nlme: Interfaces for nlme package for data science pipelines.

Description Usage Arguments Details Value Author(s) Examples

Description

Interfaces to nlme functions that can be used in a pipeline implemented by magrittr.

Usage

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Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Details

Interfaces call their corresponding interfaced function.

Value

Object returned by interfaced function.

Author(s)

Roberto Bertolusso

Examples

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## Not run: 
library(intubate)
library(magrittr)
library(nlme)

## gls
## Original function to interface
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
           correlation = corAR1(form = ~ 1 | Mare))
summary(fm1)

## The interface reverses the order of data and formula
fm1 <- ntbt_gls(Ovary, follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
                correlation = corAR1(form = ~ 1 | Mare))
summary(fm1)

## so it can be used easily in a pipeline.
Ovary %>%
  ntbt_gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
                correlation = corAR1(form = ~ 1 | Mare))  %>%
  summary()
  
## nlme
## Original function to interface
lme(distance ~ age, data = Orthodont) # random is ~ age
lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)

## The interface reverses the order of data and formula
ntbt_lme(data = Orthodont, distance ~ age) # random is ~ age
ntbt_lme(data = Orthodont, distance ~ age + Sex, random = ~ 1)

## so it can be used easily in a pipeline.
Orthodont %>%
  ntbt_lme(distance ~ age) # random is ~ age
Orthodont %>%
  ntbt_lme(distance ~ age + Sex, random = ~ 1)

## lmList
## Original function to interface
lmList(distance ~ age | Subject, Orthodont)

## The interface reverses the order of data and formula
ntbt_lmList(Orthodont, distance ~ age | Subject)

## so it can be used easily in a pipeline.
Orthodont %>%
  ntbt_lmList(distance ~ age | Subject)
  
Orthodont %>%
  ntbt_lmList(distance ~ age | Subject) %>%
  summary()

## nlme
## Original function to interface
fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
            data = Loblolly,
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1,
            start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
summary(fm1)

## The interface reverses the order of data and formula
fm1 <- ntbt_nlme(data = Loblolly,
                 height ~ SSasymp(age, Asym, R0, lrc),
                 fixed = Asym + R0 + lrc ~ 1,
                 random = Asym ~ 1,
                 start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
summary(fm1)

## so it can be used easily in a pipeline.
Loblolly %>%
  ntbt_nlme(height ~ SSasymp(age, Asym, R0, lrc),
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1,
            start = c(Asym = 103, R0 = -8.5, lrc = -3.3)) %>%
  summary()

## End(Not run)

Example output

Generalized least squares fit by REML
  Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time) 
  Data: Ovary 
       AIC      BIC    logLik
  1571.455 1590.056 -780.7273

Correlation Structure: AR(1)
 Formula: ~1 | Mare 
 Parameter estimate(s):
      Phi 
0.7532079 

Coefficients:
                       Value Std.Error   t-value p-value
(Intercept)        12.216398 0.6646437 18.380373  0.0000
sin(2 * pi * Time) -2.774712 0.6450478 -4.301561  0.0000
cos(2 * pi * Time) -0.899605 0.6975383 -1.289685  0.1981

 Correlation: 
                   (Intr) s(*p*T
sin(2 * pi * Time)  0.000       
cos(2 * pi * Time) -0.294  0.000

Standardized residuals:
        Min          Q1         Med          Q3         Max 
-2.41180365 -0.75405234 -0.02923628  0.63156880  3.16247697 

Residual standard error: 4.616172 
Degrees of freedom: 308 total; 305 residual
Generalized least squares fit by REML
  Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time) 
  Data: NULL 
       AIC      BIC    logLik
  1571.455 1590.056 -780.7273

Correlation Structure: AR(1)
 Formula: ~1 | Mare 
 Parameter estimate(s):
      Phi 
0.7532079 

Coefficients:
                       Value Std.Error   t-value p-value
(Intercept)        12.216398 0.6646437 18.380373  0.0000
sin(2 * pi * Time) -2.774712 0.6450478 -4.301561  0.0000
cos(2 * pi * Time) -0.899605 0.6975383 -1.289685  0.1981

 Correlation: 
                   (Intr) s(*p*T
sin(2 * pi * Time)  0.000       
cos(2 * pi * Time) -0.294  0.000

Standardized residuals:
        Min          Q1         Med          Q3         Max 
-2.41180365 -0.75405234 -0.02923628  0.63156880  3.16247697 

