I have following model:
> summary(mymodel.gml6)
Call:
glm(formula = anumber ~ poly(coun, 2, raw = TRUE) + pharm+
pharm:patus, family = poisson, data = mydata)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4805 -1.7070 -0.7171 0.4482 12.6264
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2786022 0.0570305 -4.885 1.03e-06 ***
poly(coun, 2, raw = TRUE)1 0.2217527 0.0162746 13.626 < 2e-16 ***
poly(coun, 2, raw = TRUE)2 -0.0156164 0.0009538 -16.372 < 2e-16 ***
pharmyes 0.3945798 0.0343844 11.476 < 2e-16 ***
pharmno:patusyes 0.0178374 0.0352272 0.506 0.613
pharmyes:patusyes 0.2206909 0.0311678 7.081 1.43e-12 ***
pharm as well as patus are factors containing the values "yes" and "no". Now, I would like to remove the term pharmno:patusyes as it is not significant. I tried with the update method, but it did not work:
mymodel<-update(mymodel, .~.-pharmno:patusyes)
The questions here and here are slightly different as they focus on the removal of a complete interaction term.
R isn't letting you do it, because it shouldn't be done. The interaction is represented by two variables; you need to retain both or neither. It doesn't matter if the p-value reported on the line associated with one of those variables is significant, as you will get different patterns of significance with the same data with different ways of specifying the same model. To test the interaction, you need a simultaneous test of both variables. It seems to me there is something strange in your model but not enough information is presented to see, nonetheless, you should be able to do drop1(mymodel.glm6, test="LRT") to get that test in R.
Related
I have the following model:
ModelPower <- lmer(DV ~ GroupAbstract * Condition_Cat_Abs + (1|Participant) + (1 + GroupAbstract|Stimulus), data = Dataset)
This model gives the following output:
Random effects:
Groups Name Variance Std.Dev. Corr
Participant (Intercept) 377.401 19.427
Stimulus (Intercept) 91.902 9.587
GroupAbstractOutgroup 2.003 1.415 -0.40
Residual 338.927 18.410
Number of obs: 16512, groups: Participant, 344; Stimulus, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 65.8962 2.0239 59.6906 32.559 < 0.0000000000000002 ***
GroupAbstractOutgroup -0.9287 0.5561 129.9242 -1.670 0.0973 .
Condition_Cat_AbsSecondOrderIn -2.2584 0.4963 16103.9277 -4.550 0.00000539 ***
Condition_Cat_AbsSecondOrderOut -7.0821 0.4963 16103.9277 -14.270 < 0.0000000000000002 ***
GroupAbstractOutgroup:Condition_Cat_AbsSecondOrderIn -3.0229 0.7019 16103.9277 -4.307 0.00001665 ***
GroupAbstractOutgroup:Condition_Cat_AbsSecondOrderOut 7.8765 0.7019 16103.9277 11.222 < 0.0000000000000002 ***
I am interested in the interaction "GroupAbstractOutgroup:Condition_Cat_AbsSecondOrderIn" and I am trying to estimate the sample size to detect an effect size of at least -2 using the R package simr. the original slope is -3.02 so I specify the new one:
ModelPower#beta[names(fixef(ModelPower)) %in% "GroupAbstractOutgroup:Condition_Cat_AbsSecondOrderIn"] <- -2
However, regardless of how I specify the powerSim function both for the main effects and interactions (see some examples below), I get power of 0% and the following error when running lastResult()$errors 'object is not a matrix'. I know what the error should mean but even after converting the original data frame and the table of fixed effects to a matrix, the error is still there and I am not sure what it is referring to and how to get the actual output. Any help would be much appreciated!
Examples of the powerSim function:
powerSim(ModelPower, test=fixed("GroupAbstract", "anova"), nsim=10, seed=1)
powerSim(ModelPower, test=fixed("GroupAbstractOutgroup:Condition_Cat_AbsSecondOrderIn", "anova"), nsim=10, seed=1)
We're trying to model a count variable with excessive zeros using a zero-inflated poisson (as implemented in pscl package). Here is a (simplified) output showing both categorical and continuous explanatory variables:
library(pscl)
> m1 <- zeroinfl(y ~ treatment + some_covar, data = d, dist =
"poisson")
> summary(m1)
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.189253 0.102256 31.189 < 2e-16 ***
treatmentB -0.282478 0.107965 -2.616 0.00889 **
treatmentC 0.227633 0.103605 2.197 0.02801 *
some_covar 0.002190 0.002329 0.940 0.34706
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.67251 0.74961 0.897 0.3696
treatmentB -1.72728 0.89931 -1.921 0.0548 .
treatmentC -0.31761 0.77668 -0.409 0.6826
some_covar -0.03736 0.02684 -1.392 0.1640
summary gave us some good answers but we are looking for a ANOVA-like table. So, the question is: is it ok to use car::Anova to obtain such table?
