Last updated on 2018-04-29 01:47:11 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9-1 | 4.91 | 146.82 | 151.73 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.9-1 | 5.86 | 126.73 | 132.59 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.9-1 | 218.47 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.9-1 | 202.02 | OK | |||
| r-devel-windows-ix86+x86_64 | 0.9-1 | 14.00 | 228.00 | 242.00 | OK | |
| r-patched-linux-x86_64 | 0.9-1 | 5.09 | 152.68 | 157.77 | ERROR | |
| r-patched-solaris-x86 | 0.9-1 | 227.70 | OK | |||
| r-release-linux-x86_64 | 0.9-1 | 6.06 | 145.48 | 151.54 | ERROR | |
| r-release-windows-ix86+x86_64 | 0.9-1 | 14.00 | 228.00 | 242.00 | OK | |
| r-release-osx-x86_64 | 0.9-1 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 0.9-1 | 8.00 | 253.00 | 261.00 | OK | |
| r-oldrel-osx-x86_64 | 0.9-1 | OK |
Version: 0.9-1
Check: tests
Result: ERROR
Running ‘test-pmodel-test.R’ [21s/26s]
Running ‘test-pmodel.R’ [40s/53s]
Comparing ‘test-pmodel.Rout’ to ‘test-pmodel.Rout.save’ ...6,12d5
<
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
65,70c58,63
< Min. :6.083 Min. :-2.09411
< 1st Qu.:6.832 1st Qu.:-0.65812
< Median :7.097 Median : 0.08235
< Mean :7.061 Mean : 0.04625
< 3rd Qu.:7.277 3rd Qu.: 0.77876
< Max. :7.667 Max. : 2.34688
---
> Min. :6.083 Min. :-2.06955
> 1st Qu.:6.838 1st Qu.:-0.67986
> Median :7.097 Median :-0.02111
> Mean :7.044 Mean : 0.03952
> 3rd Qu.:7.269 3rd Qu.: 0.72868
> Max. :7.662 Max. : 2.27505
91,96c84,89
< Min. :1.833 Min. :-0.18204 Min. :0.1246 Min. :5.579
< 1st Qu.:1.971 1st Qu.: 0.02887 1st Qu.:0.1793 1st Qu.:6.627
< Median :1.995 Median : 0.09685 Median :0.2067 Median :6.839
< Mean :1.989 Mean : 0.08811 Mean :0.2104 Mean :6.772
< 3rd Qu.:2.018 3rd Qu.: 0.15046 3rd Qu.:0.2353 3rd Qu.:6.970
< Max. :2.064 Max. : 0.37519 Max. :0.3411 Max. :7.336
---
> Min. :1.837 Min. :-0.18402 Min. :0.1242 Min. :5.564
> 1st Qu.:1.973 1st Qu.: 0.03964 1st Qu.:0.1734 1st Qu.:6.649
> Median :1.997 Median : 0.09375 Median :0.2025 Median :6.852
> Mean :1.989 Mean : 0.09052 Mean :0.2074 Mean :6.783
> 3rd Qu.:2.017 3rd Qu.: 0.15010 3rd Qu.:0.2349 3rd Qu.:6.975
> Max. :2.058 Max. : 0.35433 Max. :0.3334 Max. :7.293
98,103c91,96
< Min. :5.556 Min. :-1.1437
< 1st Qu.:6.861 1st Qu.: 0.1988
< Median :7.544 Median : 0.6617
< Mean :7.430 Mean : 0.6580
< 3rd Qu.:7.993 3rd Qu.: 1.1185
< Max. :9.075 Max. : 2.6604
---
> Min. :5.619 Min. :-1.1664
> 1st Qu.:6.853 1st Qu.: 0.2761
> Median :7.501 Median : 0.6648
> Mean :7.459 Mean : 0.6755
> 3rd Qu.:7.984 3rd Qu.: 1.0902
> Max. :9.126 Max. : 2.5114
Running ‘test-pmtree.R’ [5s/8s]
Running the tests in ‘tests/test-pmtree.R’ failed.
