Format ChiSqTest from data.
# S3 method for data.frame wrapChiSqTest( x, predictionColumnName, yColumnName, ..., yTarget = TRUE, nParameters = 1, meany = mean(x[[yColumnName]] == yTarget), na.rm = FALSE )
| x | data frame containing columns to compare |
|---|---|
| predictionColumnName | character name of prediction column |
| yColumnName | character name of column containing dependent variable |
| ... | extra arguments (not used) |
| yTarget | y value to consider positive |
| nParameters | number of variables in model |
| meany | (optional) mean of y |
| na.rm | logical, if TRUE remove NA values |
wrapped test
d <- data.frame(x=c(1,2,3,4,5,6,7,7), y=c(TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE)) model <- glm(y~x, data=d, family=binomial) summary(model)#> #> Call: #> glm(formula = y ~ x, family = binomial, data = d) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -1.37180 -1.09714 -0.00811 1.08024 1.42939 #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -0.7455 1.6672 -0.447 0.655 #> x 0.1702 0.3429 0.496 0.620 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 11.090 on 7 degrees of freedom #> Residual deviance: 10.837 on 6 degrees of freedom #> AIC: 14.837 #> #> Number of Fisher Scoring iterations: 4 #>d$pred <- predict(model,type='response',newdata=d) render(wrapChiSqTest(d,'pred','y'),pLargeCutoff=1)#> [1] "Chi-Square Test summary: pseudo-R2=0.02282 (X2(1,N=8)=0.2531, p=0.6149)."