predictpbreg: Passing-Bablok regression - predict method

In MethComp: Analysis of Method Comparison studies

Description

A predict method for the "PBreg" class object, that is a result of Passing-Bablok regression.

Usage

## S3 method for class 'PBreg'
predict(object, newdata = object$model$x, interval="none",
  level=0.95,...)

Arguments

object	an object of class "PBreg"
newdata	an optional vector of new values of x to make predictions for. If omitted, the fitted values will be used.
interval	type of interval calculation - either confidence or none. The former is the default.
level	tolerance/confidence level.
...	Not used.

Value

If interval is "confidence" this function returns a data frame with three columns: "fit", "lwr" and "upr" - similarly to predict.lm.

If interval is "none" a vector of predicted values is returned.

Author(s)

Michal J. Figurski [e-mail]

References

Passing, H. and Bablok, W. (1983), A New Biometrical Procedure for Testing the Equality of Measurements from Two Different Analytical Methods. Journal of Clinical Chemistry and Clinical Biochemistry, Vol 21, 709–720

See Also

PBreg, Deming.

Examples

  ## Model data frame generation
  a <- data.frame(x=seq(1, 30)+rnorm(mean=0, sd=1, n=30),
                  y=seq(1, 30)*rnorm(mean=1, sd=0.4, n=30))

  ## Call to PBreg
  x <- PBreg(a)
  print(x)
  predict(x, interval="none")

  ## Or the same using "Meth" object
  a <- Meth(a, y=1:2)
  x <- PBreg(a)
  print(x)
  predict(x)

Example output

Loading required package: nlme

Passing-Bablok linear regression of y on x 

Observations read: 30, used: 30
Slopes calculated: 435, offset: 33

            Estimate     2.5%CI  97.5%CI
Intercept -0.7648551 -4.0211245 1.321579
Slope      1.1522148  0.8948016 1.344128

Unadjusted summary of slopes:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-13.6495   0.4285   0.9618   2.1780   1.4244 176.9515 

Summary of residuals:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-16.0974  -3.1587   0.0000  -1.4283   0.8312   7.4040 

Test for linearity: (passed)
Linearity test not fully implemented in this version.

 [1]  0.6296903  2.4901197  3.3123183  2.9759993  4.7589338  7.4010486
 [7]  8.1025405  7.8871544  9.6070965  9.2550453 12.0695023 11.6774776
[13] 15.0483159 16.5405380 16.7699808 16.5922263 19.9191257 20.6246352
[19] 19.6021331 20.9361322 23.2036752 23.3651882 23.6881602 27.1719325
[25] 27.2342489 29.8737980 31.2176551 31.8047355 32.0275790 32.8931949
The following variables from the dataframe
"a" are used as the Meth variables:   
   y: x y 
       #Replicates
Method          1 #Items #Obs: 60 Values:  min      med      max
     x         30     30       30    1.2103172 15.34000 29.21161
     y         30     30       30    0.6366177 16.51171 31.42860

Passing-Bablok linear regression of y on x 

Observations read: 30, used: 30
Slopes calculated: 435, offset: 33

            Estimate     2.5%CI  97.5%CI
Intercept -0.7648551 -4.0211245 1.321579
Slope      1.1522148  0.8948016 1.344128

Unadjusted summary of slopes:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-13.6495   0.4285   0.9618   2.1780   1.4244 176.9515 

Summary of residuals:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-16.0974  -3.1587   0.0000  -1.4283   0.8312   7.4040 

Test for linearity: (passed)
Linearity test not fully implemented in this version.

          fit        lwr       upr
1   0.6296903 -2.3943031  2.404573
2   2.4901197 -0.2239998  3.849369
3   3.3123183  0.7351444  4.487882
4   2.9759993  0.3428080  4.226699
5   4.7589338  2.4520046  5.630816
6   7.4010486  5.6080065  7.733071
7   8.1025405  6.4500952  8.294336
8   7.8871544  6.1974729  8.125960
9   9.6070965  8.4589630  9.970829
10  9.2550453  7.9587972  9.539216
11 12.0695023 11.4650467 12.602386
12 11.6774776 10.9817445 12.169095
13 15.0483159 13.8543950 15.668331
14 16.5405380 14.9421205 17.145016
15 16.7699808 15.0981224 17.356803
16 16.5922263 14.9772642 17.192727
17 19.9191257 17.3846084 20.480006
18 20.6246352 17.9325019 21.231528
19 19.6021331 17.1384342 20.142340
20 20.9361322 18.1744082 21.563340
21 23.2036752 19.9353657 24.035028
22 23.3651882 20.0607955 24.212120
23 23.6881602 20.3116132 24.566246
24 27.1719325 23.0170854 28.568825
25 27.2342489 23.0654799 28.641521
26 29.8737980 25.1153346 31.720715
27 31.2176551 26.1589642 33.288405
28 31.8047355 26.6148866 33.973270
29 32.0275790 26.7879453 34.233230
30 32.8931949 27.4601762 35.243024

MethComp documentation built on May 2, 2019, 5:06 p.m.