Re: [R] Quoting smooth random terms mccv::gam
Thanks very much, Simon! On 12/17/18, 8:23 AM, "Simon Wood" wrote: I would quote the p-value, but not the statistic (as it is not a standard F stat). The actual statistic is given here: https://urldefense.proofpoint.com/v2/url?u=https-3A__academic.oup.com_biomet_article-2Dpdf_100_4_1005_566200_ast038.pdf=DwIDaQ=UXmaowRpu5bLSLEQRunJ2z-YIUZuUoa9Rw_x449Hd_Y=y6YM-SSv8WOxR70LMzwwFohC41WMNU4ZGFcHpTmGWLo=CqMTgm500nmgBrPfC2cLIc45sZ6h0I2odVPYT8qCmYA=epymthgL8_opS9pITmfKRJnH_uzCCzEWTYU9qEwQ_q0= On 14/12/2018 04:33, Smith, Desmond wrote: > Dear All, > > I have a mgcv::gam model of the form: > > m1 <- gam(Y ~ A + s(B, bs = "re"), data = dataframe, family = gaussian, method = "REML") > > The random term is quoted in summary(m1) as, for example, > > Approximate significance of smooth terms: > # edf Ref.df F p-value > s(B) 4.486 5 97.195 6.7e-08 *** > > My question is, how would I quote this result (statistic and P value) in a formal document? > > For example, one possibility is F[4.486,5] = 97.195, P = 6.7e-08. However, arguing against this, “reverse engineering” of the result using > > pf(q= 97.195, df1= 4.486, df2= 5, lower.tail=FALSE) > > gives an incorrect p value: > > [1] 0.1435508 > > I would be very grateful for your advice. Many thanks for your help! > > > > UCLA HEALTH SCIENCES IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_r-2Dhelp=DwIDaQ=UXmaowRpu5bLSLEQRunJ2z-YIUZuUoa9Rw_x449Hd_Y=y6YM-SSv8WOxR70LMzwwFohC41WMNU4ZGFcHpTmGWLo=CqMTgm500nmgBrPfC2cLIc45sZ6h0I2odVPYT8qCmYA=XCgKsfTTCyRoegz5hqMvtEt9m0KRm-qD0HtpXGYdxzg= > PLEASE do read the posting guide https://urldefense.proofpoint.com/v2/url?u=http-3A__www.R-2Dproject.org_posting-2Dguide.html=DwIDaQ=UXmaowRpu5bLSLEQRunJ2z-YIUZuUoa9Rw_x449Hd_Y=y6YM-SSv8WOxR70LMzwwFohC41WMNU4ZGFcHpTmGWLo=CqMTgm500nmgBrPfC2cLIc45sZ6h0I2odVPYT8qCmYA=hyi1_CVKTW4c_7lyb3TlmtPnSpsQfnI7BhRjVOLhXdI= > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Quoting smooth random terms mccv::gam
Dear All, I have a mgcv::gam model of the form: m1 <- gam(Y ~ A + s(B, bs = "re"), data = dataframe, family = gaussian, method = "REML") The random term is quoted in summary(m1) as, for example, Approximate significance of smooth terms: # edf Ref.df F p-value s(B) 4.486 5 97.195 6.7e-08 *** My question is, how would I quote this result (statistic and P value) in a formal document? For example, one possibility is F[4.486,5] = 97.195, P = 6.7e-08. However, arguing against this, “reverse engineering” of the result using pf(q= 97.195, df1= 4.486, df2= 5, lower.tail=FALSE) gives an incorrect p value: [1] 0.1435508 I would be very grateful for your advice. Many thanks for your help! UCLA HEALTH SCIENCES IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Fixed effects in negative binomial mixed model mgcv::gam
I am using gam from the mgcv package to analyze a dataset with 24 entries : ran f1 f2 y 1 30005 545 1 300010 1045 1 1 5 536 1 1 10 770 2 30005 842 2 300010 2042 2 1 5 615 2 1 10 1361 3 30005 328 3 300010 1028 3 1 5 262 3 1 10 722 4 30005 349 4 300010 665 4 1 5 255 4 1 10 470 5 30005 680 5 300010 1510 5 1 5 499 5 1 10 1422 6 30005 628 6 300010 2062 6 1 5 499 6 1 10 2158 The data has two fixed effects (f1 and f2) and one random effect (ran). The dependent data is y. Because the dependent data y represents counts and is overdispersed, I am using a negative binomial model. The gam model and its summary output is as follows: library(mgcv) summary(gam(y ~ f1 * f2 + s(ran, bs = "re"), data = df2, family = nb, method = "REML")) Family: Negative Binomial(27.376) Link function: log Formula: y ~ f1 * f2 + s(ran, bs = "re") Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.500e+00 3.137e-01 17.533 < 2e-16 *** f1 -3.421e-05 3.619e-05 -0.9450.345 f2 1.760e-01 3.355e-02 5.247 1.55e-07 *** f1:f22.665e-07 4.554e-06 0.0590.953 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Approximate significance of smooth terms: edf Ref.df Chi.sq p-value s(ran) 4.726 5 85.66 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 R-sq.(adj) = 0.866 Deviance explained = 93.6% -REML = 185.96 Scale est. = 1 n = 24 The Wald test from summary gives very high significance for f2 (P = 1.55e-07). However, when I test the significance of f2 by comparing two different models using anova, I get dramatically different results: anova(gam(y ~ f1 * f2 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"), gam(y ~ f1 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"), test="Chisq") Analysis of Deviance Table Model 1: y ~ f1 * f2 + s(ran, bs = "re") Model 2: y ~ f1 + s(ran, bs = "re") Resid. Df Resid. Dev Df Deviance Pr(>Chi) 114.843 18.340 216.652 21.529 -1.8091 -3.188 0.1752 f2 is no longer significant. The models were changed from REML to ML, as recommended for evaluation of fixed effects. If the interaction is preserved, f2 still remains insignificant using anova: anova(gam(y ~ f1 + f2 + f1:f2 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"), gam(y ~ f1 + f1:f2 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"), test="Chisq") Analysis of Deviance Table Model 1: y ~ f1 + f2 + f1:f2 + s(ran, bs = "re") Model 2: y ~ f1 + f1:f2 + s(ran, bs = "re") Resid. Df Resid. Dev Df Deviance Pr(>Chi) 114.843 18.340 215.645 19.194 -0.80159 -0.85391 0.2855 I would be very grateful for advice on which of these approaches is most appropriate. Many thanks! UCLA HEALTH SCIENCES IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
