Why is linear regression results so much different from Poisson regression?Goodness of fit and which model to...
Is tuition reimbursement a good idea if you have to stay with the job
Why did Robert pick unworthy men for the White Cloaks?
Why is it bad to use your whole foot in rock climbing
How can I find out about the game world without meta-influencing it?
Undocumented incompatibility between changes and siunitx?
Does WiFi affect the quality of images downloaded from the internet?
Can I attach a DC blower to intake manifold of my 150CC Yamaha FZS FI engine?
Why did the AvroCar fail to fly above 3 feet?
Can a 40amp breaker be used safely and without issue with a 40amp device on 6AWG wire?
Part of my house is inexplicably gone
Fully extended TQFT and lattice models
I sent an angry e-mail to my interviewers about a conflict at my home institution. Could this affect my application?
David slept with Bathsheba because she was pure?? What does that mean?
Why did the Death Eaters wait to reopen the Chamber of Secrets?
What is the theme of analysis?
What do you call the action of "describing events as they happen" like sports anchors do?
Fastest way from 8 to 7
Class A Amplifier Design: Emitter Resistance Voltage Drop
List of interesting Quantitative Finance podcasts
What do I need to do, tax-wise, for a sudden windfall?
Why are ambiguous grammars bad?
Dedicated bike GPS computer over smartphone
Why does there seem to be an extreme lack of public trashcans in Taiwan?
Am I allowed to determine tenets of my contract as a warlock?
Why is linear regression results so much different from Poisson regression?
Goodness of fit and which model to choose linear regression or PoissonOdd results from Poisson regression in RPoisson or binomial regression?Poisson regression versus Poisson random effect regression modelsWhy are there huge differences in the SEs from binomial & linear regression?Poisson or Linear Regression for Time DataWhy is Ordinary Least Squares performing better than Poisson regression?Poisson regression for ordered variablesPoisson regression for continuous variables?Odds ratio for poisson regressionWhy use Poisson regression for p-values for linear regression?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0;
}
$begingroup$
I ran both a linear regression and poisson regression on count data (data ranges from 0-54) with two continuous predictors and the p values were very different between them.
m <- glm(Count ~ Age + Days, data = dat, family = 'poisson')
m <- lm(Count ~ Age + Days, data = dat)
Am I doing something wrong here with one of those models? The p values was significant for age and days (around .02) in the poisson regression and non-significant for both age and days (around .5) in the linear regression. The standard errors were also lower in the poisson regression.
Thanks
regression
asked 8 hours ago
RyanRyan
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I ran both a linear regression and poisson regression on count data (data ranges from 0-54) with two continuous predictors and the p values were very different between them.
m <- glm(Count ~ Age + Days, data = dat, family = 'poisson')
m <- lm(Count ~ Age + Days, data = dat)
Am I doing something wrong here with one of those models? The p values was significant for age and days (around .02) in the poisson regression and non-significant for both age and days (around .5) in the linear regression. The standard errors were also lower in the poisson regression.
Thanks
regression
asked 8 hours ago
RyanRyan
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
There's nothing wrong with your seeing a "significant' effect in one model but not the other. As kjetil b halvorsen and @Glen_b has pointed, out these are two very different models that made different assumptions. As a result, they should not necessarily have agreements on all statistical tests.
$endgroup$
– StatsStudent
5 hours ago
add a comment |
$begingroup$
I ran both a linear regression and poisson regression on count data (data ranges from 0-54) with two continuous predictors and the p values were very different between them.
m <- glm(Count ~ Age + Days, data = dat, family = 'poisson')
m <- lm(Count ~ Age + Days, data = dat)
Am I doing something wrong here with one of those models? The p values was significant for age and days (around .02) in the poisson regression and non-significant for both age and days (around .5) in the linear regression. The standard errors were also lower in the poisson regression.
