Nothing
tests/testthat/_snaps/summarize_glm_count.md
In tern: Create Common TLGs Used in Clinical Trials
h_glm_poisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMB: Placebo
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_glm_poisson glm-fit works with healthy input with covariates
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.18541942 10.8438255 2.133586e-27 (Intercept)
2 0.07631174 0.17896220 0.4264126 6.698072e-01 REGION1Asia
3 0.64425750 0.22389462 2.8775033 4.008358e-03 REGION1Eurasia
4 2.13096720 0.36521976 5.8347533 5.387022e-09 REGION1Europe
5 -0.07449500 0.20314837 -0.3667024 7.138410e-01 REGION1North America
6 0.38101695 0.21554753 1.7676703 7.711605e-02 REGION1South America
7 0.11047866 0.09872549 1.1190490 2.631192e-01 ARMB: Placebo
8 -0.17694419 0.10873176 -1.6273459 1.036637e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input with covariates
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 12.64167 1.2378669 Inf 1 25.90902 5.270655e-148
2 ARM B 14.11838 1.2848735 Inf 1 29.09088 4.682722e-186
3 ARM C 10.59153 0.9708089 Inf 1 25.74821 3.375733e-146
h_glm_quasipoisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.7781805 2.5837912 0.01051488 (Intercept)
2 0.07631174 0.7510804 0.1016026 0.91917812 REGION1Asia
3 0.64425750 0.9396557 0.6856314 0.49377257 REGION1Eurasia
4 2.13096720 1.5327784 1.3902643 0.16605816 REGION1Europe
5 -0.07449500 0.8525865 -0.0873753 0.93046426 REGION1North America
6 0.38101695 0.9046241 0.4211881 0.67408881 REGION1South America
7 0.11047866 0.4143377 0.2666392 0.79003312 ARMB: Placebo
8 -0.17694419 0.4563327 -0.3877526 0.69862863 ARMC: Combination
h_glm_quasipoisson emmeans-fit works with healthy input
Code
res
Output
ARM rate std.error df null statistic p.value
1 A: Drug X 12.64167 5.195162 Inf 1 6.173420 6.682841e-10
2 B: Placebo 14.11838 5.392442 Inf 1 6.931571 4.161914e-12
3 C: Combination 10.59153 4.074355 Inf 1 6.135104 8.510352e-10
h_glm_negbin glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 1.005041594 0.1992268 5.04471149 4.542062e-07 (Intercept)
2 0.007741431 0.1919877 0.04032253 9.678360e-01 REGION1Asia
3 0.317703043 0.2360653 1.34582686 1.783584e-01 REGION1Eurasia
4 0.591541717 0.4058327 1.45759983 1.449509e-01 REGION1Europe
5 0.117240049 0.2196300 0.53380718 5.934749e-01 REGION1North America
6 0.139971334 0.2348685 0.59595610 5.512046e-01 REGION1South America
7 0.113082781 0.1056295 1.07056107 2.843668e-01 ARMB: Placebo
8 0.026817451 0.1131811 0.23694292 8.127011e-01 ARMC: Combination
h_glm_negbin emmeans-fit works with healthy input
Code
res
Output
ARM response std.error df null statistic p.value
1 A: Drug X 3.322579 0.3367532 Inf 1 11.84712 2.227054e-32
2 B: Placebo 3.720373 0.3782682 Inf 1 12.92183 3.390023e-38
3 C: Combination 3.412887 0.3424577 Inf 1 12.23369 2.054037e-34
h_glm_count glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMCDARM B
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMCDARM C
h_glm_count emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_ppmeans works with healthy input
Code
fits
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::poisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: 1498
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 1.238 Inf 10.43 15.3
ARM B 14.1 1.285 Inf 11.81 16.9
ARM C 10.6 0.971 Inf 8.85 12.7
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Code
fits2
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::quasipoisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: NA
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 5.20 Inf 5.65 28.3
ARM B 14.1 5.39 Inf 6.68 29.8
ARM C 10.6 4.07 Inf 4.98 22.5
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
s_glm_count works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8954054 0.7501944
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7378778 0.6062152 1.0865633 0.9283695
attr(,"label")
[1] "95% CI"
$pval
[1] 0.263119218 0.008203621
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8930767 0.9173508
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7260672 0.7381034 1.0985017 1.1401282
attr(,"label")
[1] "95% CI"
$pval
[1] 0.2843668 0.4367453
attr(,"label")
[1] "p-value"
summarize_glm_count works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 9.1554 8.2061 7.8551
summarize_glm_count (negative binomial) works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 3.1918 2.9275 3.0862
Try the tern package in your browser
Any scripts or data that you put into this service are public.