Residual standard error: 4.616172 
Degrees of freedom: 308 total; 305 residual
Generalized least squares fit by REML
  Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time) 
  Data: NULL 
       AIC      BIC    logLik
  1571.455 1590.056 -780.7273

Correlation Structure: AR(1)
 Formula: ~1 | Mare 
 Parameter estimate(s):
      Phi 
0.7532079 

Coefficients:
                       Value Std.Error   t-value p-value
(Intercept)        12.216398 0.6646437 18.380373  0.0000
sin(2 * pi * Time) -2.774712 0.6450478 -4.301561  0.0000
cos(2 * pi * Time) -0.899605 0.6975383 -1.289685  0.1981

 Correlation: 
                   (Intr) s(*p*T
sin(2 * pi * Time)  0.000       
cos(2 * pi * Time) -0.294  0.000

Standardized residuals:
        Min          Q1         Med          Q3         Max 
-2.41180365 -0.75405234 -0.02923628  0.63156880  3.16247697 

Residual standard error: 4.616172 
Degrees of freedom: 308 total; 305 residual
Linear mixed-effects model fit by REML
  Data: Orthodont 
  Log-restricted-likelihood: -221.3183
  Fixed: distance ~ age 
(Intercept)         age 
 16.7611111   0.6601852 

Random effects:
 Formula: ~age | Subject
 Structure: General positive-definite
            StdDev    Corr  
(Intercept) 2.3270338 (Intr)
age         0.2264276 -0.609
Residual    1.3100399       

Number of Observations: 108
Number of Groups: 27 
Linear mixed-effects model fit by REML
  Data: Orthodont 
  Log-restricted-likelihood: -218.7563
  Fixed: distance ~ age + Sex 
(Intercept)         age   SexFemale 
 17.7067130   0.6601852  -2.3210227 

Random effects:
 Formula: ~1 | Subject
        (Intercept) Residual
StdDev:    1.807425 1.431592

Number of Observations: 108
Number of Groups: 27 
Linear mixed-effects model fit by REML
  Data: Orthodont 
  Log-restricted-likelihood: -221.3183
  Fixed: distance ~ age 
(Intercept)         age 
 16.7611111   0.6601852 

Random effects:
 Formula: ~age | Subject
 Structure: General positive-definite
            StdDev    Corr  
(Intercept) 2.3270338 (Intr)
age         0.2264276 -0.609
Residual    1.3100399       

Number of Observations: 108
Number of Groups: 27 
Linear mixed-effects model fit by REML
  Data: Orthodont 
  Log-restricted-likelihood: -218.7563
  Fixed: distance ~ age + Sex 
(Intercept)         age   SexFemale 
 17.7067130   0.6601852  -2.3210227 

Random effects:
 Formula: ~1 | Subject
        (Intercept) Residual
StdDev:    1.807425 1.431592

Number of Observations: 108
Number of Groups: 27 
Linear mixed-effects model fit by REML
  Data: . 
  Log-restricted-likelihood: -221.3183
  Fixed: distance ~ age 
(Intercept)         age 
 16.7611111   0.6601852 

Random effects:
 Formula: ~age | Subject
 Structure: General positive-definite
            StdDev    Corr  
(Intercept) 2.3270338 (Intr)
age         0.2264276 -0.609
Residual    1.3100399       

Number of Observations: 108
Number of Groups: 27 
Linear mixed-effects model fit by REML
  Data: . 
  Log-restricted-likelihood: -218.7563
  Fixed: distance ~ age + Sex 
(Intercept)         age   SexFemale 
 17.7067130   0.6601852  -2.3210227 

Random effects:
 Formula: ~1 | Subject
        (Intercept) Residual
StdDev:    1.807425 1.431592

Number of Observations: 108
Number of Groups: 27 
Call:
  Model: distance ~ age | Subject 
   Data: Orthodont 

Coefficients:
    (Intercept)   age
M16       16.95 0.550
M05       13.65 0.850
M02       14.85 0.775
M11       20.05 0.325
M07       14.95 0.800
M08       19.75 0.375
M03       16.00 0.750
M12       13.25 1.000
M13        2.80 1.950
M14       19.10 0.525
M09       14.40 0.975
M15       13.50 1.125
M06       18.95 0.675
M04       24.70 0.175
M01       17.30 0.950
M10       21.25 0.750
F10       13.55 0.450
F09       18.10 0.275
F06       17.00 0.375
F01       17.25 0.375
F05       19.60 0.275
F07       16.95 0.550
F02       14.20 0.800
F08       21.45 0.175
F03       14.40 0.850
F04       19.65 0.475
F11       18.95 0.675

