Modelling Parallel bears [🇷 for BE/BA]
Dear d_labes,
When I revised bear today, I found that the following two models got the same results (with built-in dataset in bear v2.4.2) for CIs.
and
Just a quick response. I am studying Helmut's t tests for further revision.
When I revised bear today, I found that the following two models got the same results (with built-in dataset in bear v2.4.2) for CIs.
❝ lmeCmax_ss<-lme(Cmax_ss ~ drug, random=~1|subj, data=Data,
❝ method="REML" )
Dependent Variable: lnCmax
Linear mixed-effects model fit by REML
Data: TotalData
AIC BIC logLik
-14.15488 -9.279372 11.07744
Random effects:
Formula: ~1 | subj
(Intercept) Residual
StdDev: 0.1310857 0.04915712
Fixed effects: lnCmax ~ drug
Value Std.Error DF t-value p-value
(Intercept) 7.352034 0.03882889 25 189.34445 0.0000
drug2 0.008486 0.05392284 25 0.15738 0.8762
Correlation:
(Intr)
drug2 -0.72
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.83569001 -0.14504896 0.05106144 0.21037957 0.69279954
Number of Observations: 27
Number of Groups: 27
and
❝ lmCmax_ss<- lm(Cmax_ss ~ drug , data=TotalData)
Call:
lm(formula = lnCmax ~ drug, data = TotalData)
Residuals:
Min 1Q Median 3Q Max
-0.33321 -0.05783 0.02036 0.08388 0.27623
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.352034 0.038829 189.344 <2e-16 ***
drug2 0.008486 0.053923 0.157 0.876
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.14 on 25 degrees of freedom
Multiple R-squared: 0.0009897, Adjusted R-squared: -0.03897
F-statistic: 0.02477 on 1 and 25 DF, p-value: 0.8762
Just a quick response. I am studying Helmut's t tests for further revision.
—
All the best,
-- Yung-jin Lee
bear v2.9.2:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here
All the best,
-- Yung-jin Lee
bear v2.9.2:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here
Complete thread:
- Parallel bears meeting at random in infinity d_labes 2010-04-22 11:43
- Parallel bears meeting at random in infinity ElMaestro 2010-04-22 12:53
- Parallel groups in bear - CIs d_labes 2010-04-22 14:00
- Parallel groups in bear - CIs ElMaestro 2010-04-22 21:47
- Parallel groups in bear - CIs d_labes 2010-04-23 09:09
- Parallel groups in bear - CIs yjlee168 2010-04-25 23:29
- Parallel groups in bear - CIs ElMaestro 2010-04-22 21:47
- Parallel groups in bear - CIs d_labes 2010-04-22 14:00
- Parallel bears meeting at random in infinity yjlee168 2010-04-22 23:09
- Modelling Parallel bears d_labes 2010-04-23 09:12
- Modelling Parallel bears yjlee168 2010-04-23 21:14
- Validating vs. WinNonlin... Helmut 2010-04-24 00:28
- Validating vs. WinNonlin... yjlee168 2010-04-24 19:36
- Validating vs. WinNonlin... yjlee168 2010-04-26 00:09
- Validating vs. WinNonlin... Helmut 2010-04-26 01:29
- WNL in replicate BE yjlee168 2010-04-26 08:59
- WNL in replicate BE Helmut 2010-04-26 16:15
- WNL in replicate BE yjlee168 2010-04-26 08:59
- Validating vs. WinNonlin... Helmut 2010-04-26 01:29
- Modelling Parallel bearsyjlee168 2010-04-25 19:34
- Modelling Parallel bears ElMaestro 2010-04-25 20:40
- Dataset Helmut 2010-04-25 22:38
- Dataset yjlee168 2010-04-25 22:44
- Dataset Helmut 2010-04-26 01:13
- Dataset yjlee168 2010-04-26 08:16
- NCA → Statistical analysis for parallel study Helmut 2010-04-26 13:12
- NCA → Statistical analysis for parallel study yjlee168 2010-04-26 18:43
- NCA → Statistical analysis for parallel study Helmut 2010-04-26 13:12
- Dataset yjlee168 2010-04-26 08:16
- Dataset Helmut 2010-04-26 01:13
- dilemma yjlee168 2010-04-26 08:41
- Equal variances d_labes 2010-04-26 09:04
- Equal variances yjlee168 2010-04-26 09:22
- GLM = Equal variances d_labes 2010-04-26 13:29
- GLM = Equal variances Helmut 2010-04-26 14:45
- I'm a believer d_labes 2010-04-26 15:58
- I'm a believer Helmut 2010-04-26 16:31
- I'm a believer d_labes 2010-04-26 15:58
- GLM = Equal variances Helmut 2010-04-26 14:45
- GLM = Equal variances d_labes 2010-04-26 13:29
- Equal variances Helmut 2010-04-26 12:55
- gls() for unequal variances? d_labes 2010-04-26 16:36
- gls() for unequal variances? Helmut 2010-04-26 17:00
- Sims Helmut 2010-04-27 01:36
- Sandwich - Simsalabim d_labes 2010-04-28 10:58
- Sandwich - Simsalabim Helmut 2010-04-28 14:19
- parametrization of R function rlnorm martin 2010-05-02 18:22
- Mean of log-normal d_labes 2010-05-03 16:22
- parametrization of R function rlnorm ElMaestro 2013-07-26 21:42
- Martin‽ Helmut 2013-07-28 02:01
- Sandwich - Simsalabim d_labes 2010-04-28 10:58
- gls() for unequal variances? d_labes 2010-04-26 16:36
- Equal variances yjlee168 2010-04-26 09:22
- Dataset yjlee168 2010-04-25 22:44
- Validating vs. WinNonlin... Helmut 2010-04-24 00:28
- Modelling Parallel bears yjlee168 2010-04-23 21:14
- Modelling Parallel bears d_labes 2010-04-23 09:12
- Parallel bears meeting at random in infinity ElMaestro 2010-04-22 12:53