Complete output:
> library("model4you")
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
Loading required package: rpart
> library("survival")
Attaching package: 'survival'
The following object is masked from 'package:rpart':
solder
>
> ### survreg
> set.seed(1)
> data(GBSG2, package = "TH.data")
>
> ## base model
> bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
> survreg_plot(bmod)
>
> ## partitioned model
> tr <- pmtree(bmod)
No data given. I'm using data set GBSG2 from the current environment parent.frame().
Please check if that is what you want.
> plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
+ confint = TRUE))
> summary(tr)
Stratified model for node(s) 2, 4, 5
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 2 node 4 node 5
(Intercept) 7.8590 7.2936 6.981
horThyes 0.4001 0.3039 0.163
Number of obervations:
node 2 node 4 node 5
376 223 87
Objective function:
(1093.18) + (1037.81) + (457.96) = 2588.95> summary(tr, node = 1:2)
Stratified model for node(s) 1, 2
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 1 node 2
(Intercept) 7.608 7.8590
horThyes 0.306 0.4001
Number of obervations:
node 1 node 2
686 376
Objective function:
node 1 node 2
2632.096 1093.182
>
> coef(tr)
(Intercept) horThyes
2 7.859026 0.4001011
4 7.293645 0.3038557
5 6.980875 0.1630104
> coef(tr, node = 1)
(Intercept) horThyes
1 7.608449 0.3059506
> coef(bmod)
(Intercept) horThyes
7.6084486 0.3059506
>
> logLik(bmod)
'log Lik.' -2632.096 (df=3)
> logLik(tr)
'log Lik.' -2588.953 (df=9)
>
>
> ### glm binomial
> set.seed(2)
> n <- 1000
> trt <- factor(rep(1:2, each = n/2))
> age <- sample(40:60, size = n, replace = TRUE)
> eff <- -1 + I(trt == 2) + 1 * I(trt == 2) * I(age > 50)
> expit <- function(x) 1/(1 + exp(-x))
>
> success <- rbinom(n = n, size = 1, prob = expit(eff))
>
> dat <- data.frame(success, trt, age)
> library("plyr")
> dattab <- ddply(.data = dat, .variables = .(trt, age),
+ function(x) data.frame(nsuccess = sum(x$success),
+ nfailure = NROW(x) - sum(x$success)))
>
> bmod1 <- glm(success ~ trt, family = binomial)
> bmod2 <- glm(success ~ trt, family = "binomial")
> bmod3 <- glm(success ~ trt, data = dat, family = binomial)
> bmod4 <- glm(cbind(nsuccess, nfailure) ~ trt, data = dattab, family = binomial)
>
> (tr1 <- pmtree(bmod1, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr2 <- pmtree(bmod2, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr3 <- pmtree(bmod3))
No data given. I'm using data set dat from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr4 <- pmtree(bmod4))
No data given. I'm using data set dattab from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 43: n = 8
| | (Intercept) trt2
| | -0.4367177 0.7420993
| | [4] age > 43: n = 14
| | (Intercept) trt2
| | -0.9253406 0.6662319
| [5] age > 50: n = 20
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 94.40128
>
> (mtr1 <- glmtree(success ~ trt | age, data = dat, family = binomial))
