Plot Binary versus Metric

Basic plot to look at the dependence of a binary variable from a metric one. Scatterplot BINARY verus METRIC. BINARY is jittered to avoid overplotting. Blue line is logistic regression curve. Grey line is lowess smoother. Legend details Nvalid, Baserate and Wald pValue of METRIC in logistic regession.

PlotBinaryByMetric(BINARY, METRIC, BNAME = NULL, MNAME = NULL,
  MAIN = "", SCALE = "ID", XLIM = NULL, NPRETTY = 6, SPAN = 0.75,
  LOWESSCOL = 8, ...)

Arguments

BINARY

is a variable of any type with only two values
METRIC

is a metric variable
BNAME

= NULL is an optional label for BINARY
MNAME

= NULL is an optional label for M
MAIN

= "" is an optional title
SCALE

="ID" chooses a ScaleTransformation ID, POWER_Z, LOG10, LOGIT for X: use optional parameter Z to specify the power to use with POWER_Z. use optional parameter PERCENT = T for percent instead of proportions on LOGIT scale.
XLIM

= NULL sets the x-axis range to be plotted manually - generally not necessary
NPRETTY

= 6 influences approximate number of labels when using ID or POWER_Z (cf. base pretty)
SPAN

= 0.75 is the smoothing parameter for lowess
LOWESSCOL

= 8 Colour of Lowess-Smoother. Set to 0 to eliminate the curve

Value

Generates a plot and returns the summary of the logistic regression.

Examples

METRIC<-rnorm(200); BINARY<-(METRIC+rnorm(200))>0; PlotBinaryByMetric(BINARY,METRIC)
#> [1] "Ntotal =  200    Nvalid =  200"
#> 
#> Call:
#> stats::glm(formula = BINARY ~ Metric, family = "binomial")
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -2.2583  -0.8084   0.1805   0.8714   2.1444  
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  0.03087    0.17233   0.179    0.858    
#> Metric       1.61949    0.24049   6.734 1.65e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 277.18  on 199  degrees of freedom
#> Residual deviance: 202.81  on 198  degrees of freedom
#> AIC: 206.81
#> 
#> Number of Fisher Scoring iterations: 4
#>