Module 3: Bonus Exercise Results

data(BreastCancer)
bc <- BreastCancer
for (k in 2:10) # altered for current lab
    bc[,k] <- as.numeric(bc[,k]) 
head(bc)

##        Id Cl.thickness
## 1 1000025            5
## 2 1002945            5
## 3 1015425            3
## 4 1016277            6
## 5 1017023            4
## 6 1017122            8
##   Cell.size Cell.shape
## 1         1          1
## 2         4          4
## 3         1          1
## 4         8          8
## 5         1          1
## 6        10         10
##   Marg.adhesion Epith.c.size
## 1             1            2
## 2             5            7
## 3             1            2
## 4             1            3
## 5             3            2
## 6             8            7
##   Bare.nuclei Bl.cromatin
## 1           1           3
## 2          10           3
## 3           2           3
## 4           4           3
## 5           1           3
## 6          10           9
##   Normal.nucleoli Mitoses
## 1               1       1
## 2               2       1
## 3               1       1
## 4               7       1
## 5               1       1
## 6               7       1
##       Class
## 1    benign
## 2    benign
## 3    benign
## 4    benign
## 5    benign
## 6 malignant

Explore missingness:

suppressMessages(require(plotrix))

#' show data missingness as a chequered matrix
#' 
#' @param x (matrix) data matrix.
#' @param outFile (char) path to file for printing graph
#' @param wd (numeric) width in inches
#' @param ht (numeric) height in inches
#' @return plots missingness matrix to file
#' @import plotrix
#' @export
plotMissMat <- function(x,xlab="columns",
        ylab="rows",border=NA) {
    
    x <- !is.na(x)
    class(x) <- "numeric"
    color2D.matplot(x,show.values=FALSE,axes=FALSE,
        cs1=c(1,0),cs2=c(1,0),cs3=c(1,0),border=border,
        cex=0.8,
        xlab=xlab,ylab=ylab)
}

Explore missingness:

plotMissMat(bc)

colSums(is.na(bc))

##              Id 
##               0 
##    Cl.thickness 
##               0 
##       Cell.size 
##               0 
##      Cell.shape 
##               0 
##   Marg.adhesion 
##               0 
##    Epith.c.size 
##               0 
##     Bare.nuclei 
##              16 
##     Bl.cromatin 
##               0 
## Normal.nucleoli 
##               0 
##         Mitoses 
##               0 
##           Class 
##               0

Plot relationship between variables

require(ggplot2)

ggplot(bc,aes(Cell.size,Normal.nucleoli)) + geom_point()

ggplot(bc) + geom_boxplot(aes(factor(Class), Normal.nucleoli))

ggplot(bc) + geom_boxplot(aes(factor(Class), Cl.thickness))

ggplot(bc) + geom_boxplot(aes(factor(Class), Bare.nuclei))

## Warning: Removed 16 rows containing
## non-finite outside the scale
## range (`stat_boxplot()`).

Fit a binary outcome model:

mod <- glm(
    Class ~ Cl.thickness + Bare.nuclei + Normal.nucleoli + Mitoses + Bl.cromatin, 
    bc,
    family="binomial")
summary(mod)

## 
## Call:
## glm(formula = Class ~ Cl.thickness + Bare.nuclei + Normal.nucleoli + 
##     Mitoses + Bl.cromatin, family = "binomial", data = bc)
## 
## Coefficients:
##                 Estimate
## (Intercept)     -9.69635
## Cl.thickness     0.63413
## Bare.nuclei      0.50988
## Normal.nucleoli  0.35765
## Mitoses          0.52677
## Bl.cromatin      0.61398
##                 Std. Error
## (Intercept)        0.98763
## Cl.thickness       0.12267
## Bare.nuclei        0.08228
## Normal.nucleoli    0.10040
## Mitoses            0.27923
## Bl.cromatin        0.14225
##                 z value
## (Intercept)      -9.818
## Cl.thickness      5.169
## Bare.nuclei       6.197
## Normal.nucleoli   3.562
## Mitoses           1.887
## Bl.cromatin       4.316
##                 Pr(>|z|)    
## (Intercept)      < 2e-16 ***
## Cl.thickness    2.35e-07 ***
## Bare.nuclei     5.76e-10 ***
## Normal.nucleoli 0.000368 ***
## Mitoses         0.059223 .  
## Bl.cromatin     1.59e-05 ***
## ---
## Signif. codes:  
##   0 '***' 0.001 '**' 0.01
##   '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 884.35  on 682  degrees of freedom
## Residual deviance: 120.81  on 677  degrees of freedom
##   (16 observations deleted due to missingness)
## AIC: 132.81
## 
## Number of Fisher Scoring iterations: 8