By Chenyan Jia
Here is the R cheatsheet if you are interested in using R to do statistical analysis.
§ Install Packages
install.packages(“psych”)
library(psych)
install.packages(“ltm”)
library(ltm)
install.packages(“ppcor”)
library(ppcor)
install.packages(“lme4”)
library(lme4)
§ Functions
| Function | What it Calculates |
| Dataset | |
| setwd(<file path>) | Setting a working directory |
| getwd() | If you ever forget the path to your working directory, type getwd() |
| read.table() | Importing a data set.i.e. Duncan < – read.table(“Duncan.txt”, header=TRUE) |
| x < – c( , , …) | i.e. x <- c(1, 2, 3, 4, 5) |
| summary() | Summary |
| data() | If you have a data set, you can use built-in function |
| attach() | Avoid repeating the data name (to use a specific variable name) |
| objects() | Lists the names of variables and functions residing in R workspace |
| rm(list=ls()) | Remove everything in the environment |
| names() | Lists the variables in data |
| head()/tail() | Lists the first/last 6 data |
| str() | structure of data |
| dim() | dimension of data |
| sort(x) | The numbers in vector x in increasing order |
| rank(x) | Ranks of the numbers (in increasing order) in vector x |
| Univariate analysis | |
| sum() | Sum of the numbers in vector x. |
| mean(x) | Mean of the numbers in vector x. |
| median(x) | Median of the numbers in vector x |
| var(x) | Estimated variance of the population from which the numbers in vector x are sampled |
| sd(x) | Estimated standard deviation of the population from which the numbers in vector x are sampled |
| length(x) | Sample size of x |
| hist(x) | Histogram of x |
| Bivariate analysis | |
| cor(x,y) | Correlation coefficient between the numbers in vector x and the numbers in vector y |
| cov(x,y) | The covariance of the x and ycorrelation |
| cor.test(x,y) | Test for correlation between paired samplesi.e. cor.test(X, Y, alternative=”two.sided”, method=”spearman”) |
| plot(x,y) | Plot of x and y |
| For t-test | |
| qt() | i.e. Critical value (2-tailed) qt(1 – alpha/2, df=n-2) |
| pt() | i.e. p-value (2-tailed) ( 1 – pt(test_stat, df=n-2) ) * 2 |
| For z-test | |
| qnorm() | i.e. Critical value (two-tailed) qnorm(1-alpha/2) |
| pnorm() | i.e. p-value (two-tailed) pnorm(test_stat)*2 |
| fisherz() | Convert correlations to Fishers z’s |
| fisherz2r() | Convert Fishers z’s to r |
| For chi-squre | |
| qchisq() | i.e. Critical value (two-tailed) qchisq(1-alpha, df=1) |
| pchisq() | i.e. p-value ( 1 – pchisq(test_stat, df=1) ) |
| Three Variables | |
| Multiple Correlation (use the linear regression) | |
| lm(Y~X + Z) | mod <- lm(Y~X + Z) |
| Partial correlation between Y and X after controlling for the effect of Z | |
| pcor(cbind(Y, X, Z)) | Each cell gives pairwise partial correlations for each pair of variables given others. |
| pcor.test(Y, X, Z) | Significance testing |
| Semi-partial correlation between X and Y after controlling for Z | |
| ppcor::spcor(cbind(Y, X, Z)) | Gives the semi-partial correlation |
| ppcor::spcor.test(Y, X, Z) | Significance testing |
| Modeling | |
| Linear Regression Model | |
| lm(Y ~ X) | Linear regression analysis with the numbers in vector y as the dependent variable and the numbers in vector x as the independent variable. |
| anova() | Anova |
| Logistic Regression Model | |
| glm(Y ~ X, data = , family = binomial) | glm is used to fit generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution. |
| Hierarchical Linear Model | |
| lmer() | Fit a linear mixed-effects model (LMM) to data, via REML or maximum likelihood. |
| Principal Components Analysis | |
| prcomp() | Performs a principal components analysis on the given data matrix and returns the results as an object of class prcomp. |
Keep updating.
