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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(SEQTaRget)
> 
> test_check("SEQTaRget")
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
SEQuential process completed in 3.09 seconds :
Initialized with:
Outcome covariates: outcome~tx_init_bas+followup+followup_sq+trial+trial_sq+sex+N_bas+L_bas+P_bas 
Numerator covariates: NA 
Denominator covariates: NA 

Full Model Information ========================================== 

Outcome Model ==================================================== 
Coefficients and Weighting:

Call:
fastglm.default(x = X, y = y, family = quasibinomial(), method = params@fastglm.method)

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -6.829e+00  5.668e-01 -12.047  < 2e-16 ***
tx_init_bas1  1.894e-01  3.005e-02   6.301 2.96e-10 ***
followup      3.372e-02  3.277e-03  10.289  < 2e-16 ***
followup_sq  -1.469e-04  6.466e-05  -2.272 0.023086 *  
trial         4.457e-02  1.125e-02   3.962 7.42e-05 ***
trial_sq      5.788e-04  1.002e-04   5.777 7.61e-09 ***
sex1          1.272e-01  2.596e-02   4.899 9.62e-07 ***
N_bas         3.291e-03  2.589e-03   1.271 0.203755    
L_bas        -1.339e-02  1.428e-02  -0.938 0.348353    
P_bas         2.007e-01  5.956e-02   3.370 0.000752 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.013259)

    Null deviance: 59179  on 248484  degrees of freedom
Residual deviance: 57178  on 248475  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 7


Diagnostic Tables ================================================== 
Unique Outcome Table: 

|tx_init_bas | outcome|   n|
|:-----------|-------:|---:|
|0           |       1| 203|
|1           |       1| 185|

Non-Unique Outcome Table: 

|tx_init_bas | outcome|    n|
|:-----------|-------:|----:|
|0           |       1| 1928|
|1           |       1| 4432|
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with dose-response analysis
dose-response model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with dose-response analysis
dose-response model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
SEQuential process completed in 3.75 seconds :
Initialized with:
Outcome covariates: outcome~tx_init_bas+followup+followup_sq+trial+trial_sq+sex+N_bas+L_bas+P_bas 
Numerator covariates: tx_init~sex+N_bas+L_bas+P_bas+followup+followup_sq+trial+trial_sq 
Denominator covariates: tx_init~sex+N+L+P+N_bas+L_bas+P_bas+followup+followup_sq+trial+trial_sq 

Full Model Information ========================================== 

Outcome Model ==================================================== 
Coefficients and Weighting:

Call:
fastglm.default(x = X, y = y, family = quasibinomial(), weights = weight, 
    method = params@fastglm.method)

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -9.172e+00  1.018e+00  -9.006  < 2e-16 ***
tx_init_bas1  4.708e-01  7.547e-02   6.238 4.46e-10 ***
followup      2.902e-02  5.933e-03   4.891 1.00e-06 ***
followup_sq   7.894e-05  1.503e-04   0.525 0.599521    
trial         6.700e-02  1.997e-02   3.356 0.000793 ***
trial_sq      5.834e-04  1.646e-04   3.545 0.000393 ***
sex1          8.163e-02  4.266e-02   1.913 0.055704 .  
N_bas         4.870e-03  4.304e-03   1.131 0.257877    
L_bas         1.350e-02  2.013e-02   0.671 0.502347    
P_bas         4.467e-01  1.067e-01   4.184 2.86e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.01542)

    Null deviance: 22205  on 96250  degrees of freedom
Residual deviance: 21177  on 96241  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 7


Weight Information ============================================= 
Treatment Lag = 0 Treatment = 0 Model ====================================
Denominator ========================== 