> Anova(m1)
Analysis of Deviance Table (Type II tests)
Response: y
Df Chisq Pr(>Chisq)
treatment 2 30.7830 2.068e-07 ***
some_covar 1 0.8842 0.3471
It seems to work fine but i'm not really sure whether is a valid approach since documentation is missing (seems like is only considering the 'count model' part?). Do you recommend to follow this approach or there is a better way?
I want to compute a logit regression for rare events. I decided to use the Zelig package (relogit function) to do so.
Usually, I use stargazer to extract and save regression results. However, there seem to be compatibility issues with these two packages (Using stargazer with Zelig).
I now want to extract the following information from the Zelig relogit output:
Coefficients, z values, p values, number of observations, log likelihood, AIC
I have managed to extract the p-values and coefficients. However, I failed at the rest. But I am sure these values must be accessible somehow, because they are reported in the summary() output (however, I did not manage to store the summary output as an R object). The summary cannot be processed in the same way as a regular glm summary (https://stats.stackexchange.com/questions/176821/relogit-model-from-zelig-package-in-r-how-to-get-the-estimated-coefficients)
A reproducible example:
##Initiate package, model and data
require(Zelig)
data(mid)
z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
data = mid, model = "relogit")
##Call summary on output (reports in console most of the needed information)
summary(z.out1)
##Storing the summary fails and only produces a useless object
summary(z.out1) -> z.out1.sum
##Some of the output I can access as follows
z.out1$get_coef() -> z.out1.coeff
z.out1$get_pvalue() -> z.out1.p
z.out1$get_se() -> z.out1.se
However, I did not find similar commands for other elements, such as z values, AIC etc. However, as they are shown in the summary() call, they should be accessible somehow.
The summary call result:
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0742 -0.4444 -0.2772 0.3295 3.1556
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.535496 0.179685 -14.111 < 2e-16
major 2.432525 0.157561 15.439 < 2e-16
contig 4.121869 0.157650 26.146 < 2e-16
power 1.053351 0.217243 4.849 1.24e-06
maxdem 0.048164 0.010065 4.785 1.71e-06
mindem -0.064825 0.012802 -5.064 4.11e-07
years -0.063197 0.005705 -11.078 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Use from_zelig_model for deviance, AIC.
m <- from_zelig_model(z.out1)
m$aic
...
Z-values are coefficient / sd.
z.out1$get_coef()[[1]]/z.out1$get_se()[[1]]
I am getting different results from lmPerm based on the order in which I enter the variables in the function call.
For example, placing NCF.pf before TotalProperties yields the following:
pfit <- lmp(NetCashOps ~ NCF.pf + TotalProperties, data = sub.pm, subset = Presence == 1)
summary(pfit)
...
Coefficients:
Estimate Iter Pr(Prob)
NCF.pf 4.581e-01 51 1
TotalProperties 5.246e+04 5000 <2e-16 ***
but, when I switch the order of the coefficients in the formula and place TotalProperties before NCF.pf, the p-value on NCF.pf becomes significant
pfit2 <- lmp(NetCashOps ~ TotalProperties + NCF.pf, data = sub.pm, subset = Presence == 1)
summary(pfit2)
...
Coefficients:
Estimate Iter Pr(Prob)
TotalProperties 5.246e+04 5000 <2e-16 ***
NCF.pf 4.581e-01 5000 <2e-16 ***
Am I missing something? Why would the p-values be different just because I switched the order of the variables in the function call?
Update - Data Source and lm Output (11/11/2016)
The data can be found on GitHub at this link.
When calling the standard lm function twice (reversing the order of the variables on the second call), the p-values are identical (see below). Hence, unlike when using the lmPerm function, the order of the variables doesn't matter with lm.
fit1 <- lm(NetCashOps ~ NCF.pf + TotalProperties, data = sub.pm, subset = Presence == 1)
summary(fit1)
...