By Chenyan Jia
In this post, newscoding recommends several fake news or misinformation detection algorithms or datasets (especially misinformation related to COVID-19) that are used by researchers or Internet companies (*the following list is in no particular order of importance).
No. 1
Twitter: Updating our Approach to Misleading Information
In this article, Twitter introduces new labels and warning messages that will provide additional context and information on some Tweets containing disputed or misleading information related to COVID-19.
No.2
Triple Branch BERT Siamese Network for fake news classification on LIAR-PLUS dataset
A research paper published in the Proceedings of the First Workshop on Fact Extraction and VERification (FEVER) “Where is your Evidence: Improving Fact-checking by Justification Modeling” extended the LIAR dataset to the LIAR-PLUS dataset. The LIAR dataset was introduced by (Wang, 2017) and consists of 12,836 short statements taken from POLITIFACT and labeled by humans (Alhindi, Petridis, Muresan, 2018).
No.3
Metafact is a health fact-checking platform using a community of verified experts. The website has an intuitive interface and contains highly COVID-19 related content.
No.4
Neural Covidex applies state-of-the-art neural network models and AI techniques to answer questions using the COVID-19 Open Research Dataset (CORD-19) provided by the Allen Institute for AI (data release of May 26, 2020), which currently contains over 47,000 scholarly articles. In addition, Neural Covidex also supports search on randomized controlled trials related to COVID-19 provided by Trialstreamer.
No.5
Facebook: Using AI to detect COVID-19 misinformation and exploitative content
Facebook works with over 60 fact-checking organizations that review content in more than 50 languages in order to prevent the spread of misinformation during the COVID-19 pandemic.
No.6
COVID-19 related misinformation test sets
Researchers from the Center for Artificial Intelligence Research (CAiRE) posted COVID-19 related misinformation test sets newly proposed in their “Misinformation has High Perplexity” paper.
No.7
USC Melady Lab: Coronavirus on Social Media Misinformation Analysis
USC Melady Lab identifies unreliable, misleading and clickbait information shared on Twitter regarding COVID-19 from 2020-03-01 – 2020-05-03.
(keep updating)
References:
Alhindi, T, Petridis, S, & Muresan, S. (2018). Where is your Evidence: Improving Fact-checking by Justification Modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), Brussels, Belgium.
Wang, Y. W. (2017). Liar, liar pants on fire: A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, BC, Canada.
By Chenyan Jia
install.packages("mediation")
library(mediation)
<code>results <- mediate(model.mediator = mod3, model.y = mod2, treat='exercise', mediator='food', boot=TRUE, sims=500)</code>
results <- mediate(model.mediator = mod3, model.y = mod2, treat='exercise', mediator='food', boot=TRUE, sims=500) # "model.mediator": a fitted model object for mediator. # "model.y": a fitted model object for outcome (using both the focal and mediator variables) # "treat" a character string indicating the name of the treatment variable # "mediator": a character string indicating the name of the mediator variable # Typically bootstrap sample size ranges between 1000 ~ 5000. Remember, only use small simulations because our data are small. How to decipher the results? ## ACME: Average Causal Mediation Effects ## ADE: Average Direct Effects ## Total Effect: Sum of a mediation (indirect) effect and a direct effect ## Prop. Mediated: Size of the average causal mediation effects relative to the total effect. ## When ACME is significant and ADE is not significant, a complete mediation happens (Direct effects are not significant any more because of the mediator )
An Example of Results
By Chenyan Jia
If you are interested in using social network analysis to conduct research, you might want to explore this tool called EgoWeb 2.0 developed by David P. Kennedy.
Website Link: https://www.qualintitative.com/egoweb/
GitHub Link: https://github.com/qualintitative/egoweb
Install Instructions: https://www.qualintitative.com/wiki/doku.php/install
In order to use EgoWeb 2.0, the first step is to install AMPPS. Right now, EgoWeb 2.0 has upgraded its Mac version to 64-bit and works well in the latest Mac operating system. If you are Windows users, EgoWeb 2.0 functions well too.
Strengths
Downsides