Call:
fastglm.default(x = X, y = y, family = quasibinomial(), method = params@fastglm.method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -2.1964004  0.8389945  -2.618  0.00886 **
sex1         0.1115039  0.0448359   2.487  0.01289 * 
N            0.0138112  0.0042418   3.256  0.00113 **
L            0.2018464  0.0939947   2.147  0.03177 * 
P            0.2493387  0.1746751   1.427  0.15347   
N_bas        0.0054225  0.0042524   1.275  0.20227   
L_bas       -0.1994854  0.1209594  -1.649  0.09913 . 
P_bas       -0.1918628  0.1796765  -1.068  0.28562   
followup     0.0031087  0.0291996   0.106  0.91522   
followup_sq  0.0006775  0.0004754   1.425  0.15418   
trial       -0.0036625  0.0165739  -0.221  0.82511   
trial_sq     0.0001652  0.0001616   1.022  0.30692   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.001142)

    Null deviance: 14601  on 16033  degrees of freedom
Residual deviance: 14565  on 16022  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 4

Treatment Lag = 1 Treatment = 1 Model ====================================
Denominator ========================== 

Call:
fastglm.default(x = X, y = y, family = quasibinomial(), method = params@fastglm.method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.624e+00  7.925e-01   3.311 0.000932 ***
sex1        -1.424e-02  3.376e-02  -0.422 0.673234    
N            3.487e-03  3.385e-03   1.030 0.302865    
L            1.719e-01  3.874e-02   4.437 9.15e-06 ***
P           -4.534e-01  1.121e-01  -4.043 5.27e-05 ***
N_bas        3.436e-03  3.378e-03   1.017 0.309153    
L_bas       -2.988e-02  5.997e-02  -0.498 0.618303    
P_bas        4.726e-01  1.182e-01   3.997 6.42e-05 ***
followup    -6.244e-02  1.491e-02  -4.188 2.82e-05 ***
followup_sq  1.511e-05  1.395e-04   0.108 0.913751    
trial        7.136e-03  1.557e-02   0.458 0.646638    
trial_sq    -2.722e-04  1.489e-04  -1.829 0.067448 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.004322)

    Null deviance: 30197  on 77511  degrees of freedom
Residual deviance: 30046  on 77500  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 6

Weights:
Min:  0.5060308 
Max:  3.101522 
StDev:  0.04174202 
P01:  0.8809239 
P25:  0.9894742 
P50:  1 
P75:  1.009571 
P99:  1.094614 


Diagnostic Tables ================================================== 
Unique Outcome Table: 

|tx_init_bas | outcome|   n|
|:-----------|-------:|---:|
|1           |       1| 153|
|0           |       1|  50|

Non-Unique Outcome Table: 

|tx_init_bas | outcome|    n|
|:-----------|-------:|----:|
|1           |       1| 2157|
|0           |       1|  222|

Unique Switch Table: 

|switch |     n|
|:------|-----:|
|FALSE  | 11127|
|TRUE   |  1053|

Non-Unique Switch Table: 

|switch |     n|
|:------|-----:|
|FALSE  | 96251|
|TRUE   |  6498|
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
Bootstrapping with 80 % of data 2 times
censoring model created successfully
Creating Survival curves
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Completed
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
SEQuential process completed in 2.27 seconds :
Initialized with:
Outcome covariates: outcome~tx_init_bas+followup+followup_sq+trial+trial_sq+sex+N_bas+L_bas+P_bas 
Numerator covariates: NA 
Denominator covariates: NA 

Full Model Information ========================================== 

Outcome Model ==================================================== 
Coefficients and Weighting:

Call:
fastglm.default(x = X, y = y, family = quasibinomial(), method = params@fastglm.method)

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -3.898e+01  3.409e+02  -0.114 0.908980    
tx_init_bas1 -2.504e+00  4.008e-01  -6.248 4.17e-10 ***
tx_init_bas2 -7.433e-01  2.649e-01  -2.806 0.005023 ** 
followup      7.977e-02  3.667e-02   2.175 0.029609 *  
followup_sq  -2.419e-03  8.922e-04  -2.711 0.006700 ** 
trial         3.946e-01  1.109e-01   3.559 0.000373 ***
trial_sq     -6.194e-03  1.298e-03  -4.773 1.82e-06 ***
sex1          1.696e+01  3.409e+02   0.050 0.960327    
N_bas         5.239e-02  2.434e-02   2.153 0.031352 *  
L_bas         8.217e-01  1.789e-01   4.593 4.37e-06 ***
P_bas         1.370e+00  5.746e-01   2.384 0.017109 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 0.4889533)

