Last updated on 2026-07-23 12:50:18 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.8 | 7.67 | 107.48 | 115.15 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.8 | 5.44 | 65.86 | 71.30 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.8 | 13.00 | 166.35 | 179.35 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.8 | 79.17 | OK | |||
| r-devel-windows-x86_64 | 1.0.8 | 9.00 | 124.00 | 133.00 | OK | |
| r-patched-linux-x86_64 | 1.0.8 | 6.87 | 101.21 | 108.08 | OK | |
| r-release-linux-x86_64 | 1.0.8 | 7.22 | 98.03 | 105.25 | OK | |
| r-release-macos-arm64 | 1.0.8 | 2.00 | 28.00 | 30.00 | OK | |
| r-release-macos-x86_64 | 1.0.8 | 5.00 | 105.00 | 110.00 | OK | |
| r-release-windows-x86_64 | 1.0.8 | 10.00 | 120.00 | 130.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.8 | OK | ||||
| r-oldrel-macos-x86_64 | 1.0.8 | 4.00 | 74.00 | 78.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.8 | 12.00 | 160.00 | 172.00 | OK |
Version: 1.0.8
Check: tests
Result: ERROR
Running ‘1_tests.R’ [7s/8s]
Running the tests in ‘tests/1_tests.R’ failed.
Complete output:
> # install the package and use this script to test the package
> library("APCI")
> # or: remotes::install_github("jiahui1902/APCI")
> test_data <- APCI::women9017
> test_data$acc <- as.factor(test_data$acc)
> test_data$pcc <- as.factor(test_data$pcc)
> test_data$educc <- as.factor(test_data$educc)
> test_data$educr <- as.factor(test_data$educr)
>
> # equal age and period interval
> APC_I <- APCI::apci(outcome = "inlfc",
+ age = "acc",
+ period = "pcc",
+ cohort = "ccc",
+ weight = "wt",
+ data = test_data,dev.test=FALSE,
+ print = TRUE,
+ family = "gaussian")
inlfc ~ acc * pcc
<environment: 0x55a851fe2c88>
Intercept:
estimate se p sig
1 0.723 0.016 0.000 ***
Age Effect:
age_group age_estimate age_se age_p age_sig
1 1 -0.023 0.047 0.621
2 2 0.040 0.051 0.429
3 3 0.105 0.036 0.004 **
4 4 0.073 0.038 0.054
5 5 0.077 0.041 0.063
6 6 0.097 0.043 0.024 *
7 7 0.014 0.042 0.734
8 8 -0.049 0.050 0.324
9 9 -0.334 0.056 0.000 ***
Period Effect:
period_group period_estimate period_se period_p period_sig
1 1 -0.037 0.041 0.364
2 2 0.082 0.036 0.021 *
3 3 -0.032 0.031 0.288
4 4 0.035 0.031 0.262
5 5 -0.041 0.037 0.266
6 6 -0.007 0.039 0.859
Cohort Deviation:
cohort_average_group cohort_average cohort_average_se cohort_average_t
1 1 0.040 0.146 0.276
2 2 -0.166 0.111 -1.499
3 3 -0.004 0.063 -0.060
4 4 0.136 0.058 2.358
5 5 -0.085 0.049 -1.735
6 6 -0.016 0.044 -0.363
7 7 0.056 0.035 1.621
8 8 0.024 0.033 0.728
9 9 -0.001 0.038 -0.019
10 10 0.016 0.044 0.362
11 11 -0.036 0.049 -0.726
12 12 -0.064 0.074 -0.862
13 13 0.006 0.080 0.071
14 14 0.045 0.135 0.332
cohort_average_p cohort_average_sig
