set.seed(34903490)
x = rnorm(50)
y = 0.5*x^2 + 2*x + rnorm(length(x))
frm = data.frame(
x = x,
y = y,
yC = y>=as.numeric(quantile(y,probs=0.8)),
stringsAsFactors = FALSE)
frm$absY <- abs(frm$y)
frm$posY = frm$y > 0
Scatterplots
Scatterplot with smoothing line through points.
WVPlots::ScatterHist(frm, "x", "y", title="Example Fit")
Scatterplot with best linear fit through points. Also report the
R-squared and significance of the linear fit.
WVPlots::ScatterHist(frm, "x", "y", smoothmethod="lm",
title="Example Linear Fit", estimate_sig = TRUE)
Scatterplot compared to the line x = y. Also report the
coefficient of determination between x and y
(where y is “true outcome” and x is “predicted
outcome”).
WVPlots::ScatterHist(frm, "x", "y", smoothmethod="identity",
title="Example Relation Plot", estimate_sig = TRUE)
Scatterplot of (x, y) color-coded by category/group, with
marginal distributions of x and y conditioned on
group.
set.seed(34903490)
fmScatterHistC = data.frame(
x=rnorm(50),
y=rnorm(50),
stringsAsFactors = FALSE)
fmScatterHistC$cat <- fmScatterHistC$x+fmScatterHistC$y>0
WVPlots::ScatterHistC(fmScatterHistC, "x", "y", "cat", title="Example Conditional Distribution")
Scatterplot of (x, y) color-coded by discretized z.
The continuous variable z is binned into three groups, and then
plotted as by ScatterHistC
set.seed(34903490)
frmScatterHistN = data.frame(
x=rnorm(50),
y=rnorm(50),
stringsAsFactors = FALSE)
frmScatterHistN$z <- frmScatterHistN$x+frmScatterHistN$y
WVPlots::ScatterHistN(frmScatterHistN, "x", "y", "z", title="Example Joint Distribution")
Plot the relationship y as a function of x with a
smoothing curve that estimates \(E[y |
x]\). If y is a 0/1 variable as below (binary
classification, where 1 is the target class), then the smoothing curve
estimates \(P(y | x)\). Since \(y \in \{0,1\}\) with \(y\) intended to be monotone in \(x\) is the most common use of this graph,
BinaryYScatterPlot uses a glm smoother by
default (use_glm=TRUE, this is essentially Platt scaling),
as the best estimate of \(P(y |
x)\).
WVPlots::BinaryYScatterPlot(frm, "x", "posY", use_glm=FALSE,
title="Example 'Probability of Y' Plot (ggplot2 smoothing)")
WVPlots::BinaryYScatterPlot(frm, "x", "posY", use_glm=TRUE,
title="Example 'Probability of Y' Plot (GLM smoothing)")
Hexbin Plot
if(requireNamespace("hexbin", quietly = TRUE)) {
set.seed(5353636)
df = rbind(data.frame(x=rnorm(1000, mean = 1),
y=rnorm(1000, mean = 1, sd = 0.5 ),
stringsAsFactors = FALSE),
data.frame(x = rnorm(1000, mean = -1, sd = 0.5),
y = rnorm(1000, mean = -1, sd = 0.5),
stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
print(WVPlots::HexBinPlot(df, "x", "y", "Two gaussians"))
}
Gain Curves
set.seed(34903490)
y = abs(rnorm(20)) + 0.1
x = abs(y + 0.5*rnorm(20))
frm = data.frame(
model=x,
value=y,
stringsAsFactors = FALSE)
frm$costs=1
frm$costs[1]=5
frm$rate = with(frm, value/costs)
frm$isValuable = (frm$value >= as.numeric(quantile(frm$value, probs=0.8)))
Basic curve: each item “costs” the same. The wizard sorts by true
value, the x axis sorts by the model, and plots the fraction of the
total population.
WVPlots::GainCurvePlot(frm, "model", "value", title="Example Continuous Gain Curve")
We can annotate a point of the model at a specific x value
gainx = 0.10 # get the top 10% most valuable points as sorted by the model
# make a function to calculate the label for the annotated point
labelfun = function(gx, gy) {
pctx = gx*100
pcty = gy*100
paste("The top ", pctx, "% most valuable points by the model\n",
"are ", pcty, "% of total actual value", sep='')
}
WVPlots::GainCurvePlotWithNotation(frm, "model", "value",
title="Example Gain Curve with annotation",
gainx=gainx,labelfun=labelfun)
When the x values have different costs, take that into
account in the gain curve. The wizard now sorts by value/cost, and the x
axis is sorted by the model, but plots the fraction of total cost,
rather than total count.
WVPlots::GainCurvePlotC(frm, "model", "costs", "value", title="Example Continuous Gain CurveC")
ROC Plots
set.seed(34903490)
# data with two different regimes of behavior
frm <- rbind(
data.frame(
model = rnorm(1000),
isValuable = sample(c(TRUE, FALSE), prob = c(0.02, 0.98), size = 1000, replace = TRUE)),
data.frame(
model = rnorm(200) + 5,
isValuable = sample(c(TRUE, FALSE), size = 200, replace = TRUE))
)
WVPlots::ROCPlot(frm, "model", "isValuable", TRUE, title="Example ROC plot")
Plotting the ROC of two models on the same data, where predictions
and true outcome all in the same data frame.
set.seed(34903490)
x1 = rnorm(50)
x2 = rnorm(length(x1))
y = 0.2*x2^2 + 0.5*x2 + x1 + rnorm(length(x1))
frmP = data.frame(
x1=x1,
x2=x2,
yC = y>=as.numeric(quantile(y,probs=0.8)),
stringsAsFactors = FALSE)
# WVPlots::ROCPlot(frmP, "x1", "yC", TRUE, title="Example ROC plot")
# WVPlots::ROCPlot(frmP, "x2", "yC", TRUE, title="Example ROC plot")
WVPlots::ROCPlotPair(frmP, "x1", "x2", "yC", TRUE, title="Example ROC pair plot")
Plotting the results from two data sets, for example the results of a
model on training and test sets, where predictions/outcome for the two
data sets are in different data frames.
set.seed(2342458)
make_data <- function(nrows) {
d <- data.frame(x = rnorm(nrows))
d['y'] = sin(d['x']) + 0.25*rnorm(n = nrows)
d['x2'] = rnorm(n = nrows)
d['yc'] = d[['y']]>0.5
return(d)
}
training <- make_data(500)
test <- make_data(200)
model <- glm(yc ~ x + x2, data=training, family=binomial)
training$pred <- predict(model, newdata=training, type="response")
test$pred <- predict(model, newdata=test, type="response")
WVPlots::ROCPlotPair2(nm1 = "Training", # model 1
frame1 = training,
xvar1 = "pred", truthVar1 = "yc", truthTarget1 = TRUE,
nm2 ="Test", # model 2
frame2 = test,
xvar2 = "pred", truthVar2 = "yc", truthTarget2 = TRUE,
title = "Model performance, training vs test",
estimate_sig = FALSE)