Regression is a very popular statistical technique. In this post, I’ll show you how to plot regression effects using the R ‘effects’ library (https://cran.r-project.org/web/packages/effects/effects.pdf). This library provides a set of easy-to-write commands to visualize the effects of each variable in your regression model. The visualizations provide easy-to-understand plots that are very helpful when you interpret your model.

Here’s a simple R script for a logistic regression model plotted using the R effects library.

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logit1 <- glm(Hashtag ~ COMSTRS+COMCOP+ADVSS+BOUND+IDENT+GRPCOM+INFODIS, data = data, family = “binomial”)

summary(logit1)

library(effects)

plot(allEffects(logit1))

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Here’re the results:

Call:

glm(formula = Hashtag ~ COMSTRS + COMCOP + ADVSS + BOUND + IDENT +

GRPCOM + INFODIS, family = “binomial”, data = data)

Deviance Residuals:

Min 1Q Median 3Q Max

-1.5409 -1.3369 0.8532 1.0259 2.1899

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -2.0149 0.5323 -3.785 0.000153 ***

COMSTRS 2.3823 0.5386 4.423 9.73e-06 ***

COMCOP 2.8381 0.6027 4.709 2.49e-06 ***

ADVSS 2.6210 0.7355 3.564 0.000366 ***

BOUND 0.3499 0.6732 0.520 0.603244

IDENT -0.2877 0.7094 -0.406 0.685086

GRPCOM 1.0341 0.8612 1.201 0.229852

INFODIS 1.4351 0.5823 2.465 0.013716 *

—

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 1260.0 on 908 degrees of freedom

Residual deviance: 1132.8 on 901 degrees of freedom

AIC: 1148.8

Number of Fisher Scoring iterations: 4