data=panel, model="random")To view these models without and with standard error.
stargazer(random.effect, type='text')
stargazer(coeftest(random.effect, vcovHC), type="text")Hausman test to verify if to use fixed effect or random effect. If p < 0.05, use fixed effects model.
phtest(fixed.effect, random.effect)First convert the dataset into time-series form. We can see this through the plot().
temp.ts <- ts(temp, start=1998, end=2017, frequency = 1)
plot(temp.ts)The basic steps for the ARIMA prediction are as follows. Do not know the detail, but this looks at historical data to predict future trends. It is one variable dependent, meaning interactions are harder to integrate.
arima.temp <- arima(temp.ts, order=c(3,1,1))
summary(arima.temp)
plot(forecast(arima.temp, 5))
forecast(arima.temp, 5)
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] HistData_0.8-6 forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.0.3 tibble_3.0.1
[9] ggplot2_3.3.0 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.13 haven_2.2.0 lattice_0.20-41
[5] colorspace_1.4-1 vctrs_0.3.0 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.1 rlang_0.4.6 later_1.0.0 pillar_1.4.4
[13] withr_2.2.0 glue_1.4.1 DBI_1.1.0 dbplyr_1.4.3
[17] modelr_0.1.7 readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.5 evaluate_0.14
[25] knitr_1.28 httpuv_1.5.2 fansi_0.4.1 broom_0.5.6
[29] Rcpp_1.0.4.6 promises_1.1.0 backports_1.1.6 scales_1.1.1
[33] jsonlite_1.6.1 fs_1.4.1 hms_0.5.3 digest_0.6.25
[37] stringi_1.4.6 grid_4.0.0 rprojroot_1.3-2 cli_2.0.2
[41] tools_4.0.0 magrittr_1.5 crayon_1.3.4 whisker_0.4
[45] pkgconfig_2.0.3 ellipsis_0.3.0 xml2_1.3.2 reprex_0.3.0
[49] lubridate_1.7.8 rstudioapi_0.11 assertthat_0.2.1 rmarkdown_2.1
[53] httr_1.4.1 R6_2.4.1 nlme_3.1-147 git2r_0.27.1
[57] compiler_4.0.0