I am doing a linear regression in R. The output shows some variables (equity & Equity, and loan & Loan) double and one is written with a capital letter. In the dataset, they are always written in lowercase but appear in two different ways when I run the regression. I do not find the answer online, so maybe some of you can help me out? Any ideas are highly appreciated!
Model1 <- lm(Lifetime_CO2 ~ signatory + as.factor(Finance_Type), data = Data_dup)
summary(Model1)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 90.351 4.397 20.550 < 2e-16 ***
signatory 7.378 1.732 4.259 2.10e-05 ***
as.factor(Finance_Type)equity -29.059 4.640 -6.263 4.18e-10 ***
as.factor(Finance_Type)Equity 14.549 38.971 0.373 0.708914
as.factor(Finance_Type)government grant -81.284 22.784 -3.568 0.000365 ***
as.factor(Finance_Type)insurance -2.810 16.397 -0.171 0.863948
as.factor(Finance_Type)loan -25.183 4.422 -5.695 1.32e-08 ***
as.factor(Finance_Type)Loan 14.549 27.731 0.525 0.599852
as.factor(Finance_Type)refinancing bond -9.728 19.878 -0.489 0.624578
as.factor(Finance_Type)refinancing equity -40.601 27.731 -1.464 0.143252
as.factor(Finance_Type)refinancing loan -26.889 5.344 -5.031 5.09e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
You can convert upper-case characters in the Finance_Type column to lower-case, or vice versa.
By the way, as.factor() is not needed unless you want to re-order levels of a categorical variable.
Data_dup$Finance_Type <- tolower(Data_dup$Finance_Type)
Model1 <- lm(Lifetime_CO2 ~ signatory + Finance_Type, data = Data_dup)
summary(Model1)
Related
I'm using 'gamlss' from the package 'gamlss' (version 5.4-1) in R for a generalized additive model for location scale and shape.
My model looks like this
propvoc3 = gamlss(proporcion.voc ~ familiaridad * proporcion)
When I want to see the Anova table I get this output
Mu link function: identity
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.625e-01 9.476e-02 5.936 1.9e-06 ***
familiaridaddesconocido -1.094e-01 1.059e-01 -1.032 0.31042
proporcionmayor 4.375e-01 1.340e-01 3.265 0.00281 **
proporcionmenor 1.822e-17 1.340e-01 0.000 1.00000
familiaridaddesconocido:proporcionmayor -3.281e-01 1.708e-01 -1.921 0.06464 .
familiaridaddesconocido:proporcionmenor 5.469e-01 1.708e-01 3.201 0.00331 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
I just want to know if there is a way to get the values just by variable and not by every term?
I'm using the glca package to run a latent class analysis. I want to see how covariates (other than indicators used to construct latent classes) affect the probability of class assignment. I understand this is a multinomial logistic regression, and thus, my question is, is there a way I can change the base reference latent class? For example, my model is currently a 4-class model, and the output shows the effect of covariates on class prevalence with respect to Class-4 (base category) as default. I want to change this base category to, for example, Class-2.
My code is as follows
fc <- item(intrst, respect, expert, inclu, contbt,secure,pay,bonus, benft, innov, learn, rspons, promote, wlb, flex) ~ atenure+super+sal+minority+female+age40+edu+d_bpw+d_skill
lca4_cov <- glca(fc, data = bpw, nclass = 4, seed = 1)
and I get the following output.
> coef(lca4_cov)
Class 1 / 4 :
Odds Ratio Coefficient Std. Error t value Pr(>|t|)
(Intercept) 1.507537 0.410477 0.356744 1.151 0.24991
atenure 0.790824 -0.234679 0.102322 -2.294 0.02183 *
super 1.191961 0.175600 0.028377 6.188 6.29e-10 ***
sal 0.937025 -0.065045 0.035490 -1.833 0.06686 .
minority 2.002172 0.694233 0.060412 11.492 < 2e-16 ***
female 1.210653 0.191160 0.059345 3.221 0.00128 **
age40 1.443603 0.367142 0.081002 4.533 5.89e-06 ***
edu 1.069771 0.067444 0.042374 1.592 0.11149
d_bpw 0.981104 -0.019077 0.004169 -4.576 4.78e-06 ***
d_skill 1.172218 0.158898 0.036155 4.395 1.12e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Class 2 / 4 :
Odds Ratio Coefficient Std. Error t value Pr(>|t|)
(Intercept) 3.25282 1.17952 0.43949 2.684 0.00729 **
atenure 0.95131 -0.04992 0.12921 -0.386 0.69926
super 1.16835 0.15559 0.03381 4.602 4.22e-06 ***
sal 1.01261 0.01253 0.04373 0.287 0.77450
minority 0.72989 -0.31487 0.08012 -3.930 8.55e-05 ***
female 0.45397 -0.78971 0.07759 -10.178 < 2e-16 ***
age40 1.26221 0.23287 0.09979 2.333 0.01964 *
edu 1.29594 0.25924 0.05400 4.801 1.60e-06 ***
d_bpw 0.97317 -0.02720 0.00507 -5.365 8.26e-08 ***
d_skill 1.16223 0.15034 0.04514 3.330 0.00087 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Class 3 / 4 :
Odds Ratio Coefficient Std. Error t value Pr(>|t|)
(Intercept) 0.218153 -1.522557 0.442060 -3.444 0.000575 ***
atenure 0.625815 -0.468701 0.123004 -3.810 0.000139 ***
super 1.494112 0.401532 0.031909 12.584 < 2e-16 ***
sal 1.360924 0.308164 0.044526 6.921 4.72e-12 ***
minority 0.562590 -0.575205 0.081738 -7.037 2.07e-12 ***
female 0.860490 -0.150253 0.072121 -2.083 0.037242 *
age40 1.307940 0.268453 0.100376 2.674 0.007495 **
edu 1.804949 0.590532 0.054522 10.831 < 2e-16 ***
d_bpw 0.987353 -0.012727 0.004985 -2.553 0.010685 *
d_skill 1.073519 0.070942 0.045275 1.567 0.117163
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I would appreciate it if anyone let me know codes/references to address my problem. Thanks in advance.