Generalized linear model tree (family: binomial)
Model formula:
success ~ trt | age
Fitted party:
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function (negative log-likelihood): 621.198
> # (mtr2 <- glmtree(cbind(nsuccess, nfailure) ~ trt | age, data = dattab, family = binomial))
>
> library("strucchange")
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> sctest(tr3)
Error in UseMethod("estfun") :
no applicable method for 'estfun' applied to an object of class "c('pmtree', 'constparty', 'party')"
Calls: sctest -> sctest.default -> gefp -> scores
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.9-1
Check: tests
Result: ERROR
Running ‘test-pmodel-test.R’ [17s/26s]
Running ‘test-pmodel.R’ [30s/48s]
Comparing ‘test-pmodel.Rout’ to ‘test-pmodel.Rout.save’ ...6,12d5
<
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
65,70c58,63
< Min. :6.083 Min. :-2.09411
< 1st Qu.:6.832 1st Qu.:-0.65812
< Median :7.097 Median : 0.08235
< Mean :7.061 Mean : 0.04625
< 3rd Qu.:7.277 3rd Qu.: 0.77876
< Max. :7.667 Max. : 2.34688
---
> Min. :6.083 Min. :-2.06955
> 1st Qu.:6.838 1st Qu.:-0.67986
> Median :7.097 Median :-0.02111
> Mean :7.044 Mean : 0.03952
> 3rd Qu.:7.269 3rd Qu.: 0.72868
> Max. :7.662 Max. : 2.27505
91,96c84,89
< Min. :1.833 Min. :-0.18204 Min. :0.1246 Min. :5.579
< 1st Qu.:1.971 1st Qu.: 0.02887 1st Qu.:0.1793 1st Qu.:6.627
< Median :1.995 Median : 0.09685 Median :0.2067 Median :6.839
< Mean :1.989 Mean : 0.08811 Mean :0.2104 Mean :6.772
< 3rd Qu.:2.018 3rd Qu.: 0.15046 3rd Qu.:0.2353 3rd Qu.:6.970
< Max. :2.064 Max. : 0.37519 Max. :0.3411 Max. :7.336
---
> Min. :1.837 Min. :-0.18402 Min. :0.1242 Min. :5.564
> 1st Qu.:1.973 1st Qu.: 0.03964 1st Qu.:0.1734 1st Qu.:6.649
> Median :1.997 Median : 0.09375 Median :0.2025 Median :6.852
> Mean :1.989 Mean : 0.09052 Mean :0.2074 Mean :6.783
> 3rd Qu.:2.017 3rd Qu.: 0.15010 3rd Qu.:0.2349 3rd Qu.:6.975
> Max. :2.058 Max. : 0.35433 Max. :0.3334 Max. :7.293
98,103c91,96
< Min. :5.556 Min. :-1.1437
< 1st Qu.:6.861 1st Qu.: 0.1988
< Median :7.544 Median : 0.6617
< Mean :7.430 Mean : 0.6580
< 3rd Qu.:7.993 3rd Qu.: 1.1185
< Max. :9.075 Max. : 2.6604
---
> Min. :5.619 Min. :-1.1664
> 1st Qu.:6.853 1st Qu.: 0.2761
> Median :7.501 Median : 0.6648
> Mean :7.459 Mean : 0.6755
> 3rd Qu.:7.984 3rd Qu.: 1.0902
> Max. :9.126 Max. : 2.5114
Running ‘test-pmtree.R’ [5s/7s]
Running the tests in ‘tests/test-pmtree.R’ failed.
Complete output:
> library("model4you")
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
Loading required package: rpart
> library("survival")
Attaching package: 'survival'
The following object is masked from 'package:rpart':
solder
>
> ### survreg
> set.seed(1)
> data(GBSG2, package = "TH.data")
>
> ## base model
> bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
> survreg_plot(bmod)
>
> ## partitioned model
> tr <- pmtree(bmod)
No data given. I'm using data set GBSG2 from the current environment parent.frame().
Please check if that is what you want.
> plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
+ confint = TRUE))
> summary(tr)
Stratified model for node(s) 2, 4, 5
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 2 node 4 node 5
(Intercept) 7.8590 7.2936 6.981
horThyes 0.4001 0.3039 0.163
Number of obervations:
node 2 node 4 node 5
376 223 87
Objective function:
(1093.18) + (1037.81) + (457.96) = 2588.95> summary(tr, node = 1:2)
Stratified model for node(s) 1, 2
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 1 node 2
(Intercept) 7.608 7.8590
horThyes 0.306 0.4001
Number of obervations:
node 1 node 2
686 376
Objective function:
node 1 node 2
2632.096 1093.182
>
> coef(tr)
(Intercept) horThyes
2 7.859026 0.4001011
4 7.293645 0.3038557
5 6.980875 0.1630104
> coef(tr, node = 1)
(Intercept) horThyes
1 7.608449 0.3059506
> coef(bmod)
(Intercept) horThyes
7.6084486 0.3059506
>
> logLik(bmod)
'log Lik.' -2632.096 (df=3)
> logLik(tr)
'log Lik.' -2588.953 (df=9)
>
>
> ### glm binomial
> set.seed(2)
> n <- 1000
> trt <- factor(rep(1:2, each = n/2))
> age <- sample(40:60, size = n, replace = TRUE)
> eff <- -1 + I(trt == 2) + 1 * I(trt == 2) * I(age > 50)
> expit <- function(x) 1/(1 + exp(-x))
>
> success <- rbinom(n = n, size = 1, prob = expit(eff))
>
> dat <- data.frame(success, trt, age)
> library("plyr")
> dattab <- ddply(.data = dat, .variables = .(trt, age),
+ function(x) data.frame(nsuccess = sum(x$success),
+ nfailure = NROW(x) - sum(x$success)))
>
> bmod1 <- glm(success ~ trt, family = binomial)
> bmod2 <- glm(success ~ trt, family = "binomial")
> bmod3 <- glm(success ~ trt, data = dat, family = binomial)
> bmod4 <- glm(cbind(nsuccess, nfailure) ~ trt, data = dattab, family = binomial)
>
> (tr1 <- pmtree(bmod1, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr2 <- pmtree(bmod2, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr3 <- pmtree(bmod3))
No data given. I'm using data set dat from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr4 <- pmtree(bmod4))
No data given. I'm using data set dattab from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 43: n = 8
| | (Intercept) trt2
| | -0.4367177 0.7420993
| | [4] age > 43: n = 14
| | (Intercept) trt2
| | -0.9253406 0.6662319
| [5] age > 50: n = 20
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 94.40128
>
> (mtr1 <- glmtree(success ~ trt | age, data = dat, family = binomial))
Generalized linear model tree (family: binomial)
Model formula:
success ~ trt | age
Fitted party:
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function (negative log-likelihood): 621.198
> # (mtr2 <- glmtree(cbind(nsuccess, nfailure) ~ trt | age, data = dattab, family = binomial))
>
> library("strucchange")
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> sctest(tr3)
Error in UseMethod("estfun") :
no applicable method for 'estfun' applied to an object of class "c('pmtree', 'constparty', 'party')"
Calls: sctest -> sctest.default -> gefp -> scores
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.9-1
Check: tests
Result: ERROR
Running ‘test-pmodel-test.R’ [22s/30s]
Running ‘test-pmodel.R’ [42s/55s]
Comparing ‘test-pmodel.Rout’ to ‘test-pmodel.Rout.save’ ...6,12d5
<
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
65,70c58,63
< Min. :6.083 Min. :-2.09411
< 1st Qu.:6.832 1st Qu.:-0.65812
< Median :7.097 Median : 0.08235
< Mean :7.061 Mean : 0.04625
< 3rd Qu.:7.277 3rd Qu.: 0.77876
< Max. :7.667 Max. : 2.34688
---
> Min. :6.083 Min. :-2.06955
> 1st Qu.:6.838 1st Qu.:-0.67986
> Median :7.097 Median :-0.02111
> Mean :7.044 Mean : 0.03952
> 3rd Qu.:7.269 3rd Qu.: 0.72868
> Max. :7.662 Max. : 2.27505
91,96c84,89
< Min. :1.833 Min. :-0.18204 Min. :0.1246 Min. :5.579
< 1st Qu.:1.971 1st Qu.: 0.02887 1st Qu.:0.1793 1st Qu.:6.627
< Median :1.995 Median : 0.09685 Median :0.2067 Median :6.839
< Mean :1.989 Mean : 0.08811 Mean :0.2104 Mean :6.772
< 3rd Qu.:2.018 3rd Qu.: 0.15046 3rd Qu.:0.2353 3rd Qu.:6.970
< Max. :2.064 Max. : 0.37519 Max. :0.3411 Max. :7.336
---
> Min. :1.837 Min. :-0.18402 Min. :0.1242 Min. :5.564
> 1st Qu.:1.973 1st Qu.: 0.03964 1st Qu.:0.1734 1st Qu.:6.649
> Median :1.997 Median : 0.09375 Median :0.2025 Median :6.852
> Mean :1.989 Mean : 0.09052 Mean :0.2074 Mean :6.783
> 3rd Qu.:2.017 3rd Qu.: 0.15010 3rd Qu.:0.2349 3rd Qu.:6.975
> Max. :2.058 Max. : 0.35433 Max. :0.3334 Max. :7.293
98,103c91,96
< Min. :5.556 Min. :-1.1437
< 1st Qu.:6.861 1st Qu.: 0.1988
< Median :7.544 Median : 0.6617
< Mean :7.430 Mean : 0.6580
< 3rd Qu.:7.993 3rd Qu.: 1.1185
< Max. :9.075 Max. : 2.6604
---
> Min. :5.619 Min. :-1.1664
> 1st Qu.:6.853 1st Qu.: 0.2761
> Median :7.501 Median : 0.6648
> Mean :7.459 Mean : 0.6755
> 3rd Qu.:7.984 3rd Qu.: 1.0902
> Max. :9.126 Max. : 2.5114
Running ‘test-pmtree.R’ [5s/7s]
Running the tests in ‘tests/test-pmtree.R’ failed.