Thanks
regression
asked 8 hours ago
RyanRyan
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I ran both a linear regression and poisson regression on count data (data ranges from 0-54) with two continuous predictors and the p values were very different between them.
m <- glm(Count ~ Age + Days, data = dat, family = 'poisson')
m <- lm(Count ~ Age + Days, data = dat)
Am I doing something wrong here with one of those models? The p values was significant for age and days (around .02) in the poisson regression and non-significant for both age and days (around .5) in the linear regression. The standard errors were also lower in the poisson regression.
Thanks
regression
regression
asked 8 hours ago
RyanRyan
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 hours ago
RyanRyan
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 hours ago
RyanRyan
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 hours ago
RyanRyan
133
asked 8 hours ago
RyanRyan
133
133
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Ryan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
There's nothing wrong with your seeing a "significant' effect in one model but not the other. As kjetil b halvorsen and @Glen_b has pointed, out these are two very different models that made different assumptions. As a result, they should not necessarily have agreements on all statistical tests.
$endgroup$
– StatsStudent
5 hours ago
add a comment |
$begingroup$
There's nothing wrong with your seeing a "significant' effect in one model but not the other. As kjetil b halvorsen and @Glen_b has pointed, out these are two very different models that made different assumptions. As a result, they should not necessarily have agreements on all statistical tests.
$endgroup$
– StatsStudent
5 hours ago
$begingroup$
There's nothing wrong with your seeing a "significant' effect in one model but not the other. As kjetil b halvorsen and @Glen_b has pointed, out these are two very different models that made different assumptions. As a result, they should not necessarily have agreements on all statistical tests.
$endgroup$
– StatsStudent
5 hours ago
$begingroup$
There's nothing wrong with your seeing a "significant' effect in one model but not the other. As kjetil b halvorsen and @Glen_b has pointed, out these are two very different models that made different assumptions. As a result, they should not necessarily have agreements on all statistical tests.
$endgroup$
– StatsStudent
5 hours ago
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
The biggest difference there will be caused by the fact that the Poisson GLM by default will be using the log link while the regression model uses an identity link. That is, it will fit a model
$log(E[Y|x_1,x_2]) = beta_0 + beta_1 x_1 + beta_2 x_2$
for the Poisson GLM (by default). This model is linear in the log of the conditional mean. Meanwhile the regression model fits
$E[Y|x_1,x_2] = beta_0 + beta_1 x_1 + beta_2 x_2,.$
which is linear in the mean.
These are very different models. The coefficients mean different things. The shape of the fit is different.
The two models also make different variance assumptions.
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
$endgroup$
add a comment |
$begingroup$
Why do you expect the resultd from those two models to be even similar? Those are very different models, linear regression is an additive model while Poisson regression is multiplicative. See Goodness of fit and which model to choose linear regression or Poisson for a comparison.
For count data, mostly the Poisson regression model is indicated.
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
$endgroup$
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
add a comment |
Your Answer
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "65"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Ryan is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f412577%2fwhy-is-linear-regression-results-so-much-different-from-poisson-regression%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
The biggest difference there will be caused by the fact that the Poisson GLM by default will be using the log link while the regression model uses an identity link. That is, it will fit a model
$log(E[Y|x_1,x_2]) = beta_0 + beta_1 x_1 + beta_2 x_2$
for the Poisson GLM (by default). This model is linear in the log of the conditional mean. Meanwhile the regression model fits
$E[Y|x_1,x_2] = beta_0 + beta_1 x_1 + beta_2 x_2,.$
which is linear in the mean.
These are very different models. The coefficients mean different things. The shape of the fit is different.
The two models also make different variance assumptions.
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
$endgroup$
add a comment |
$begingroup$
The biggest difference there will be caused by the fact that the Poisson GLM by default will be using the log link while the regression model uses an identity link. That is, it will fit a model
$log(E[Y|x_1,x_2]) = beta_0 + beta_1 x_1 + beta_2 x_2$
for the Poisson GLM (by default). This model is linear in the log of the conditional mean. Meanwhile the regression model fits
$E[Y|x_1,x_2] = beta_0 + beta_1 x_1 + beta_2 x_2,.$
which is linear in the mean.
