My independet variables are binominal (gender and posture)
but my dependent varibale is interval scaled (it's seven steps Likert scale)
What is the right regression model for this? And how do I apply it in R?
Thanky you for any advice
`For the constellation 2 binominal IVs and one metric DV, it is okay to use a simple Linear model like lm in R.
Related
I was wondering if someone would know an R package that would allow me to fit an Ordinal Logistic regression with a LASSO regularization or, alternatively, a Beta regression still with the LASSO? And if you also know of a nice tutorial to help me code that in R (with appropriate cross-validation), that would be even better!
Some context: My response variable is a satisfaction score between 0 and 10 (actually, values lie between 2 and 10) so I can model it with a Beta regression or I can convert its values into ranked categories. My interest is to identify important variables explaining this score but as I have too many potential explanatory variables (p = 12) compared to my sample size (n = 105), I need to use a penalized regression method for model selection, hence my interest in the LASSO.
The ordinalNet package does this. There's a paper with example here:
https://www.jstatsoft.org/article/download/v099i06/1440
Also the glmnetcr package: https://cran.r-project.org/web/packages/glmnetcr/vignettes/glmnetcr.pdf
I want to perform a linear regression with 2 continuous predictors (Y~X1*X2) and an interaction between them.
Moreover, i want to use weighted least square with « weights » argument. However the weight for X1 and X2 are different car i want to use their variance (var(X1) and var(X2)). I did that for each univariate regression.
I do not know how to perform that in a model including these 2 variables and their interaction term.
Coud you help me please ?
I'm fitting a multiple linear regression model with 6 predictiors (3 continuous and 3 categorical). The residuals vs. fitted plot show that there is heteroscedasticity, also it's confirmed by bptest().
summary of sales_lm
rediduals vs. fitted plot
Also I calculated the sqrt for my train data and test data, as showed below:
sqrt(mean(sales_train_lm_pred-sales_train$SALES)^2)
2 3533.665
sqrt(mean(sales_test_lm_pred-sales_test$SALES)^2)
2 3556.036
I tried to fit glm() model, but still didn't rectify heteroscedasticity.
glm.test3<-glm(SALES~.,weights=1/sales_fitted$.resid^2,family=gaussian(link="identity"), data=sales_train)
resid vs. fitted plot for glm.test3
it looks weird.
glm.test3 plot
Could you please help me what should I do next?
Thanks in advance!
That you observe heteroscedasticity for your data means that the variance is not stationary. You can try the following:
1) Apply the one-parameter Box-Cox transformation (of the which the log transform is a special case) with a suitable lambda to one or more variables in the data set. The optimal lambda can be determined by looking at its log-likelihood function. Take a look at MASS::boxcox.
2) Play with your feature set (decrease, increase, add new variables).
2) Use the weighted linear regression method.
I have a training dataset that has 3233 rows and 62 columns. The independent variable is Happy (train$Happy), which is a binary variable. The other 61 columns are categorical independent variables.
I've created a logistic regression model as follows:
logModel <- glm(Happy ~ ., data = train, family = binary)
However, I want to reduce the number of independent variables that go into the model, perhaps down to 20 or so. I would like to start by getting rid of colinear categorical variables.
Can someone shed some light on how to determine which categorical variables are colinear and what threshold that I should use when removing a variable from a model?
Thank you!
if your variables were categorical then the obvious solution would be penalized logistic regression (Lasso) in R it is implemented in glmnet.
With categorical variables the problem is much more difficult.
I was in a similar situation and I used the importance plot from the package random forest in order to reduce the number of variables.
This would not help you to find collinearity but only to rank the variables by importance.
You have only 60 variable and maybe you have a knowledge of the field so you can try to add to you model some variables that makes sense to you (like z=x1-x3 if you think that the value x1-x3 is important.) and then rank them according to a random forest model
You could use Cramer's V, or the related Phi or contingency coefficient (see a great paper at http://www.harding.edu/sbreezeel/460%20files/statbook/chapter15.pdf), to measure colinearity among categorical variables. If two or more categorical variables have a Cramer's V value close to 1, it means they're highly "correlated" and you may not need to keep all of them in your logistic regression model.
I have fit my discrete count data using a variety of functions for comparison. I fit a GEE model using geepack, a linear mixed effect model on the log(count) using lme (nlme), a GLMM using glmer (lme4), and a GAMM using gamm4 (gamm4) in R.
I am interested in comparing these models and would like to plot the expected (predicted) values for a new set of data (predictor variables). My goal is to compare the predicted effects for each model under particular conditions (x variables). Of particular interest is the comparison between marginal (GEE) and conditional estimates.
I think my main problem might be getting the new data in the correct form with the correct labels and attributes and such. I am still very much an R novice and struggle with this stuff (no course on this at my university unfortunately).
I currently have fitted models
gee1 lme1 lmer1 gamm1
and can extract their fixed effect coefficients and standard errors without a problem. I also don't have a problem converting them from the log scale or estimating confidence intervals accounting for the random effects.
I also have my new dataframe newdat which has 365 observations of 23 variables (average environmental data for each day of the year).
I am stuck on how to predict new count estimates from this. I played around with the model.matrix function but couldn't get it to work. For example, I tried:
mm = model.matrix(terms(glmm1), newdat) # Error in model.frame.default(object,
# data, xlev = xlev) : object is not a matrix
newdat$pcount = mm %*% fixef(glmm1)
Any suggestions or good references would be greatly appreciated. Can anyone help with the error above?
Getting predictions for lme() and lmer() is documented on http://glmm.wikidot.com/faq