Complete output:
> library("model4you")
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
Loading required package: rpart
> library("survival")
Attaching package: 'survival'
The following object is masked from 'package:rpart':
solder
>
> ### survreg
> set.seed(1)
> data(GBSG2, package = "TH.data")
>
> ## base model
> bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
> survreg_plot(bmod)
>
> ## partitioned model
> tr <- pmtree(bmod)
No data given. I'm using data set GBSG2 from the current environment parent.frame().
Please check if that is what you want.
> plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
+ confint = TRUE))
> summary(tr)
Stratified model for node(s) 2, 4, 5
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 2 node 4 node 5
(Intercept) 7.8590 7.2936 6.981
horThyes 0.4001 0.3039 0.163
Number of obervations:
node 2 node 4 node 5
376 223 87
Objective function:
(1093.18) + (1037.81) + (457.96) = 2588.95> summary(tr, node = 1:2)
Stratified model for node(s) 1, 2
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 1 node 2
(Intercept) 7.608 7.8590
horThyes 0.306 0.4001
Number of obervations:
node 1 node 2
686 376
Objective function:
node 1 node 2
2632.096 1093.182
>
> coef(tr)
(Intercept) horThyes
2 7.859026 0.4001011
4 7.293645 0.3038557
5 6.980875 0.1630104
> coef(tr, node = 1)
(Intercept) horThyes
1 7.608449 0.3059506
> coef(bmod)
(Intercept) horThyes
7.6084486 0.3059506
>
> logLik(bmod)
'log Lik.' -2632.096 (df=3)
> logLik(tr)
'log Lik.' -2588.953 (df=9)
>
>
> ### glm binomial
> set.seed(2)
> n <- 1000
> trt <- factor(rep(1:2, each = n/2))
> age <- sample(40:60, size = n, replace = TRUE)
> eff <- -1 + I(trt == 2) + 1 * I(trt == 2) * I(age > 50)
> expit <- function(x) 1/(1 + exp(-x))
>
> success <- rbinom(n = n, size = 1, prob = expit(eff))
>
> dat <- data.frame(success, trt, age)
> library("plyr")
> dattab <- ddply(.data = dat, .variables = .(trt, age),
+ function(x) data.frame(nsuccess = sum(x$success),
+ nfailure = NROW(x) - sum(x$success)))
>
> bmod1 <- glm(success ~ trt, family = binomial)
> bmod2 <- glm(success ~ trt, family = "binomial")
> bmod3 <- glm(success ~ trt, data = dat, family = binomial)
> bmod4 <- glm(cbind(nsuccess, nfailure) ~ trt, data = dattab, family = binomial)
>
> (tr1 <- pmtree(bmod1, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr2 <- pmtree(bmod2, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr3 <- pmtree(bmod3))
No data given. I'm using data set dat from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr4 <- pmtree(bmod4))
No data given. I'm using data set dattab from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 43: n = 8
| | (Intercept) trt2
| | -0.4367177 0.7420993
| | [4] age > 43: n = 14
| | (Intercept) trt2
| | -0.9253406 0.6662319
| [5] age > 50: n = 20
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 94.40128
>
> (mtr1 <- glmtree(success ~ trt | age, data = dat, family = binomial))