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.088e+05 2.258e+05 3.138 0.0019 **
NCF.pf 4.581e-01 1.112e-01 4.121 5.11e-05 ***
TotalProperties 5.246e+04 9.519e+03 5.511 8.76e-08 ***
fit2 <- lm(NetCashOps ~ TotalProperties + NCF.pf, data = sub.pm, subset = Presence == 1)
summary(fit2)
...
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.088e+05 2.258e+05 3.138 0.0019 **
TotalProperties 5.246e+04 9.519e+03 5.511 8.76e-08 ***
NCF.pf 4.581e-01 1.112e-01 4.121 5.11e-05 ***
Thanks!
I already saw 2 close votes to migrate this to Cross Validated, but in my humble opinion this should stay on Stack Overflow. It is true, that t-statistic and p-value are not invariant to the specification order of terms, under the non-pivoted QR factorization strategy used by lm and lmp, but as shown in the new edit, for OP's data, these statistic should be invariant. So there must be something sensitive at programming level.
My quick diagnose suggests, that if we set seqs = TRUE, rather than using the default FALSE, we would get consistent result:
## I have subsetted data with `Presence == 1` into a new dataset `dat`
## I have also renamed variable name for simplicity
coef(summary(lmp(y ~ x1 + x2, dat, seqs = TRUE)))
# Estimate Iter Pr(Prob)
#(Intercept) 2.019959e+06 5000 0
#x1 4.580840e-01 5000 0
#x2 5.245619e+04 5000 0
coef(summary(lmp(y ~ x2 + x1, dat, seqs = TRUE)))
# Estimate Iter Pr(Prob)
#(Intercept) 2.019959e+06 5000 0
#x2 5.245619e+04 5000 0
#x1 4.580840e-01 5000 0
Note, the Pr(Prob) should be "< 2e-16" when printed by summary, but when using coef to obtain a matrix, those tiny values are 0.
The documentation of ?lmp mentions a little on this part:
The SS will be calculated _sequentially_, just as ‘lm()’ does; or
they may be calculated _uniquely_, which means that the SS for
each source is calculated conditionally on all other sources.
I am at the moment not sure what SS is (as I am not a user of lmPerm), but this sounds like that for consistent result, we should set seqs = TRUE.
I'm trying to do a linear regression but I'm only looking to use variables with positive coefficients (I think this is called hard-thresholding, but I'm not certain).
for example:
> summary(lm1)
Call:
lm(formula = value ~ ., data = intCollect1[, -c(1, 3)])
Residuals:
Min 1Q Median 3Q Max
-15.6518 -0.2089 -0.0227 0.2035 15.2235
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.099763 0.024360 4.095 4.22e-05 ***
modelNum3802 0.208867 0.008260 25.285 < 2e-16 ***
modelNum8000 -0.086258 0.013104 -6.582 4.65e-11 ***
modelNum8001 -0.058225 0.010741 -5.421 5.95e-08 ***
modelNum8002 -0.001813 0.012087 -0.150 0.880776
modelNum8003 -0.083646 0.011015 -7.594 3.13e-14 ***
modelNum8004 0.002521 0.010729 0.235 0.814254
modelNum8005 0.301286 0.011314 26.630 < 2e-16 ***
In the above regression, I would only want to use models 3802, 8004 and 8005. Is there a way to do this without copying and pasting each variable name?
Instead of using lm, you can formulate your problem in terms of a Quadratic Programming:
Minimize the sum of the squared replication errors subject to the constraint that your linear coefficients are all positive.
Such problems can be solved using lsei from the limSolve package. Looking at your example, it would look a lot like this:
x.variables <- c("modelNum3802", "modelNum8000", ...)
num.var <- length(x.variables)
lsei(A = intCollect1[, x.variables],
B = intCollect1$value,
G = diag(num.var),
H = rep(0, num.var))
I found the nnls (non-negative least square) package to be worth looking at.
You can also reformulate your linear regression model in the following way:
label ~ sum(exp(\alpha_i) f_i)
the optimization target will be
sum_j (label_j - sum_i(exp(\alpha_i) f_i))^2
This has no closed form solution but can be solved efficiently since it's convex in the \alpha_i's.
Once you compute the \alpha_i's, you can recast them as the regressors of a usual linear model by exponentiating them.