# [R] using ddply with segmented regression

arun smartpink111 at yahoo.com
Tue Oct 15 00:50:02 CEST 2013

```Hi Paul,

No problem.

Try:

par(mfrow=c(1,2))
ldply(SP.seg,plot)
#or
lapply(SP.seg,plot)

A.K.

On Monday, October 14, 2013 5:57 PM, "Prew, Paul" <Paul.Prew at ecolab.com> wrote:
Hello,  the code provided by arun did the trick.  Thank you very much, arun.

However, I'm now unsure of how to further process the results .  Looking at the vignette  aka "split-apply-combine". It appears that I could now create a dataframe from the list of results, and then run the results through the function plot.segmented to view the piecewise regressions by the grouping variable Lot.Run.  However, the list is not in the structure expected by ldply --

>SP.seg <- dlply((df,.(Lot.Run),segmentf_df)
>SP.out <- ldply(SP.seg)

[9] ERROR:
Results must be all atomic, or all data frames

>class(SP.seg)[[1]]
[1] "list"

\$`J062431-1`
Call: segmented.lm(obj = out.lm, seg.Z = ~Cycle, psi = (Cycle = NA),
control = seg.control(stop.if.error = FALSE, n.boot = 0,
gap = FALSE, jt = FALSE, nonParam = TRUE))

Meaningful coefficients of the linear terms:
(Intercept)        Cycle     U1.Cycle     U2.Cycle     U3.Cycle     U4.Cycle     U5.Cycle     U6.Cycle
40.11786     -0.06664     -0.68539      0.49316      0.14955      0.03612      0.22257     -0.41166
U7.Cycle     U8.Cycle     U9.Cycle    U10.Cycle
-0.48365      0.37949      0.24945      0.06712

Estimated Break-Point(s) psi1.Cycle psi2.Cycle psi3.Cycle psi4.Cycle psi5.Cycle psi6.Cycle psi7.Cycle psi8.Cycle psi9.Cycle psi10.Cycle :  19.67  34.31  51.02  72.10  97.94 117.20 130.10 147.10 155.70 160.40

\$`J062431-2`
Call: segmented.lm(obj = out.lm, seg.Z = ~Cycle, psi = (Cycle = NA),
control = seg.control(stop.if.error = FALSE, n.boot = 0,
gap = FALSE, jt = FALSE, nonParam = TRUE))

Meaningful coefficients of the linear terms:
(Intercept)        Cycle     U1.Cycle     U2.Cycle     U3.Cycle     U4.Cycle     U5.Cycle     U6.Cycle
40.11786     -0.06664     -0.68539      0.49316      0.14955      0.03612      0.22257     -0.41166
U7.Cycle     U8.Cycle     U9.Cycle    U10.Cycle
-0.48365      0.37949      0.24945      0.06712

Estimated Break-Point(s) psi1.Cycle psi2.Cycle psi3.Cycle psi4.Cycle psi5.Cycle psi6.Cycle psi7.Cycle psi8.Cycle psi9.Cycle psi10.Cycle :  19.67  34.31  51.02  72.10  97.94 117.20 130.10 147.10 155.70 160.40

My hope was to eventually increase my understanding enough to create lattice plots using 'segment.plot' via ldply.  Will that even work with the output object from this segmented package?