Generalized linear model tree (family: binomial)
Model formula:
success ~ trt | age
Fitted party:
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function (negative log-likelihood): 621.198
> # (mtr2 <- glmtree(cbind(nsuccess, nfailure) ~ trt | age, data = dattab, family = binomial))
>
> library("strucchange")
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> sctest(tr3)
Error in UseMethod("estfun") :
no applicable method for 'estfun' applied to an object of class "c('pmtree', 'constparty', 'party')"
Calls: sctest -> sctest.default -> gefp -> scores
Execution halted
Flavor: r-patched-linux-x86_64
Version: 0.9-1
Check: tests
Result: ERROR
Running ‘test-pmodel-test.R’ [21s/27s]
Running ‘test-pmodel.R’ [37s/49s]
Comparing ‘test-pmodel.Rout’ to ‘test-pmodel.Rout.save’ ...6,12d5
<
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
65,70c58,63
< Min. :6.083 Min. :-2.09411
< 1st Qu.:6.832 1st Qu.:-0.65812
< Median :7.097 Median : 0.08235
< Mean :7.061 Mean : 0.04625
< 3rd Qu.:7.277 3rd Qu.: 0.77876
< Max. :7.667 Max. : 2.34688
---
> Min. :6.083 Min. :-2.06955
> 1st Qu.:6.838 1st Qu.:-0.67986
> Median :7.097 Median :-0.02111
> Mean :7.044 Mean : 0.03952
> 3rd Qu.:7.269 3rd Qu.: 0.72868
> Max. :7.662 Max. : 2.27505
91,96c84,89
< Min. :1.833 Min. :-0.18204 Min. :0.1246 Min. :5.579
< 1st Qu.:1.971 1st Qu.: 0.02887 1st Qu.:0.1793 1st Qu.:6.627
< Median :1.995 Median : 0.09685 Median :0.2067 Median :6.839
< Mean :1.989 Mean : 0.08811 Mean :0.2104 Mean :6.772
< 3rd Qu.:2.018 3rd Qu.: 0.15046 3rd Qu.:0.2353 3rd Qu.:6.970
< Max. :2.064 Max. : 0.37519 Max. :0.3411 Max. :7.336
---
> Min. :1.837 Min. :-0.18402 Min. :0.1242 Min. :5.564
> 1st Qu.:1.973 1st Qu.: 0.03964 1st Qu.:0.1734 1st Qu.:6.649
> Median :1.997 Median : 0.09375 Median :0.2025 Median :6.852
> Mean :1.989 Mean : 0.09052 Mean :0.2074 Mean :6.783
> 3rd Qu.:2.017 3rd Qu.: 0.15010 3rd Qu.:0.2349 3rd Qu.:6.975
> Max. :2.058 Max. : 0.35433 Max. :0.3334 Max. :7.293
98,103c91,96
< Min. :5.556 Min. :-1.1437
< 1st Qu.:6.861 1st Qu.: 0.1988
< Median :7.544 Median : 0.6617
< Mean :7.430 Mean : 0.6580
< 3rd Qu.:7.993 3rd Qu.: 1.1185
< Max. :9.075 Max. : 2.6604
---
> Min. :5.619 Min. :-1.1664
> 1st Qu.:6.853 1st Qu.: 0.2761
> Median :7.501 Median : 0.6648
> Mean :7.459 Mean : 0.6755
> 3rd Qu.:7.984 3rd Qu.: 1.0902
> Max. :9.126 Max. : 2.5114
Running ‘test-pmtree.R’ [5s/7s]
Running the tests in ‘tests/test-pmtree.R’ failed.
Complete output:
> library("model4you")
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
Loading required package: rpart
> library("survival")
Attaching package: 'survival'
The following object is masked from 'package:rpart':
solder
>
> ### survreg
> set.seed(1)
> data(GBSG2, package = "TH.data")
>
> ## base model
> bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
> survreg_plot(bmod)
>
> ## partitioned model
> tr <- pmtree(bmod)
No data given. I'm using data set GBSG2 from the current environment parent.frame().
