MindMap Gallery Data Analysis - R code
This is a mind map talking about data analysis in R Code.
Edited at 2020-09-27 12:08:06Halloween has many faces. The theme you envision should influence how you decorate the party space. Jack-o'-lanterns and friendly ghosts are more lighthearted Halloween characters. Zombies, witches, and vampires are much darker. If you want to celebrate all the fun sides of Halloween, then it’s okay to mesh the cute with the frightening. Here is a mind map which lists down the 39 Cutest Couples Halloween Costumes of 2021.
Halloween simply wouldn't be Halloween without the movies that go along with it. There's nothing like a movie night filled with all the greatest chainsaw-wielding, spell-binding, hair-raising flicks to get you in the spooky season spirit. So, break out the stash of extra candy, turn off all the lights, lock every last door, and settle in for the best of the best Halloween movies. Here are the 35 Halloween movies listed on the mind map based on the year of release.
This mind map contains lots of interesting Halloween trivia, great tips for costumes and parties (including food, music, and drinks) and much more. It talks about the perfect Halloween night. Each step has been broken down into smaller steps to understand and plan better. Anybody can understand this Halloween mind map just by looking at it. It gives us full story of what is planned and how it is executed.
Halloween has many faces. The theme you envision should influence how you decorate the party space. Jack-o'-lanterns and friendly ghosts are more lighthearted Halloween characters. Zombies, witches, and vampires are much darker. If you want to celebrate all the fun sides of Halloween, then it’s okay to mesh the cute with the frightening. Here is a mind map which lists down the 39 Cutest Couples Halloween Costumes of 2021.
Halloween simply wouldn't be Halloween without the movies that go along with it. There's nothing like a movie night filled with all the greatest chainsaw-wielding, spell-binding, hair-raising flicks to get you in the spooky season spirit. So, break out the stash of extra candy, turn off all the lights, lock every last door, and settle in for the best of the best Halloween movies. Here are the 35 Halloween movies listed on the mind map based on the year of release.
This mind map contains lots of interesting Halloween trivia, great tips for costumes and parties (including food, music, and drinks) and much more. It talks about the perfect Halloween night. Each step has been broken down into smaller steps to understand and plan better. Anybody can understand this Halloween mind map just by looking at it. It gives us full story of what is planned and how it is executed.
Data Analysis(R code)
1. Population and Sampling Distributions
Distribution test
2. Bivariate Regression Analysis
Quantile-Normal Plot
car::qqPlot( )
car::scatterplot( )
shapiro.test( )
Box-Cox Transformation
car::powerTransform( )
car::bcPower( )
Gamma for negative values
Lambda for all transformation
symbox( )
Confidence interval
Regression Line
Points
3. Multiple Regression Analysis
car::scatterplotMatrix( )
Conditional Effect Plots
effects:: allEffects ( )
plot(Effect( ))
Beta Coefficients
coefplot ::coefplot( )
Stepwise Regression
MASS:: StepAIC( )
Factoer Analysis
scatterplot( y~x1 | x2 )
4. Instrumental Variable Regression
AER::ivreg( original model | instrumental variable + exogenous )
summary(ivreg, diagnostics=T)
Combination of IV estimation
Sargan test
n * R square, n means n observations
n <- nobs ( model )
1-pchisq( )
5. Regression Diagnostics
Multicollinearity
vif( )
Partial effects plots
avPlots(lm)
Residual plots (Tukey test)
residualPlots(lm)
student.resid
rstudent(lm)
DFBeta
dfbetas(lm)
Cook distance
cooks.distance(lm)
Leverage Plot
Leverage Plot
Then use boxplot
Diagnostics PlotBonferroni p-values
car::influenceIndexPlot(lm)
6. Spatial Autocorrelation & Heteroscedasticity
Heteroscedasticity
car::ncvTest(lmBase, var.formula=~log(pop), data=Bladder)
lmUpdated <- update(lmBase, weights=1/exp(predLogSigma2))
lmHetero( )
weighted.residuals( )
Spatial Autocorrelation
mapColorQual(prov.shp$REGION, prov.shp, map.title="Italy's Regions", legend.title="Region", add.to.map=T)
Link matrix
poly2nb(prov.shp, queen=F)
row-sum standardized neighbors
prov.linkW <- nb2listw(prov.link, style="W")
Test with W-coding scheme
lm.morantest(fert.wlm, prov.linkW)
moran.plot(weighted.residuals(fert.wlm),prov.linkW, labels=prov.shp$PROVNAME)
Spatial autoregressive model (SAR)
spautolm( )
7. Logistic Regression Analysis
Normal Logistic
glm(y~x,family=binomial(logit))
glm(y~x,family=binomial(probit))
confint(GLM.01, level=0.95, type="Wald",trace = FALSE)
Find the confidence interval of regression coefficients
Effects Plot
effects::plot(allEffects(GLM.03), type="response", ylim=c(0,1), ask=FALSE)
Low & High Prob
eff.GLM.low <- effect("lived",GLM.03, given.values=c(educ=20,"contamyes"=0,"hscyes"=0,"nodadyes"=1))
Residual Exploration
resid.GLM.03 <- residuals(GLM.03, type="response")
pred.GLM.03 <- predict(GLM.03, type="response")
8. The Generalized Linear Model
Overcome dispersion
Normal Possion
Likelihood Ratio Test
Logistic Regression
LR<- -2 *(logLik(log2) - logLik(log1))
anova(log2, log1, test = "LRT")