These are very different models. The coefficients mean different things. The shape of the fit is different.
The two models also make different variance assumptions.
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
$endgroup$
add a comment |
$begingroup$
The biggest difference there will be caused by the fact that the Poisson GLM by default will be using the log link while the regression model uses an identity link. That is, it will fit a model
$log(E[Y|x_1,x_2]) = beta_0 + beta_1 x_1 + beta_2 x_2$
for the Poisson GLM (by default). This model is linear in the log of the conditional mean. Meanwhile the regression model fits
$E[Y|x_1,x_2] = beta_0 + beta_1 x_1 + beta_2 x_2,.$
which is linear in the mean.
These are very different models. The coefficients mean different things. The shape of the fit is different.
The two models also make different variance assumptions.
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
$endgroup$
The biggest difference there will be caused by the fact that the Poisson GLM by default will be using the log link while the regression model uses an identity link. That is, it will fit a model
$log(E[Y|x_1,x_2]) = beta_0 + beta_1 x_1 + beta_2 x_2$
for the Poisson GLM (by default). This model is linear in the log of the conditional mean. Meanwhile the regression model fits
$E[Y|x_1,x_2] = beta_0 + beta_1 x_1 + beta_2 x_2,.$
which is linear in the mean.
These are very different models. The coefficients mean different things. The shape of the fit is different.
The two models also make different variance assumptions.
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
edited 5 hours ago
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
answered 6 hours ago
Glen_b♦Glen_b
218k23423781
218k23423781
add a comment |
add a comment |
$begingroup$
Why do you expect the resultd from those two models to be even similar? Those are very different models, linear regression is an additive model while Poisson regression is multiplicative. See Goodness of fit and which model to choose linear regression or Poisson for a comparison.
For count data, mostly the Poisson regression model is indicated.
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
$endgroup$
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
add a comment |
$begingroup$
Why do you expect the resultd from those two models to be even similar? Those are very different models, linear regression is an additive model while Poisson regression is multiplicative. See Goodness of fit and which model to choose linear regression or Poisson for a comparison.
For count data, mostly the Poisson regression model is indicated.
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
$endgroup$
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
add a comment |
$begingroup$
Why do you expect the resultd from those two models to be even similar? Those are very different models, linear regression is an additive model while Poisson regression is multiplicative. See Goodness of fit and which model to choose linear regression or Poisson for a comparison.
For count data, mostly the Poisson regression model is indicated.
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
$endgroup$
Why do you expect the resultd from those two models to be even similar? Those are very different models, linear regression is an additive model while Poisson regression is multiplicative. See Goodness of fit and which model to choose linear regression or Poisson for a comparison.
For count data, mostly the Poisson regression model is indicated.
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
answered 8 hours ago
kjetil b halvorsenkjetil b halvorsen
34.3k990262
34.3k990262
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
add a comment |
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
Thanks so much for the answer. So everything looks ok with my code? Also, im new to stats, can you tell me what you mean by multiplicative and how its different from the additive model?
$endgroup$
– Ryan
8 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
$begingroup$
There is no problem with your code. Else, see the link above. And tell us what you know about glm's (generalized linear models.) Do you know about link functions? For additive/mult see stats.stackexchange.com/…*+model+answers%3A1
$endgroup$
– kjetil b halvorsen
7 hours ago
add a comment |
Ryan is a new contributor. Be nice, and check out our Code of Conduct.
Ryan is a new contributor. Be nice, and check out our Code of Conduct.
Ryan is a new contributor. Be nice, and check out our Code of Conduct.
Ryan is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Cross Validated!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f412577%2fwhy-is-linear-regression-results-so-much-different-from-poisson-regression%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
There's nothing wrong with your seeing a "significant' effect in one model but not the other. As kjetil b halvorsen and @Glen_b has pointed, out these are two very different models that made different assumptions. As a result, they should not necessarily have agreements on all statistical tests.
$endgroup$
– StatsStudent
5 hours ago