Try using the decreasing option.
lca4_cov <- glca(fc, data = bpw, nclass = 4, seed = 1, decreasing = T)
I am trying to figure out how to calculate the marginal effects of my model using the, "clogit," function in the survival package. The margins package does not seem to work with this type of model, but does work with "multinom" and "mclogit." However, I am investigating the affects of choice characteristics, and not individual characteristics, so it needs to be a conditional logit model. The mclogit function works with the margins package, but these results are widely different from the results using the clogit function, why is that? Any help calculating the marginal effects from the clogit function would be greatly appreciated.
mclogit output:
Call:
mclogit(formula = cbind(selected, caseID) ~ SysTEM + OWN + cost +
ENVIRON + NEIGH + save, data = atl)
Estimate Std. Error z value Pr(>|z|)
SysTEM 0.139965 0.025758 5.434 5.51e-08 ***
OWN 0.008931 0.026375 0.339 0.735
cost -0.103012 0.004215 -24.439 < 2e-16 ***
ENVIRON 0.675341 0.037104 18.201 < 2e-16 ***
NEIGH 0.419054 0.031958 13.112 < 2e-16 ***
save 0.532825 0.023399 22.771 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Null Deviance: 18380
Residual Deviance: 16670
Number of Fisher Scoring iterations: 4
Number of observations: 8364
clogit output:
Call:
coxph(formula = Surv(rep(1, 25092L), selected) ~ SysTEM + OWN +
cost + ENVIRON + NEIGH + save + strata(caseID), data = atl,
method = "exact")
n= 25092, number of events= 8364
coef exp(coef) se(coef) z Pr(>|z|)
SysTEM 0.133184 1.142461 0.034165 3.898 9.69e-05 ***
OWN -0.015884 0.984241 0.036346 -0.437 0.662
cost -0.179833 0.835410 0.005543 -32.442 < 2e-16 ***
ENVIRON 1.186329 3.275036 0.049558 23.938 < 2e-16 ***
NEIGH 0.658657 1.932195 0.042063 15.659 < 2e-16 ***
save 0.970051 2.638079 0.031352 30.941 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
SysTEM 1.1425 0.8753 1.0685 1.2216
OWN 0.9842 1.0160 0.9166 1.0569
cost 0.8354 1.1970 0.8264 0.8445
ENVIRON 3.2750 0.3053 2.9719 3.6091
NEIGH 1.9322 0.5175 1.7793 2.0982
save 2.6381 0.3791 2.4809 2.8053
Concordance= 0.701 (se = 0.004 )
Rsquare= 0.103 (max possible= 0.688 )
Likelihood ratio test= 2740 on 6 df, p=<2e-16
Wald test = 2465 on 6 df, p=<2e-16
Score (logrank) test = 2784 on 6 df, p=<2e-16
margins output for mclogit
margins(model2A)
SysTEM OWN cost ENVIRON NEIGH save
0.001944 0.000124 -0.001431 0.00938 0.00582 0.0074
margins output for clogit
margins(model2A)
Error in match.arg(type) :
'arg' should be one of “risk”, “expected”, “lp”
I made a zero-inflated negative binomial model with glmTMB as below
M2<- glmmTMB(psychological100~ (1|ID) + time*MNM01, data=mnmlong,
ziformula=~ (1|ID) + time*MNM01, family=nbinom2())
summary(M2)
Here is the output
Family: nbinom2 ( log )
Formula: psychological100 ~ (1 | ID) + time * MNM01
Zero inflation: ~(1 | ID) + time * MNM01
Data: mnmlong
AIC BIC logLik deviance df.resid
3507.0 3557.5 -1742.5 3485.0 714
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
ID (Intercept) 0.2862 0.535
Number of obs: 725, groups: ID, 337
Zero-inflation model:
Groups Name Variance Std.Dev.