    Null deviance: 660.77  on 128156  degrees of freedom
Residual deviance: 562.54  on 128146  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 23


Diagnostic Tables ================================================== 
Unique Outcome Table: 

|tx_init_bas | outcome|  n|
|:-----------|-------:|--:|
|0           |       1|  1|
|2           |       1|  1|
|1           |       1|  1|

Non-Unique Outcome Table: 

|tx_init_bas | outcome|  n|
|:-----------|-------:|--:|
|0           |       1| 16|
|2           |       1| 16|
|1           |       1|  4|
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
SEQuential process completed in 1.93 seconds :
Initialized with:
Outcome covariates: outcome~tx_init_bas+followup+followup_sq+trial+trial_sq+sex 
Numerator covariates: tx_init~sex+time+time_sq 
Denominator covariates: tx_init~sex+N+L+P+time+time_sq 

Full Model Information ========================================== 

Outcome Model ==================================================== 
Coefficients and Weighting:

Call:
fastglm.default(x = X, y = y, family = quasibinomial(), weights = weight, 
    method = params@fastglm.method)

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -449.45481  583.13135  -0.771    0.441    
tx_init_bas1   18.07678  511.49001   0.035    0.972    
tx_init_bas2   -0.84364  602.46925  -0.001    0.999    
followup        0.77486    0.09922   7.809 5.89e-15 ***
followup_sq    -0.28416    0.02613 -10.875  < 2e-16 ***
trial          24.33871    1.54157  15.788  < 2e-16 ***
trial_sq       -0.36210    0.02296 -15.773  < 2e-16 ***
sex1           18.66948  278.84619   0.067    0.947    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 0.006579745)

    Null deviance: 82.063  on 42296  degrees of freedom
Residual deviance: 40.100  on 42289  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 27


Weight Information ============================================= 
Treatment Lag = 0 Treatment = 0 Model ====================================
Denominator ========================== 
Nested Model: Treatment = 1 =========

Call:
fastglm.default(x = X, y = ybin, family = family, method = method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.1573661  4.5273800  -1.360    0.174
sex1         0.2235736  0.2032074   1.100    0.271
N           -0.0133612  0.0210610  -0.634    0.526
L           -0.1302414  0.0920059  -1.416    0.157
P            0.3745089  0.4774276   0.784    0.433
time         0.1139056  0.0863576   1.319    0.187
time_sq     -0.0009750  0.0006659  -1.464    0.143

(Dispersion parameter for quasibinomial family taken to be 1.006216)

    Null deviance: 741.08  on 1182  degrees of freedom
Residual deviance: 728.00  on 1176  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 5

Nested Model: Treatment = 2 =========

Call:
fastglm.default(x = X, y = ybin, family = family, method = method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -3.0960751  4.7492673  -0.652   0.5146  
sex1         0.3534343  0.2109185   1.676   0.0941 .
N           -0.0102324  0.0219052  -0.467   0.6405  
L            0.0103966  0.0823995   0.126   0.8996  
P            0.0314063  0.5020658   0.063   0.9501  
time         0.0330558  0.0900849   0.367   0.7137  
time_sq     -0.0002855  0.0006856  -0.416   0.6772  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.004432)

    Null deviance: 699.62  on 1182  degrees of freedom
Residual deviance: 688.41  on 1176  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 5

Treatment Lag = 1 Treatment = 1 Model ====================================
Denominator ========================== 
Nested Model: Treatment = 1 =========

Call:
fastglm.default(x = X, y = ybin, family = family, method = method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  1.067e+00  5.211e+00   0.205    0.838
sex1         9.544e-02  1.834e-01   0.520    0.603
N            3.205e-03  1.820e-02   0.176    0.860
L           -9.328e-04  5.605e-02  -0.017    0.987
P            2.208e-01  5.528e-01   0.399    0.690
time         1.477e-02  9.823e-02   0.150    0.880
time_sq      9.222e-05  7.132e-04   0.129    0.897

