The following sections show options to create the classic Shewhart control charts: X-bar charts, p-charts, and u-charts.
Calculate statistics that can be used to produce X-bar charts, p-charts, and u-charts. This includes producing means for center lines, 3-sigma upper and lower control limits. Users can also calculate values before and after an intervention to see if a change in the control process happened. Values are returned in a data frame.
Hospital LOS. View your output.
spc_x <- control(x="los", time="month", data=hosprog, type="x", n.equal=TRUE)
print(spc_x)
#> month los agr_sd agr_n c4 SD N MOC Mean
#> 1 1 4.575018 2.352310 51 0.9950128 1.937359 60 0.7540967 4.439575
#> 2 2 4.213063 1.795740 63 0.9959760 1.937359 60 0.7533674 4.439575
#> 3 3 4.998275 2.321623 49 0.9948056 1.937359 60 0.7542538 4.439575
#> 4 4 4.393394 1.853836 66 0.9961614 1.937359 60 0.7532272 4.439575
#> 5 5 4.174968 2.043365 65 0.9961015 1.937359 60 0.7532725 4.439575
#> 6 6 4.283090 1.683121 60 0.9957719 1.937359 60 0.7535219 4.439575
#> 7 7 4.295917 1.520956 50 0.9949113 1.937359 60 0.7541736 4.439575
#> 8 8 4.275860 1.620757 52 0.9951103 1.937359 60 0.7540228 4.439575
#> 9 9 4.418855 1.675656 65 0.9961015 1.937359 60 0.7532725 4.439575
#> 10 10 4.262903 1.702403 71 0.9964351 1.937359 60 0.7530203 4.439575
#> 11 11 4.488335 1.828521 67 0.9962194 1.937359 60 0.7531833 4.439575
#> 12 12 4.895228 2.850019 61 0.9958422 1.937359 60 0.7534687 4.439575
#> LCL UCL type
#> 1 3.685479 5.193672 x
#> 2 3.686208 5.192943 x
#> 3 3.685322 5.193829 x
#> 4 3.686348 5.192803 x
#> 5 3.686303 5.192848 x
#> 6 3.686054 5.193097 x
#> 7 3.685402 5.193749 x
#> 8 3.685553 5.193598 x
#> 9 3.686303 5.192848 x
#> 10 3.686555 5.192596 x
#> 11 3.686392 5.192759 x
#> 12 3.686107 5.193044 xBasic X-bar chart.
Hospital readmissions.
p-chart, using only the numerator (i.e., y=NULL). Specify unequal sample sizes.
p-chart, adding specification target and time point lines.
u-chart for infection rates with an intervention.
spc_u <- control(x="HAI", y="PatientDays", time="Month", data=infections,
type="u", n.equal=FALSE, intervention=22)u-chart with trend lines, various graphing options, x.axis start at 2nd year and y.axis changed to show HAIs per 1,000 patient days.
plot(spc_u, main="u-Chart: HAI per 1,000 Patient Days Pre/Post Intervention",
col=c("green","dodgerblue"), trend=TRUE, trcol="red", x.axis=c((1:41+12)), round.c=1,
y.axis=seq(min(spc_u$HAI)*1000, max(spc_u$HAI)*1000, length.out=nrow(spc_u)),
xlab="Months (starting at year 2)", icol="gray", lwd=2, cex=2,
cex.axis=1.1, cex.main=1.25, cex.text=1.25)
One thing that is helpful in ham’s chart options is the ordinary least squares trend line arrows in optional red above. It can show the trend line, especially helpful in out-of-control processes.