tern documentation built on June 22, 2024, 10:25 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
h_glm_poisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMB: Placebo
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_glm_poisson glm-fit works with healthy input with covariates
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.18541942 10.8438255 2.133586e-27 (Intercept)
2 0.07631174 0.17896220 0.4264126 6.698072e-01 REGION1Asia
3 0.64425750 0.22389462 2.8775033 4.008358e-03 REGION1Eurasia
4 2.13096720 0.36521976 5.8347533 5.387022e-09 REGION1Europe
5 -0.07449500 0.20314837 -0.3667024 7.138410e-01 REGION1North America
6 0.38101695 0.21554753 1.7676703 7.711605e-02 REGION1South America
7 0.11047866 0.09872549 1.1190490 2.631192e-01 ARMB: Placebo
8 -0.17694419 0.10873176 -1.6273459 1.036637e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input with covariates
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 12.64167 1.2378669 Inf 1 25.90902 5.270655e-148
2 ARM B 14.11838 1.2848735 Inf 1 29.09088 4.682722e-186
3 ARM C 10.59153 0.9708089 Inf 1 25.74821 3.375733e-146
h_glm_quasipoisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.7781805 2.5837912 0.01051488 (Intercept)
2 0.07631174 0.7510804 0.1016026 0.91917812 REGION1Asia
3 0.64425750 0.9396557 0.6856314 0.49377257 REGION1Eurasia
4 2.13096720 1.5327784 1.3902643 0.16605816 REGION1Europe
5 -0.07449500 0.8525865 -0.0873753 0.93046426 REGION1North America
6 0.38101695 0.9046241 0.4211881 0.67408881 REGION1South America
7 0.11047866 0.4143377 0.2666392 0.79003312 ARMB: Placebo
8 -0.17694419 0.4563327 -0.3877526 0.69862863 ARMC: Combination
h_glm_quasipoisson emmeans-fit works with healthy input
Code
res
Output
ARM rate std.error df null statistic p.value
1 A: Drug X 12.64167 5.195162 Inf 1 6.173420 6.682841e-10
2 B: Placebo 14.11838 5.392442 Inf 1 6.931571 4.161914e-12
3 C: Combination 10.59153 4.074355 Inf 1 6.135104 8.510352e-10
h_glm_negbin glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 1.005041594 0.1992268 5.04471149 4.542062e-07 (Intercept)
2 0.007741431 0.1919877 0.04032253 9.678360e-01 REGION1Asia
3 0.317703043 0.2360653 1.34582686 1.783584e-01 REGION1Eurasia
4 0.591541717 0.4058327 1.45759983 1.449509e-01 REGION1Europe
5 0.117240049 0.2196300 0.53380718 5.934749e-01 REGION1North America
6 0.139971334 0.2348685 0.59595610 5.512046e-01 REGION1South America
7 0.113082781 0.1056295 1.07056107 2.843668e-01 ARMB: Placebo
8 0.026817451 0.1131811 0.23694292 8.127011e-01 ARMC: Combination
h_glm_negbin emmeans-fit works with healthy input
Code
res
Output
ARM response std.error df null statistic p.value
1 A: Drug X 3.322579 0.3367532 Inf 1 11.84712 2.227054e-32
2 B: Placebo 3.720373 0.3782682 Inf 1 12.92183 3.390023e-38
3 C: Combination 3.412887 0.3424577 Inf 1 12.23369 2.054037e-34
h_glm_count glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMCDARM B
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMCDARM C
h_glm_count emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_ppmeans works with healthy input
Code
fits
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::poisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: 1498
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 1.238 Inf 10.43 15.3
ARM B 14.1 1.285 Inf 11.81 16.9
ARM C 10.6 0.971 Inf 8.85 12.7
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Code
fits2
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::quasipoisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: NA
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 5.20 Inf 5.65 28.3
ARM B 14.1 5.39 Inf 6.68 29.8
ARM C 10.6 4.07 Inf 4.98 22.5
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
s_glm_count works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8954054 0.7501944
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7378778 0.6062152 1.0865633 0.9283695
attr(,"label")
[1] "95% CI"
$pval
[1] 0.263119218 0.008203621
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8930767 0.9173508
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7260672 0.7381034 1.0985017 1.1401282
attr(,"label")
[1] "95% CI"
$pval
[1] 0.2843668 0.4367453
attr(,"label")
[1] "p-value"
summarize_glm_count works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 9.1554 8.2061 7.8551
summarize_glm_count (negative binomial) works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 3.1918 2.9275 3.0862
Try the tern package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.