Degrees of freedom: 108 total; 54 residual
Residual standard error: 1.31004
Call:
  Model: distance ~ age | Subject 
   Data: Orthodont 

Coefficients:
    (Intercept)   age
M16       16.95 0.550
M05       13.65 0.850
M02       14.85 0.775
M11       20.05 0.325
M07       14.95 0.800
M08       19.75 0.375
M03       16.00 0.750
M12       13.25 1.000
M13        2.80 1.950
M14       19.10 0.525
M09       14.40 0.975
M15       13.50 1.125
M06       18.95 0.675
M04       24.70 0.175
M01       17.30 0.950
M10       21.25 0.750
F10       13.55 0.450
F09       18.10 0.275
F06       17.00 0.375
F01       17.25 0.375
F05       19.60 0.275
F07       16.95 0.550
F02       14.20 0.800
F08       21.45 0.175
F03       14.40 0.850
F04       19.65 0.475
F11       18.95 0.675

Degrees of freedom: 108 total; 54 residual
Residual standard error: 1.31004
Call:
  Model: distance ~ age | Subject 
   Data: . 

Coefficients:
    (Intercept)   age
M16       16.95 0.550
M05       13.65 0.850
M02       14.85 0.775
M11       20.05 0.325
M07       14.95 0.800
M08       19.75 0.375
M03       16.00 0.750
M12       13.25 1.000
M13        2.80 1.950
M14       19.10 0.525
M09       14.40 0.975
M15       13.50 1.125
M06       18.95 0.675
M04       24.70 0.175
M01       17.30 0.950
M10       21.25 0.750
F10       13.55 0.450
F09       18.10 0.275
F06       17.00 0.375
F01       17.25 0.375
F05       19.60 0.275
F07       16.95 0.550
F02       14.20 0.800
F08       21.45 0.175
F03       14.40 0.850
F04       19.65 0.475
F11       18.95 0.675

Degrees of freedom: 108 total; 54 residual
Residual standard error: 1.31004
Call:
  Model: distance ~ age | Subject 
   Data: . 

Coefficients:
   (Intercept) 
    Estimate Std. Error   t value     Pr(>|t|)
M16    16.95   3.288173 5.1548379 3.695247e-06
M05    13.65   3.288173 4.1512411 1.181678e-04
M02    14.85   3.288173 4.5161854 3.458934e-05
M11    20.05   3.288173 6.0976106 1.188838e-07
M07    14.95   3.288173 4.5465974 3.116705e-05
M08    19.75   3.288173 6.0063745 1.665712e-07
M03    16.00   3.288173 4.8659237 1.028488e-05
M12    13.25   3.288173 4.0295930 1.762580e-04
M13     2.80   3.288173 0.8515366 3.982319e-01
M14    19.10   3.288173 5.8086964 3.449588e-07
M09    14.40   3.288173 4.3793313 5.509579e-05
M15    13.50   3.288173 4.1056231 1.373664e-04
M06    18.95   3.288173 5.7630783 4.078189e-07
M04    24.70   3.288173 7.5117696 6.081644e-10
M01    17.30   3.288173 5.2612799 2.523621e-06
M10    21.25   3.288173 6.4625549 3.065505e-08
F10    13.55   3.288173 4.1208291 1.306536e-04
F09    18.10   3.288173 5.5045761 1.047769e-06
F06    17.00   3.288173 5.1700439 3.499774e-06
F01    17.25   3.288173 5.2460739 2.665260e-06
F05    19.60   3.288173 5.9607565 1.971127e-07
F07    16.95   3.288173 5.1548379 3.695247e-06
F02    14.20   3.288173 4.3185072 6.763806e-05
F08    21.45   3.288173 6.5233789 2.443813e-08
F03    14.40   3.288173 4.3793313 5.509579e-05
F04    19.65   3.288173 5.9759625 1.863600e-07
F11    18.95   3.288173 5.7630783 4.078189e-07
   age 
    Estimate Std. Error   t value     Pr(>|t|)
M16    0.550  0.2929338 1.8775576 6.584707e-02
M05    0.850  0.2929338 2.9016799 5.361639e-03
M02    0.775  0.2929338 2.6456493 1.065760e-02
M11    0.325  0.2929338 1.1094659 2.721458e-01
M07    0.800  0.2929338 2.7309929 8.511442e-03
M08    0.375  0.2929338 1.2801529 2.059634e-01
M03    0.750  0.2929338 2.5603058 1.328807e-02
M12    1.000  0.2929338 3.4137411 1.222240e-03
M13    1.950  0.2929338 6.6567951 1.485652e-08
M14    0.525  0.2929338 1.7922141 7.870160e-02
M09    0.975  0.2929338 3.3283976 1.577941e-03
M15    1.125  0.2929338 3.8404587 3.247135e-04
M06    0.675  0.2929338 2.3042752 2.508117e-02
M04    0.175  0.2929338 0.5974047 5.527342e-01
M01    0.950  0.2929338 3.2430540 2.030113e-03
M10    0.750  0.2929338 2.5603058 1.328807e-02
F10    0.450  0.2929338 1.5361835 1.303325e-01
F09    0.275  0.2929338 0.9387788 3.520246e-01
F06    0.375  0.2929338 1.2801529 2.059634e-01
F01    0.375  0.2929338 1.2801529 2.059634e-01
F05    0.275  0.2929338 0.9387788 3.520246e-01
F07    0.550  0.2929338 1.8775576 6.584707e-02
F02    0.800  0.2929338 2.7309929 8.511442e-03
F08    0.175  0.2929338 0.5974047 5.527342e-01
F03    0.850  0.2929338 2.9016799 5.361639e-03
F04    0.475  0.2929338 1.6215270 1.107298e-01
F11    0.675  0.2929338 2.3042752 2.508117e-02