Thanks,Paul

Paul Prew  |  Statistician
651-795-5942   |   fax 651-204-7504
Ecolab Research Center  | Mail Stop ESC-F4412-A
655 Lone Oak Drive  |  Eagan, MN 55121-1560

-----Original Message-----
From: arun [mailto:smartpink111 at yahoo.com]
Sent: Saturday, October 12, 2013 1:42 AM
To: R help
Cc: Prew, Paul
Subject: Re: [R] using ddply with segmented regression

Hi,
Try:

segmentf_df <- function(df) {
out.lm<-lm(deltaWgt~Cycle, data=df)
segmented(out.lm,seg.Z=~Cycle, psi=(Cycle=NA),control=seg.control(stop.if.error=FALSE,n.boot=0))
}

library(plyr)
library(segmented)

dlply(df,.(Lot.Run),segmentf_df)
\$`J062431-1`
Call: segmented.lm(obj = out.lm, seg.Z = ~Cycle, psi = (Cycle = NA),
control = seg.control(stop.if.error = FALSE, n.boot = 0))

Meaningful coefficients of the linear terms:
(Intercept)        Cycle     U1.Cycle     U2.Cycle
38.480        1.130       -2.760        1.497

Estimated Break-Point(s) psi1.Cycle psi2.Cycle : 3.732 5.056

\$`J062431-2`
Call: segmented.lm(obj = out.lm, seg.Z = ~Cycle, psi = (Cycle = NA),
control = seg.control(stop.if.error = FALSE, n.boot = 0))

Meaningful coefficients of the linear terms:
(Intercept)        Cycle     U1.Cycle     U2.Cycle
48.4300      -3.2500       3.0905      -0.6555

Estimated Break-Point(s) psi1.Cycle psi2.Cycle :  2.12 22.15

attr(,"split_type")
[1] "data.frame"
attr(,"split_labels")
Lot.Run
1 J062431-1
2 J062431-2

#or

dlply(df,.(Lot.Run),function(x) segmentf_df(x))
#or
lapply(split(df,df\$Lot.Run,drop=TRUE),function(x) segmentf_df(x))

A.K.

On Friday, October 11, 2013 11:16 PM, "Prew, Paul" <Paul.Prew at ecolab.com> wrote:
Hello,

I’m unsuccessfully trying to apply piecewise linear regression over each of 22 groups.  The data structure of the reproducible toy dataset is below.  I’m using the ‘segmented’ package, it worked fine with a data set that containing only one group (“Lot.Run”).

\$ Cycle   : int  1 2 3 4 5 6 7 8 9 10 ...
\$ Lot.Run : Factor w/ 22 levels "J062431-1","J062431-2",..: 1 1 1 1 1 1 1 1 1 1 ...
\$ deltaWgt: num  38.7 42.6 41 42.3 40.6 ...

I am new to ‘segmented’, and also new to ‘plyr’, which is how I’m trying to apply this segmented regression to the 22 Lot.Run groups.  Within a Lot.Run, the piecewise linear regressions are deltaWgt vs. Cycle.

#####  define the linear regression #####
out.lm<-lm(deltaWgt~Cycle, data=Test50.df)

#####  define the function called by dlply  #####
#####  find cutpoints via bootstrapping, fit the piecewise regressions  #####
segmentf_df <- function(df) {
segmented(out.lm,seg.Z=~Cycle, psi=(Cycle=NA),control=seg.control(stop.if.error=FALSE,n.boot=0)), data = df)
}

at this point, there’s an  error message
23] ERROR: <text>

#####  repeat for each Lot.Run group   #####
dlply(Test50.df, .(Lot.Run), segmentf_df)

at this point, there’s an  error message
[28] ERROR:

Any suggestions?
Thanks, Paul

> dput(Test50.df)
structure(list(Cycle = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L), Lot.Run = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("J062431-1",
"J062431-2", "J062431-3", "J062432-1", "J062432-2", "J062433-1",
"J062433-2", "J062433-3", "Lot 1-1", "Lot 1-2", "Lot 2-1", "Lot 2-2",
"Lot 2-3", "Lot 3-1", "Lot 3-2", "Lot 3-3", "P041231-1", "P041231-2",
"P041531-1", "P041531-2", "P041531-3", "P041531-4"), class = "factor"),
deltaWgt = c(38.69, 42.58, 40.95, 42.26, 40.63, 41.61, 36.73,
41.28, 39.98, 40.63, 39.66, 39.98, 40.95, 38.36, 39.01, 39,
38.03, 39.66, 37.7, 39.66, 40.63, 38.03, 37.71, 36.73, 37.7,
45.18, 41.93, 42.59, 39.98, 40.95, 42.91, 38.03, 40.96, 39,
41.61, 39.33, 43.88, 39.98, 38.68, 38.68, 36.08, 39.99, 38.35,
40.31, 40.63, 38.68, 37.05, 38.36, 35.43, 36.73)), .Names = c("Cycle",
"Lot.Run", "deltaWgt"), row.names = c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 207L, 208L, 209L, 210L, 211L, 212L,
213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L,
224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L), class = "data.frame")

Paul Prew   ▪  Statistician
651-795-5942   ▪   fax 651-204-7504
Ecolab Research Center   ▪  Mail Stop ESC-F4412-A
655 Lone Oak Drive   ▪   Eagan, MN 55121-1560

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