Figure Short Caption
Figure 2.1 Histogram of difference in two ages, stratified by imputation type
Figure 2.2 Missing data patterns in d
Figure 2.3 Plot of the degree of symmetry of the distribution of a variable (value of 1.0 is most symmetric) vs. the number of distinct values of the variable. Hover over a point to see the variable name and detailed characteristics.
Figure 2.4 Categorical variable proportions for titanic5
Figure 2.5 Continuous variable spike histograms for titanic5
Figure 2.6 Dot plot of proportion of males by class
Figure 2.7 Extended box plots showing age and ticket price distributions by sex
Figure 2.8 Extended box plots of age and price by class
Figure 2.9 Scatter plot of ticket price vs. age
Figure 2.10 Moving proportion survived vs. price by class
Figure 2.11 Moving proportion survived vs. age by sex and class
Figure 2.12 Nonparametric smoother for survival vs. age by class and sex
Figure 6.1 Missing data patterns in support
Figure 6.1 Missing data patterns in support
Figure 8.1 Consort diagram produced with consort_plot
Figure 8.2 Consort diagram using component functions
Figure 8.3 Consort diagram produced by mermaid
Figure 8.4 Consort diagram produced with mermaid with individual exclusions linked to the overall exclusions node, and with a tooltip to show more detail
Figure 8.5 Flowchart of sequential exclusion of observations due to missing values
Figure 8.6 Plot of the degree of symmetry of the distribution of a variable (value of 1.0 is most symmetric) vs. the number of distinct values of the variable. Hover over a point to see the variable name and detailed characteristics.
Figure 8.1 Consort diagram produced with consort_plot
Figure 8.2 Consort diagram using component functions
Figure 8.3 Consort diagram produced by mermaid
Figure 8.4 Consort diagram produced with mermaid with individual exclusions linked to the overall exclusions node, and with a tooltip to show more detail
Figure 8.5 Flowchart of sequential exclusion of observations due to missing values
Figure 8.6 Plot of the degree of symmetry of the distribution of a variable (value of 1.0 is most symmetric) vs. the number of distinct values of the variable. Hover over a point to see the variable name and detailed characteristics.
Figure 9.1 Regular plot(describe) output
Figure 9.2 plotly plot(describe) output
Figure 9.3 summaryM plots
Figure 9.4 Spike histograms of heart rate stratified by ECG category
Figure 9.5 Representative curves determined by curveRep stratified by per-subject sample size ranges
?fig-descript-multevent Multi-event chart for 4 patients
Figure 9.8 State transition proportions
Figure 9.9 State occupancy proportions by time and male/female
Figure 9.10 Proportion of adverse events by Treatment
Figure 9.11 Event chart
Figure 9.12 Spearman rank correlation matrix. Positive correlations are blue and negative are red.
Figure 18.1 Simulation of maximum absolute correlation coefficient
Figure 14.1 plotly translation of a ggplot2 graph making use of variable labels from a data table that are translated to use within-string html font changes
Figure 14.2 ggfreqScatter example
Figure 14.3 plotly version of Figure 14.2
Figure 15.1 Nonparametric smooth estimated relationships between several continuous variables and probability of hospital death
Figure 15.2 Prototype extended box plot
Figure 15.3 bpplotM extended box plot
Figure 15.4 summaryM plotly graphic with interactive extended box plots
Figure 15.5 ggfreqAcatter plot showing all raw data for two continuous variables with only slight binning
Figure 15.6 movStats moving estimates of mean and quantiles
Figure 15.7 Moving mean and quantile estimates of effect of age with interquartile bands
Figure 15.8 Moving estimates of WBC vs. hospital mortality
Figure 15.9 Moving nonparametric and flexible parametric estimates of mortality
Figure 15.10 1-year mortality estimates stratified by disease group
Figure 15.11 Stratified moving Kaplan-Meier and HARE estimates varying age
Figure 15.12 Moving estimates of effect of age on glycohemoglobin stratified by race/ethnicity
Figure 15.13 Parametric spline estimates of age vs. glycohemoglobin
Figure 15.14 Smooth age effects on three quartiles of HbA$$_{1c}$$
Figure 15.15 Moving Kaplan-Meier and HARE esetimates for several continuous covariates
Figure 15.16 Kaplan-Meier estimates of 1y and 2y incidence stratified separately by a series of discrete predictorsw
Figure 15.17 Kaplan-Meier estimates of 1y and 2y incidence with each predictor in its own tab
Figure 15.18 Interactive survival curves with half-width confidence bands
Figure 15.19 Spearman correlations between pairwise clinical chemistry/hematology variable correlations and time
Figure 15.20 Graphical representations of a fitted binary logistic model