Residual standard error: 1.31004 on 54 degrees of freedom

Nonlinear mixed-effects model fit by maximum likelihood
  Model: height ~ SSasymp(age, Asym, R0, lrc) 
 Data: Loblolly 
       AIC      BIC    logLik
  239.4856 251.6397 -114.7428

Random effects:
 Formula: Asym ~ 1 | Seed
            Asym  Residual
StdDev: 3.650642 0.7188625

Fixed effects: Asym + R0 + lrc ~ 1 
         Value Std.Error DF   t-value p-value
Asym 101.44960 2.4616951 68  41.21128       0
R0    -8.62733 0.3179505 68 -27.13420       0
lrc   -3.23375 0.0342702 68 -94.36052       0
 Correlation: 
    Asym   R0    
R0   0.704       
lrc -0.908 -0.827

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-2.23601930 -0.62380854  0.05917466  0.65727206  1.95794425 

Number of Observations: 84
Number of Groups: 14 
Nonlinear mixed-effects model fit by maximum likelihood
  Model: height ~ SSasymp(age, Asym, R0, lrc) 
 Data: Loblolly 
       AIC      BIC    logLik
  239.4856 251.6397 -114.7428

Random effects:
 Formula: Asym ~ 1 | Seed
            Asym  Residual
StdDev: 3.650642 0.7188625

Fixed effects: Asym + R0 + lrc ~ 1 
         Value Std.Error DF   t-value p-value
Asym 101.44960 2.4616951 68  41.21128       0
R0    -8.62733 0.3179505 68 -27.13420       0
lrc   -3.23375 0.0342702 68 -94.36052       0
 Correlation: 
    Asym   R0    
R0   0.704       
lrc -0.908 -0.827

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-2.23601930 -0.62380854  0.05917466  0.65727206  1.95794425 

Number of Observations: 84
Number of Groups: 14 
Nonlinear mixed-effects model fit by maximum likelihood
  Model: height ~ SSasymp(age, Asym, R0, lrc) 
 Data: . 
       AIC      BIC    logLik
  239.4856 251.6397 -114.7428

Random effects:
 Formula: Asym ~ 1 | Seed
            Asym  Residual
StdDev: 3.650642 0.7188625

Fixed effects: Asym + R0 + lrc ~ 1 
         Value Std.Error DF   t-value p-value
Asym 101.44960 2.4616951 68  41.21128       0
R0    -8.62733 0.3179505 68 -27.13420       0
lrc   -3.23375 0.0342702 68 -94.36052       0
 Correlation: 
    Asym   R0    
R0   0.704       
lrc -0.908 -0.827

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-2.23601930 -0.62380854  0.05917466  0.65727206  1.95794425 

Number of Observations: 84
Number of Groups: 14 

intubate documentation built on May 2, 2019, 2:46 p.m.