1 0.783
2 0.134
3 0.952
4 0.019 *
5 0.083
6 0.717
7 0.105
8 0.467
9 0.985
10 0.717
11 0.468
12 0.389
13 0.943
14 0.740
Cohort Life Course Dynamics:
cohort_slope_group cohort_slope cohort_slope_se cohort_slope_t
1 1 NA NA NA
2 2 0.087 0.151 0.574
3 3 -0.005 0.117 -0.047
4 4 0.372 0.121 3.067
5 5 -0.207 0.098 -2.112
6 6 -0.084 0.102 -0.819
7 7 0.101 0.086 1.163
8 8 -0.102 0.084 -1.206
9 9 0.007 0.083 0.084
10 10 0.034 0.106 0.321
11 11 0.002 0.097 0.022
12 12 0.043 0.094 0.455
13 13 -0.096 0.104 -0.929
14 14 NA NA NA
cohort_slope_p cohort_slope_sig
1 NA <NA>
2 0.566
3 0.963
4 0.002 **
5 0.035 *
6 0.413
7 0.245
8 0.228
9 0.933
10 0.749
11 0.982
12 0.649
13 0.353
14 NA <NA>
> summary(APC_I)
Length Class Mode
model 34 svyglm list
dev_global 0 -none- NULL
intercept 4 -none- character
age_effect 45 -none- character
period_effect 30 -none- character
cohort_average 6 data.frame list
cohort_slope 6 data.frame list
int_matrix 5 data.frame list
cohort_index 54 -none- numeric
data 23 data.frame list
>
> APC_I$model$coefficients
(Intercept) acc1 acc2 acc3 acc4 acc5
0.722823721 -0.023136361 0.040013754 0.104541041 0.073303652 0.076670235
acc6 acc7 acc8 pcc1 pcc2 pcc3
0.097489397 0.014339427 -0.049435166 -0.037227987 0.082244257 -0.032465606
pcc4 pcc5 acc1:pcc1 acc2:pcc1 acc3:pcc1 acc4:pcc1
0.035360547 -0.041063735 0.107855136 0.134505814 0.013080152 0.038193073
acc5:pcc1 acc6:pcc1 acc7:pcc1 acc8:pcc1 acc1:pcc2 acc2:pcc2
0.155871138 -0.138548374 -0.123916766 -0.227203968 -0.102488703 -0.093827942
acc3:pcc2 acc4:pcc2 acc5:pcc2 acc6:pcc2 acc7:pcc2 acc8:pcc2
0.026067418 0.051046947 -0.061070811 -0.111212431 0.151910564 0.244367187
acc1:pcc3 acc2:pcc3 acc3:pcc3 acc4:pcc3 acc5:pcc3 acc6:pcc3
-0.046682319 0.168232251 0.011077909 -0.017826557 0.010408696 0.068786474
acc7:pcc3 acc8:pcc3 acc1:pcc4 acc2:pcc4 acc3:pcc4 acc4:pcc4
-0.156482189 0.094155962 -0.077249961 -0.048079158 -0.051491253 -0.065069323
acc5:pcc4 acc6:pcc4 acc7:pcc4 acc8:pcc4 acc1:pcc5 acc2:pcc5
-0.004133526 -0.029593441 0.007157184 -0.166500416 0.073878255 -0.098389755
acc3:pcc5 acc4:pcc5 acc5:pcc5 acc6:pcc5 acc7:pcc5 acc8:pcc5
0.017733402 0.059110660 -0.107119394 0.067861346 0.185069747 -0.053669159
> summary(APC_I$model)
Call:
svyglm(formula = temp6_formula, design = wtdata2, family = get(family))
Survey design:
survey::svydesign(id = ~1, strata = NULL, data = data2, weights = as.formula(paste0("~",
weight)))
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.722824 0.016027 45.101 < 2e-16 ***
acc1 -0.023136 0.046719 -0.495 0.62056
acc2 0.040014 0.050587 0.791 0.42915
acc3 0.104541 0.035939 2.909 0.00371 **
acc4 0.073304 0.037961 1.931 0.05378 .
acc5 0.076670 0.041160 1.863 0.06281 .