ID (Intercept) 0.5403 0.7351
Number of obs: 725, groups: ID, 337
Overdispersion parameter for nbinom2 family (): 3.14
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.89772 0.09213 31.451 < 2e-16 ***
time -0.08724 0.01796 -4.858 1.18e-06 ***
MNM01 0.02094 0.12433 0.168 0.866
time:MNM01 -0.01193 0.02420 -0.493 0.622
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.29940 0.17298 -1.731 0.083478 .
time 0.12204 0.03338 3.656 0.000256 ***
MNM01 0.06771 0.24217 0.280 0.779790
time:MNM01 -0.02821 0.04462 -0.632 0.527282
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I wanted to know the R square of the model and tried the following 2 methods but not successful
MuMIn::r.squaredGLMM(M2)
Error in r.squaredGLMM.glmmTMB(M2) : r.squaredGLMM cannot (yet)
handle 'glmmTMB' object with zero-inflation
performance::r2_zeroinflated(M2)
Error in residuals.glmmTMB(model, type = "pearson") : pearson
residuals are not implemented for models with zero-inflation or
variable dispersion
what do you advise me?
Try with the pseudo-R^2 based on a likelihood-ratio (MuMIn::r.squaredLR). You may need to provide a null model for comparison explicitly.
I'm creating a GLM with quasipoisson distribution and when I do an analysis of deviance one of my variables is not significant, but its interaction with another is. It's my understanding that you include interactions when you expect a relationship between the two, so as one goes up the other will also go up.
Worked.out.vol.hours is Total Time.
AAB...BW is the organisers.
Sorry about the terrible variable names.
Call:
glm(formula = total.debris ~ Beach.Region + Volunteers..n. *
worked.out.vol.hour + Survey.Window + AAB...BW, family = quasipoisson,
data = ltype.all)
Deviance Residuals:
Min 1Q Median 3Q Max
-128.45 -22.71 -10.72 7.98 242.77
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.298e+00 4.650e-01 13.544 < 2e-16 ***
Beach.RegionNorth East 5.523e-01 1.142e-01 4.838 1.36e-06 ***
Beach.RegionNorth West 7.873e-01 1.233e-01 6.385 1.92e-10 ***
Beach.RegionNorthern Ireland 6.919e-01 1.554e-01 4.452 8.77e-06 ***
Beach.RegionScotland 6.168e-01 1.023e-01 6.030 1.80e-09 ***
Beach.RegionSouth East 7.663e-01 9.997e-02 7.665 2.27e-14 ***
Beach.RegionSouth West 8.261e-01 1.008e-01 8.196 3.38e-16 ***
Beach.RegionWales 6.714e-01 1.104e-01 6.079 1.33e-09 ***
Volunteers..n. 1.710e-02 1.235e-03 13.852 < 2e-16 ***
worked.out.vol.hour 3.579e-03 6.620e-04 5.407 6.83e-08 ***
Survey.Window2000 3.944e-01 1.893e-01 2.083 0.0373 *
Survey.Window2001 1.199e-01 1.851e-01 0.647 0.5174
Survey.Window2002 1.804e-01 1.773e-01 1.017 0.3090
Survey.Window2003 2.789e-01 1.747e-01 1.596 0.1106
Survey.Window2004 1.441e-01 1.738e-01 0.829 0.4069
Survey.Window2005 1.008e-01 1.722e-01 0.586 0.5581
Survey.Window2006 8.810e-02 1.718e-01 0.513 0.6081
Survey.Window2007 7.097e-02 1.726e-01 0.411 0.6809
AAB...BWAAB Combined -7.903e-01 6.679e-01 -1.183 0.2368
AAB...BWAdopt a Beach -6.070e-01 4.234e-01 -1.434 0.1517
AAB...BWBeachwatch Only -4.539e-01 4.227e-01 -1.074 0.2829
AAB...BWBW Combined -6.548e-01 4.863e-01 -1.347 0.1782
Volunteers..n.:worked.out.vol.hour -2.232e-05 1.586e-06 -14.071 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasipoisson family taken to be 1238.943)
Null deviance: 3637808 on 3737 degrees of freedom
Residual deviance: 2952919 on 3715 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 5
When I run the code to see which variables are significantanova(actmod1, test="Chisq")
Analysis of Deviance Table
Model: quasipoisson, link: log
Response: total.debris
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 3737 3637808
Beach.Region 7 141546 3730 3496262 < 2.2e-16 ***
Volunteers..n. 1 255212 3729 3241050 < 2.2e-16 ***
worked.out.vol.hour 1 1227 3728 3239823 0.3196126
Survey.Window 8 17788 3720 3222035 0.0729141 .
AAB...BW 4 27536 3716 3194499 0.0001807 ***
Volunteers..n.:worked.out.vol.hour 1 241579 3715 2952919 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
worked.out.vol.hours is not significant in the analysis of deviance, but its interaction with Volunteers..n. is, which is expected since the total hours surveyed will naturally increase with more volunteers. I, however want to keep these values separate in the model. How do I go about this issue? Do i just drop the variable altogether? Or do I keep it in because the interaction is significant?
Also, any help with how to succintly report these values would be greatly appreciated since I am quite new to this.