(Dispersion parameter for quasibinomial family taken to be 1.002066)

    Null deviance: 1011.3  on 2455  degrees of freedom
Residual deviance: 1010.1  on 2449  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 5

Nested Model: Treatment = 2 =========

Call:
fastglm.default(x = X, y = ybin, family = family, method = method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  6.0684132  7.4996437   0.809    0.419
sex1        -0.1873988  0.2682454  -0.699    0.485
N           -0.0018731  0.0265776  -0.070    0.944
L           -0.0260750  0.0900972  -0.289    0.772
P           -1.0423959  0.7989424  -1.305    0.192
time        -0.1557571  0.1416047  -1.100    0.271
time_sq      0.0006375  0.0010422   0.612    0.541

(Dispersion parameter for quasibinomial family taken to be 1.003276)

    Null deviance: 556.56  on 2455  degrees of freedom
Residual deviance: 552.92  on 2449  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 6

Treatment Lag = 2 Treatment = 2 Model ====================================
Denominator ========================== 
Nested Model: Treatment = 1 =========

Call:
fastglm.default(x = X, y = ybin, family = family, method = method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -12.436421   7.385435  -1.684   0.0923 .
sex1          0.563480   0.268322   2.100   0.0358 *
N            -0.030240   0.026924  -1.123   0.2615  
L             0.241511   0.105948   2.280   0.0227 *
P             0.919744   0.778131   1.182   0.2373  
time          0.182061   0.141787   1.284   0.1993  
time_sq      -0.001910   0.001087  -1.758   0.0789 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 0.9638027)

    Null deviance: 535.95  on 2336  degrees of freedom
Residual deviance: 520.83  on 2330  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 7

Nested Model: Treatment = 2 =========

Call:
fastglm.default(x = X, y = ybin, family = family, method = method)

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept)  5.9274608  5.4667749   1.084  0.27836   
sex1        -0.5842318  0.1932441  -3.023  0.00253 **
N           -0.0036972  0.0195214  -0.189  0.84980   
L           -0.0252389  0.0673760  -0.375  0.70799   
P           -0.2937206  0.5798381  -0.507  0.61251   
time        -0.0450474  0.1037497  -0.434  0.66419   
time_sq      0.0002942  0.0007572   0.388  0.69771   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.004845)

    Null deviance: 928.74  on 2336  degrees of freedom
Residual deviance: 918.68  on 2330  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 6

Weights:
Min:  0.2478252 
Max:  6.309442 
StDev:  0.1998333 
P01:  0.757827 
P25:  0.97187 
P50:  0.9987108 
P75:  1.020873 
P99:  1.623825 


Diagnostic Tables ================================================== 
Unique Outcome Table: 

|tx_init_bas | outcome|  n|
|:-----------|-------:|--:|
|1           |       1|  1|

Non-Unique Outcome Table: 

|tx_init_bas | outcome|  n|
|:-----------|-------:|--:|
|1           |       1|  4|

Unique Switch Table: 

|switch |    n|
|:------|----:|
|FALSE  | 5515|
|TRUE   |  461|

Non-Unique Switch Table: 

|switch |     n|
|:------|-----:|
|FALSE  | 42297|
|TRUE   |  2713|
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with dose-response analysis
Bootstrapping with 80 % of data 2 times
dose-response model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with dose-response analysis
Bootstrapping with 80 % of data 2 times
dose-response model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Creating Survival curves
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
Bootstrapping with 80 % of data 2 times
ITT model created successfully
Creating Survival curves
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
Bootstrapping with 80 % of data 2 times
ITT model created successfully
Creating Survival curves
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with ITT analysis
ITT model created successfully
Completed
Non-required columns provided, pruning for efficiency
Pruned
Expanding Data...
Expansion Successful
Moving forward with censoring analysis
censoring model created successfully
Completed
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 92 ]
> 
> proc.time()
   user  system elapsed 
 176.12   34.31  206.71