acc6 0.097489 0.043186 2.257 0.02421 *
acc7 0.014339 0.042147 0.340 0.73377
acc8 -0.049435 0.050100 -0.987 0.32403
pcc1 -0.037228 0.040961 -0.909 0.36365
pcc2 0.082244 0.035506 2.316 0.02075 *
pcc3 -0.032466 0.030517 -1.064 0.28767
pcc4 0.035361 0.031488 1.123 0.26173
pcc5 -0.041064 0.036866 -1.114 0.26563
acc1:pcc1 0.107855 0.088323 1.221 0.22234
acc2:pcc1 0.134506 0.094547 1.423 0.15517
acc3:pcc1 0.013080 0.085888 0.152 0.87899
acc4:pcc1 0.038193 0.086145 0.443 0.65761
acc5:pcc1 0.155871 0.079288 1.966 0.04960 *
acc6:pcc1 -0.138548 0.154952 -0.894 0.37148
acc7:pcc1 -0.123917 0.133879 -0.926 0.35490
acc8:pcc1 -0.227204 0.140806 -1.614 0.10695
acc1:pcc2 -0.102489 0.123326 -0.831 0.40616
acc2:pcc2 -0.093828 0.111254 -0.843 0.39924
acc3:pcc2 0.026067 0.066304 0.393 0.69430
acc4:pcc2 0.051047 0.069410 0.735 0.46226
acc5:pcc2 -0.061071 0.100118 -0.610 0.54202
acc6:pcc2 -0.111212 0.098794 -1.126 0.26058
acc7:pcc2 0.151911 0.057056 2.662 0.00789 **
acc8:pcc2 0.244367 0.059277 4.122 4.08e-05 ***
acc1:pcc3 -0.046682 0.086663 -0.539 0.59025
acc2:pcc3 0.168232 0.067884 2.478 0.01338 *
acc3:pcc3 0.011078 0.073146 0.151 0.87965
acc4:pcc3 -0.017827 0.072732 -0.245 0.80643
acc5:pcc3 0.010409 0.082186 0.127 0.89925
acc6:pcc3 0.068786 0.072383 0.950 0.34220
acc7:pcc3 -0.156482 0.100332 -1.560 0.11918
acc8:pcc3 0.094156 0.105536 0.892 0.37253
acc1:pcc4 -0.077250 0.092708 -0.833 0.40491
acc2:pcc4 -0.048079 0.091510 -0.525 0.59943
acc3:pcc4 -0.051491 0.070961 -0.726 0.46825
acc4:pcc4 -0.065069 0.078646 -0.827 0.40824
acc5:pcc4 -0.004134 0.065733 -0.063 0.94987
acc6:pcc4 -0.029593 0.087274 -0.339 0.73462
acc7:pcc4 0.007157 0.089854 0.080 0.93653
acc8:pcc4 -0.166500 0.120332 -1.384 0.16679
acc1:pcc5 0.073878 0.090681 0.815 0.41545
acc2:pcc5 -0.098390 0.164649 -0.598 0.55027
acc3:pcc5 0.017733 0.080237 0.221 0.82513
acc4:pcc5 0.059111 0.081654 0.724 0.46930
acc5:pcc5 -0.107119 0.108338 -0.989 0.32304
acc6:pcc5 0.067861 0.071882 0.944 0.34538
acc7:pcc5 0.185070 0.066662 2.776 0.00561 **
acc8:pcc5 -0.053669 0.125209 -0.429 0.66829
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.1675259)
Number of Fisher Scoring iterations: 2
> APC_I$dev_global
NULL
> APC_I$dev_local
NULL
> APC_I$intercept
estimate se p
"0.722823721264995" "0.0160266588455221" "9.5996005190467e-238"
sig
"***"
> APC_I$age_effect
age_group age_estimate age_se
[1,] "1" "-0.023136361077591" "0.0467189865700206"
[2,] "2" "0.0400137538851534" "0.0505869371699012"
[3,] "3" "0.104541040705761" "0.035939018607914"
[4,] "4" "0.0733036516665031" "0.0379605595668322"
[5,] "5" "0.0766702345597859" "0.0411600045877691"
[6,] "6" "0.0974893973496912" "0.0431859489277772"
[7,] "7" "0.0143394267266666" "0.042147470605983"
[8,] "8" "-0.0494351655374788" "0.0501000476452751"
[9,] "9" "-0.333785978278491" "0.0563343401094491"
age_p sig
[1,] "0.620557295976925" " "
[2,] "0.429148593343231" " "
[3,] "0.00371273596349115" "** "
[4,] "0.0537762981351436" " "
[5,] "0.0628098179708096" " "
[6,] "0.0242088241676351" "* "
[7,] "0.733766292492727" " "
[8,] "0.324028386806769" " "
[9,] "4.37020931955974e-09" "***"
> APC_I$period_effect
period_group period_estimate period_se
[1,] "1" "-0.037227987494298" "0.0409606310911546"
[2,] "2" "0.0822442574049102" "0.0355057334748146"
[3,] "3" "-0.0324656058696508" "0.0305174614114505"
[4,] "4" "0.0353605473842638" "0.0314877519936346"
[5,] "5" "-0.0410637348638453" "0.0368664711145681"
[6,] "6" "-0.00684747656137997" "0.0385389768221606"
period_p sig
[1,] "0.363649454235801" " "
[2,] "0.0207516886707821" "* "
[3,] "0.28767457461471" " "
[4,] "0.261725687538456" " "
[5,] "0.265626875798358" " "
[6,] "0.859015035996362" " "