Please check if that is what you want.
> plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
+ confint = TRUE))
> summary(tr)
Stratified model for node(s) 2, 4, 5
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 2 node 4 node 5
(Intercept) 7.8590 7.2936 6.981
horThyes 0.4001 0.3039 0.163
Number of obervations:
node 2 node 4 node 5
376 223 87
Objective function:
(1093.18) + (1037.81) + (457.96) = 2588.95> summary(tr, node = 1:2)
Stratified model for node(s) 1, 2
Model call:
survreg(formula = Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
Coefficients:
node 1 node 2
(Intercept) 7.608 7.8590
horThyes 0.306 0.4001
Number of obervations:
node 1 node 2
686 376
Objective function:
node 1 node 2
2632.096 1093.182
>
> coef(tr)
(Intercept) horThyes
2 7.859026 0.4001011
4 7.293645 0.3038557
5 6.980875 0.1630104
> coef(tr, node = 1)
(Intercept) horThyes
1 7.608449 0.3059506
> coef(bmod)
(Intercept) horThyes
7.6084486 0.3059506
>
> logLik(bmod)
'log Lik.' -2632.096 (df=3)
> logLik(tr)
'log Lik.' -2588.953 (df=9)
>
>
> ### glm binomial
> set.seed(2)
> n <- 1000
> trt <- factor(rep(1:2, each = n/2))
> age <- sample(40:60, size = n, replace = TRUE)
> eff <- -1 + I(trt == 2) + 1 * I(trt == 2) * I(age > 50)
> expit <- function(x) 1/(1 + exp(-x))
>
> success <- rbinom(n = n, size = 1, prob = expit(eff))
>
> dat <- data.frame(success, trt, age)
> library("plyr")
> dattab <- ddply(.data = dat, .variables = .(trt, age),
+ function(x) data.frame(nsuccess = sum(x$success),
+ nfailure = NROW(x) - sum(x$success)))
>
> bmod1 <- glm(success ~ trt, family = binomial)
> bmod2 <- glm(success ~ trt, family = "binomial")
> bmod3 <- glm(success ~ trt, data = dat, family = binomial)
> bmod4 <- glm(cbind(nsuccess, nfailure) ~ trt, data = dattab, family = binomial)
>
> (tr1 <- pmtree(bmod1, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr2 <- pmtree(bmod2, data = dat))
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr3 <- pmtree(bmod3))
No data given. I'm using data set dat from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 621.198
> (tr4 <- pmtree(bmod4))
No data given. I'm using data set dattab from the current environment parent.frame().
Please check if that is what you want.
[1] root
| [2] age <= 50
| | [3] age <= 43: n = 8
| | (Intercept) trt2
| | -0.4367177 0.7420993
| | [4] age > 43: n = 14
| | (Intercept) trt2
| | -0.9253406 0.6662319
| [5] age > 50: n = 20
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 94.40128
>
> (mtr1 <- glmtree(success ~ trt | age, data = dat, family = binomial))
Generalized linear model tree (family: binomial)
Model formula:
success ~ trt | age
Fitted party:
[1] root
| [2] age <= 50
| | [3] age <= 42: n = 153
| | (Intercept) trt2
| | -0.1300531 0.5027284
| | [4] age > 42: n = 371
| | (Intercept) trt2
| | -0.9932518 0.7773634
| [5] age > 50: n = 476
| (Intercept) trt2
| -0.7958013 1.9056497
Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function (negative log-likelihood): 621.198
> # (mtr2 <- glmtree(cbind(nsuccess, nfailure) ~ trt | age, data = dattab, family = binomial))
>
> library("strucchange")
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> sctest(tr3)
Error in UseMethod("estfun") :
no applicable method for 'estfun' applied to an object of class "c('pmtree', 'constparty', 'party')"
Calls: sctest -> sctest.default -> gefp -> scores
Execution halted
Flavor: r-release-linux-x86_64