> APC_I$cohort_average
cohort_group cohort_average cohort_average_se cohort_average_t
1 1 0.0401637946523128 0.145654410499202 0.275747191689282
2 2 -0.165998098269934 0.110732007704118 -1.49909770184508
3 3 -0.00373993589994506 0.0627359414591251 -0.0596139280444492
4 4 0.135619511341992 0.0575231564574465 2.35765072179789
5 5 -0.0845597999766206 0.0487337724678583 -1.73513757902471
6 6 -0.015798227932657 0.0435517907715651 -0.362745771247865
7 7 0.0564770823462887 0.0348308485769292 1.62146730997812
8 8 0.0237893260546683 0.0326982034566164 0.72754229712442
9 9 -0.000729531341517646 0.0377506459702066 -0.0193250028646769
10 10 0.0158813704942078 0.0438719972507362 0.361993332636373
11 11 -0.0356207185155389 0.0490354608253836 -0.726427730380369
12 12 -0.0640357816985939 0.0742918076889194 -0.861949435484592
13 13 0.00571852247977949 0.0800513176721246 0.0714357070698262
14 14 0.0446875910691726 0.134689398365975 0.331782542733976
cohort_average_p sig
1 0.782802468508043
2 0.134181910088998
3 0.952475715770102
4 0.0185937732339823 *
5 0.0830421104761112
6 0.716875833812225
7 0.105250678987751
8 0.467073832032295
9 0.984585913291567
10 0.717437856984917
11 0.467756293668003
12 0.388933779223363
13 0.943066091087778
14 0.740126957575424
> APC_I$cohort_slope
cohort_group cohort_slope cohort_slope_se cohort_slope_t
1 1 <NA> <NA> <NA>
2 2 0.0865581708070433 0.150720313851626 0.574296646517429
3 3 -0.00548252587219852 0.11730697307554 -0.0467365726730332
4 4 0.37180671996158 0.121228187024816 3.06699893058262
5 5 -0.207439235923212 0.0982093910700732 -2.11221384903204
6 6 -0.0838238142879899 0.102317500605001 -0.819251973438968
7 7 0.100508860836206 0.0864003588542193 1.16329216879518
8 8 -0.101851041311175 0.0844353569025986 -1.20626056485634
9 9 0.00696039732231724 0.0832372302074845 0.0836212029757253
10 10 0.0341347660583372 0.106458987559418 0.320637710736122
11 11 0.0021231724752769 0.0966078090729115 0.0219772345077664
12 12 0.0429795991327735 0.0944515389019701 0.455043926572561
13 13 -0.0963924180967428 0.10377624257321 -0.928848604522776
14 14 <NA> <NA> <NA>
cohort_slope_p sig
1 <NA> <NA>
2 0.565903606134681
3 0.962733045596044
4 0.00222368013426539 **
5 0.0349296876945179 *
6 0.412849008697304
7 0.245004319811083
8 0.228018620420455
9 0.933375299565241
10 0.748555769858807
11 0.982470749951475
12 0.649182047032306
13 0.353204490100309
14 <NA> <NA>
> APC_I$cohort_index
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 9 10 11 12 13 14
[2,] 8 9 10 11 12 13
[3,] 7 8 9 10 11 12
[4,] 6 7 8 9 10 11
[5,] 5 6 7 8 9 10
[6,] 4 5 6 7 8 9
[7,] 3 4 5 6 7 8
[8,] 2 3 4 5 6 7
[9,] 1 2 3 4 5 6
>
>
> apci.plot.raw(data = test_data, outcome_var = "inlfc",age="acc",period="pcc")
Ignoring unknown labels:
• names : "Age"
Ignoring unknown labels:
• names : "Period"
> apci.plot(data = test_data, outcome_var = "inlfc", age = "acc",model=APC_I,
+ period = "pcc",type="explore")
Ignoring unknown labels:
• names : "Age"
Ignoring unknown labels:
• names : "Period"
>
> apci.bar(model = APC_I, age = "acc",period = "pcc")
Ignoring unknown labels:
• names : ""
Warning message:
Removed 13 rows containing missing values or values outside the scale range
(`geom_text()`).
> apci.plot.heatmap(model = APC_I, age = "acc",period = "pcc")
> apci.plot.hexagram(model = APC_I, age = "acc",period = "pcc",
+ first_age = 20,first_period = 1990,interval = 5)
> apci.plot(data = test_data, outcome_var = "inlfc", age = "acc",model=APC_I,
+ period = "pcc")
Warning message:
In ggplot2::geom_text(x = 0.5, y = 0.5, label = c("APC-I Model")) :*** buffer overflow detected ***: terminated
Aborted
Flavor: r-devel-linux-x86_64-debian-gcc